import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
     import os

     # The slow tests are often failing with OOM error on GPU
     # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
     # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
     A     :  List[Any]					     =       '''platform'''

     import jax
     import jax.numpy as jnp

     from transformers import BlenderbotTokenizer
     from transformers.models.blenderbot.modeling_flax_blenderbot import (
         FlaxBlenderbotForConditionalGeneration,
         FlaxBlenderbotModel,
         shift_tokens_right,
     )


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      __UpperCamelCase=None     ,      ):




      if attention_mask is None:
            SCREAMING_SNAKE_CASE_          =  np.where(input_ids != config.pad_token_id     ,      1     ,      0				)
      if decoder_attention_mask is None:
            SCREAMING_SNAKE_CASE_          =  np.where(decoder_input_ids != config.pad_token_id     ,      1     ,      0				)
      if head_mask is None:
            SCREAMING_SNAKE_CASE_          =  np.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            SCREAMING_SNAKE_CASE_          =  np.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            SCREAMING_SNAKE_CASE_          =  np.ones((config.decoder_layers, config.decoder_attention_heads)				)
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": attention_mask,
      }


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       Union[str, Any]  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Tuple=13  ,     __magic_name__ :       List[Any]=7  ,     __magic_name__ :       int=True  ,     __magic_name__ :       int=False  ,     __magic_name__ :       Union[str, Any]=99  ,     __magic_name__ :       List[Any]=16  ,     __magic_name__ :       Any=2  ,     __magic_name__ :       Optional[int]=4  ,     __magic_name__ :       str=4  ,     __magic_name__ :       Optional[int]="gelu"  ,     __magic_name__ :       Optional[Any]=0.1  ,     __magic_name__ :       int=0.1  ,     __magic_name__ :       Optional[Any]=32  ,     __magic_name__ :       List[Any]=2  ,     __magic_name__ :       Union[str, Any]=1  ,     __magic_name__ :       List[Any]=0  ,     __magic_name__ :       str=0.02  ,     )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  seq_length
        SCREAMING_SNAKE_CASE_          =  is_training
        SCREAMING_SNAKE_CASE_          =  use_labels
        SCREAMING_SNAKE_CASE_          =  vocab_size
        SCREAMING_SNAKE_CASE_          =  hidden_size
        SCREAMING_SNAKE_CASE_          =  num_hidden_layers
        SCREAMING_SNAKE_CASE_          =  num_attention_heads
        SCREAMING_SNAKE_CASE_          =  intermediate_size
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  hidden_dropout_prob
        SCREAMING_SNAKE_CASE_          =  attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_          =  max_position_embeddings
        SCREAMING_SNAKE_CASE_          =  eos_token_id
        SCREAMING_SNAKE_CASE_          =  pad_token_id
        SCREAMING_SNAKE_CASE_          =  bos_token_id
        SCREAMING_SNAKE_CASE_          =  initializer_range







  def    __A     (							self :       Any   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,     self.vocab_size   )  ,     3  ,     self.vocab_size   )
        SCREAMING_SNAKE_CASE_          =  np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,     dtype=np.intaa   ))  ,     -1   )

        SCREAMING_SNAKE_CASE_          =  shift_tokens_right(__magic_name__  ,     1  ,     2   )

        SCREAMING_SNAKE_CASE_          =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,     d_model=self.hidden_size  ,     encoder_layers=self.num_hidden_layers  ,     decoder_layers=self.num_hidden_layers  ,     encoder_attention_heads=self.num_attention_heads  ,     decoder_attention_heads=self.num_attention_heads  ,     encoder_ffn_dim=self.intermediate_size  ,     decoder_ffn_dim=self.intermediate_size  ,     dropout=self.hidden_dropout_prob  ,     attention_dropout=self.attention_probs_dropout_prob  ,     max_position_embeddings=self.max_position_embeddings  ,     eos_token_id=self.eos_token_id  ,     bos_token_id=self.bos_token_id  ,     pad_token_id=self.pad_token_id  ,     initializer_range=self.initializer_range  ,     use_cache=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  prepare_blenderbot_inputs_dict(__magic_name__  ,     __magic_name__  ,     __magic_name__   )
        return config, inputs_dict







  def    __A     (							self :       Any   )  ->	Dict:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        return config, inputs_dict







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  20
        SCREAMING_SNAKE_CASE_          =  model_class_name(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  model.encode(inputs_dict["input_ids"]   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        SCREAMING_SNAKE_CASE_          =  model.init_cache(decoder_input_ids.shape[0]  ,     __magic_name__  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,     dtype="i4"   )

        SCREAMING_SNAKE_CASE_          =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]  ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,     )
        SCREAMING_SNAKE_CASE_          =  model.decode(
            decoder_input_ids[:, :-1]  ,     __magic_name__  ,     decoder_attention_mask=__magic_name__  ,     past_key_values=__magic_name__  ,     decoder_position_ids=__magic_name__  ,     )

        SCREAMING_SNAKE_CASE_          =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype="i4"   )
        SCREAMING_SNAKE_CASE_          =  model.decode(
            decoder_input_ids[:, -1:]  ,     __magic_name__  ,     decoder_attention_mask=__magic_name__  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_position_ids=__magic_name__  ,     )

        SCREAMING_SNAKE_CASE_          =  model.decode(__magic_name__  ,     __magic_name__   )

        SCREAMING_SNAKE_CASE_          =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
        self.parent.assertTrue(diff < 1e-3  ,     msg=F'''Max diff is {diff}'''   )







  def    __A     (							self :       Tuple  ,     __magic_name__ :       Optional[Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       str   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  20
        SCREAMING_SNAKE_CASE_          =  model_class_name(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  model.encode(inputs_dict["input_ids"]   )

        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  (
            inputs_dict["decoder_input_ids"],
            inputs_dict["decoder_attention_mask"],
        )

        SCREAMING_SNAKE_CASE_          =  jnp.concatenate(
            [
                decoder_attention_mask,
                jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
            ]  ,     axis=-1  ,     )

        SCREAMING_SNAKE_CASE_          =  model.init_cache(decoder_input_ids.shape[0]  ,     __magic_name__  ,     __magic_name__   )
        SCREAMING_SNAKE_CASE_          =  jnp.broadcast_to(
            jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]  ,     (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,     )

        SCREAMING_SNAKE_CASE_          =  model.decode(
            decoder_input_ids[:, :-1]  ,     __magic_name__  ,     decoder_attention_mask=__magic_name__  ,     past_key_values=__magic_name__  ,     decoder_position_ids=__magic_name__  ,     )
        SCREAMING_SNAKE_CASE_          =  jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,     dtype="i4"   )
        SCREAMING_SNAKE_CASE_          =  model.decode(
            decoder_input_ids[:, -1:]  ,     __magic_name__  ,     past_key_values=outputs_cache.past_key_values  ,     decoder_attention_mask=__magic_name__  ,     decoder_position_ids=__magic_name__  ,     )

        SCREAMING_SNAKE_CASE_          =  model.decode(__magic_name__  ,     __magic_name__  ,     decoder_attention_mask=__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
        self.parent.assertTrue(diff < 1e-3  ,     msg=F'''Max diff is {diff}'''   )



@require_flax
class   lowerCamelCase					(unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						9_9







  def    __A     (							self :       Optional[Any]   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  np.array(
            [
                [71, 82, 18, 33, 46, 91, 2],
                [68, 34, 26, 58, 30, 82, 2],
                [5, 97, 17, 39, 94, 40, 2],
                [76, 83, 94, 25, 70, 78, 2],
                [87, 59, 41, 35, 48, 66, 2],
                [55, 13, 16, 58, 5, 2, 1],  # note padding
                [64, 27, 31, 51, 12, 75, 2],
                [52, 64, 86, 17, 83, 39, 2],
                [48, 61, 9, 24, 71, 82, 2],
                [26, 1, 60, 48, 22, 13, 2],
                [21, 5, 62, 28, 14, 76, 2],
                [45, 98, 37, 86, 59, 48, 2],
                [70, 70, 50, 9, 28, 0, 2],
            ]  ,     dtype=np.intaa  ,     )

        SCREAMING_SNAKE_CASE_          =  input_ids.shape[0]
        SCREAMING_SNAKE_CASE_          =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,     d_model=24  ,     encoder_layers=2  ,     decoder_layers=2  ,     encoder_attention_heads=2  ,     decoder_attention_heads=2  ,     encoder_ffn_dim=32  ,     decoder_ffn_dim=32  ,     max_position_embeddings=48  ,     eos_token_id=2  ,     pad_token_id=1  ,     bos_token_id=0  ,     )
        return config, input_ids, batch_size







  def    __A     (							self :       Union[str, Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self._get_config_and_data()
        SCREAMING_SNAKE_CASE_          =  FlaxBlenderbotForConditionalGeneration(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  lm_model(input_ids=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  (batch_size, input_ids.shape[1], config.vocab_size)
        self.assertEqual(outputs["logits"].shape  ,     __magic_name__   )







  def    __A     (							self :       Dict   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  BlenderbotConfig(
            vocab_size=self.vocab_size  ,     d_model=14  ,     encoder_layers=2  ,     decoder_layers=2  ,     encoder_attention_heads=2  ,     decoder_attention_heads=2  ,     encoder_ffn_dim=8  ,     decoder_ffn_dim=8  ,     max_position_embeddings=48  ,     )
        SCREAMING_SNAKE_CASE_          =  FlaxBlenderbotForConditionalGeneration(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,     dtype=np.intaa   )
        SCREAMING_SNAKE_CASE_          =  np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,     dtype=np.intaa   )
        SCREAMING_SNAKE_CASE_          =  lm_model(input_ids=__magic_name__  ,     decoder_input_ids=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  (*summary.shape, config.vocab_size)
        self.assertEqual(outputs["logits"].shape  ,     __magic_name__   )







  def    __A     (							self :       Tuple   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,     dtype=np.intaa   )
        SCREAMING_SNAKE_CASE_          =  shift_tokens_right(__magic_name__  ,     1  ,     2   )
        SCREAMING_SNAKE_CASE_          =  np.equal(__magic_name__  ,     1   ).astype(np.floataa   ).sum()
        SCREAMING_SNAKE_CASE_          =  np.equal(__magic_name__  ,     1   ).astype(np.floataa   ).sum()
        self.assertEqual(shifted.shape  ,     input_ids.shape   )
        self.assertEqual(__magic_name__  ,     n_pad_before - 1   )
        self.assertTrue(np.equal(shifted[:, 0]  ,     2   ).all()   )



@require_flax
class   lowerCamelCase					(UpperCamelCase__  ,      unittest.TestCase  ,      UpperCamelCase__					):
  """simple docstring"""







  lowerCamelCase__     =						True
  lowerCamelCase__     =						(
      (
          FlaxBlenderbotModel,
          FlaxBlenderbotForConditionalGeneration,
      )
      if is_flax_available()
      else ()
  )
  lowerCamelCase__     =						(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()







  def    __A     (							self :       str   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  FlaxBlenderbotModelTester(self   )







  def    __A     (							self :       Any   )  ->	str:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Optional[int]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        for model_class in self.all_model_classes:
              self.model_tester.check_use_cache_forward_with_attn_mask(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       List[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__   ):
                    SCREAMING_SNAKE_CASE_          =  self._prepare_for_class(__magic_name__  ,     __magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )

                    @jax.jit
                    def encode_jitted(__magic_name__ :       List[Any]  ,     __magic_name__ :       List[Any]=None  ,     **__magic_name__ :       int   ):
                          return model.encode(input_ids=__magic_name__  ,     attention_mask=__magic_name__   )

                    with self.subTest("JIT Enabled"   ):
                          SCREAMING_SNAKE_CASE_          =  encode_jitted(**__magic_name__   ).to_tuple()

                    with self.subTest("JIT Disabled"   ):
                          with jax.disable_jit():
                                SCREAMING_SNAKE_CASE_          =  encode_jitted(**__magic_name__   ).to_tuple()

                    self.assertEqual(len(__magic_name__   )  ,     len(__magic_name__   )   )
                    for jitted_output, output in zip(__magic_name__  ,     __magic_name__   ):
                          self.assertEqual(jitted_output.shape  ,     output.shape   )







  def    __A     (							self :       List[Any]   )  ->	Any:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
              with self.subTest(model_class.__name__   ):
                    SCREAMING_SNAKE_CASE_          =  model_class(__magic_name__   )
                    SCREAMING_SNAKE_CASE_          =  model.encode(inputs_dict["input_ids"]  ,     inputs_dict["attention_mask"]   )

                    SCREAMING_SNAKE_CASE_          =  {
                        "decoder_input_ids": inputs_dict["decoder_input_ids"],
                        "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                        "encoder_outputs": encoder_outputs,
                    }

                    @jax.jit
                    def decode_jitted(__magic_name__ :       Any  ,     __magic_name__ :       str  ,     __magic_name__ :       List[str]   ):
                          return model.decode(
                              decoder_input_ids=__magic_name__  ,     decoder_attention_mask=__magic_name__  ,     encoder_outputs=__magic_name__  ,     )

                    with self.subTest("JIT Enabled"   ):
                          SCREAMING_SNAKE_CASE_          =  decode_jitted(**__magic_name__   ).to_tuple()

                    with self.subTest("JIT Disabled"   ):
                          with jax.disable_jit():
                                SCREAMING_SNAKE_CASE_          =  decode_jitted(**__magic_name__   ).to_tuple()

                    self.assertEqual(len(__magic_name__   )  ,     len(__magic_name__   )   )
                    for jitted_output, output in zip(__magic_name__  ,     __magic_name__   ):
                          self.assertEqual(jitted_output.shape  ,     output.shape   )







  @slow
  def    __A     (							self :       str   )  ->	Dict:
        for model_class_name in self.all_model_classes:
              SCREAMING_SNAKE_CASE_          =  model_class_name.from_pretrained("facebook/blenderbot-400M-distill"   )
              # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
              SCREAMING_SNAKE_CASE_          =  np.ones((1, 1)   ) * model.config.eos_token_id
              SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )
              self.assertIsNotNone(__magic_name__   )







  @unittest.skipUnless(jax_device != "cpu"  ,     "3B test too slow on CPU."   )
  @slow
  def    __A     (							self :       Optional[int]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  {"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
        SCREAMING_SNAKE_CASE_          =  {"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

        SCREAMING_SNAKE_CASE_          =  FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"  ,     from_pt=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )

        SCREAMING_SNAKE_CASE_          =  ["Sam"]
        SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     return_tensors="jax"   )

        SCREAMING_SNAKE_CASE_          =  model.generate(**__magic_name__  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  "Sam is a great name. It means \"sun\" in Gaelic."

        SCREAMING_SNAKE_CASE_          =  tokenizer.batch_decode(__magic_name__  ,     **__magic_name__   )
        assert generated_txt[0].strip() == tgt_text



import copy

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


A     :  Tuple					     =       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(SCREAMING_SNAKE_CASE__					)
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''rag'''
  lowerCamelCase__     =						True







  def __init__(							self :       Any  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Any=None  ,     __magic_name__ :       Optional[Any]=None  ,     __magic_name__ :       Any=" / "  ,     __magic_name__ :       int=" // "  ,     __magic_name__ :       Any=5  ,     __magic_name__ :       Dict=300  ,     __magic_name__ :       Optional[Any]=768  ,     __magic_name__ :       str=8  ,     __magic_name__ :       List[Any]="wiki_dpr"  ,     __magic_name__ :       Any="train"  ,     __magic_name__ :       Any="compressed"  ,     __magic_name__ :       List[Any]=None  ,     __magic_name__ :       Optional[int]=None  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       Union[str, Any]=False  ,     __magic_name__ :       List[str]=0.0  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       str=False  ,     __magic_name__ :       int=False  ,     __magic_name__ :       Tuple=False  ,     __magic_name__ :       Tuple=True  ,     __magic_name__ :       Dict=None  ,     **__magic_name__ :       int  ,     )  ->	List[str]:
        super().__init__(
            bos_token_id=__magic_name__  ,     pad_token_id=__magic_name__  ,     eos_token_id=__magic_name__  ,     decoder_start_token_id=__magic_name__  ,     forced_eos_token_id=__magic_name__  ,     is_encoder_decoder=__magic_name__  ,     prefix=__magic_name__  ,     vocab_size=__magic_name__  ,     **__magic_name__  ,     )
        assert (
            "question_encoder" in kwargs and "generator" in kwargs
        ), "Config has to be initialized with question_encoder and generator config"
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("question_encoder"   )
        SCREAMING_SNAKE_CASE_          =  question_encoder_config.pop("model_type"   )
        SCREAMING_SNAKE_CASE_          =  kwargs.pop("generator"   )
        SCREAMING_SNAKE_CASE_          =  decoder_config.pop("model_type"   )

        from ..auto.configuration_auto import AutoConfig

        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  AutoConfig.for_model(__magic_name__  ,     **__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  reduce_loss
        SCREAMING_SNAKE_CASE_          =  label_smoothing
        SCREAMING_SNAKE_CASE_          =  exclude_bos_score
        SCREAMING_SNAKE_CASE_          =  do_marginalize

        SCREAMING_SNAKE_CASE_          =  title_sep
        SCREAMING_SNAKE_CASE_          =  doc_sep
        SCREAMING_SNAKE_CASE_          =  n_docs
        SCREAMING_SNAKE_CASE_          =  max_combined_length

        SCREAMING_SNAKE_CASE_          =  dataset
        SCREAMING_SNAKE_CASE_          =  dataset_split
        SCREAMING_SNAKE_CASE_          =  index_name

        SCREAMING_SNAKE_CASE_          =  retrieval_vector_size
        SCREAMING_SNAKE_CASE_          =  retrieval_batch_size
        SCREAMING_SNAKE_CASE_          =  passages_path
        SCREAMING_SNAKE_CASE_          =  index_path
        SCREAMING_SNAKE_CASE_          =  use_dummy_dataset

        SCREAMING_SNAKE_CASE_          =  output_retrieved

        SCREAMING_SNAKE_CASE_          =  do_deduplication

        SCREAMING_SNAKE_CASE_          =  use_cache

        if self.forced_eos_token_id is None:
              SCREAMING_SNAKE_CASE_          =  getattr(self.generator  ,     "forced_eos_token_id"  ,     __magic_name__   )







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       PretrainedConfig  ,     __magic_name__ :       PretrainedConfig  ,     **__magic_name__ :       List[str]   )  ->	PretrainedConfig:
        return cls(question_encoder=question_encoder_config.to_dict()  ,     generator=generator_config.to_dict()  ,     **__magic_name__   )







  def    __A     (							self :       Any   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  copy.deepcopy(self.__dict__   )
        SCREAMING_SNAKE_CASE_          =  self.question_encoder.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.generator.to_dict()
        SCREAMING_SNAKE_CASE_          =  self.__class__.model_type
        return output


