import unittest

from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
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 GPTNeoXJapaneseForCausalLM, GPTNeoXJapaneseModel


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       List[str]  ,     __magic_name__ :       int  ,     __magic_name__ :       Tuple=13  ,     __magic_name__ :       Dict=7  ,     __magic_name__ :       Dict=True  ,     __magic_name__ :       List[Any]=True  ,     __magic_name__ :       Optional[Any]=True  ,     __magic_name__ :       str=True  ,     __magic_name__ :       List[str]=99  ,     __magic_name__ :       List[str]=32  ,     __magic_name__ :       Any=5  ,     __magic_name__ :       Optional[Any]=4  ,     __magic_name__ :       Union[str, Any]=4  ,     __magic_name__ :       Optional[int]="gelu"  ,     __magic_name__ :       str=0.0  ,     __magic_name__ :       Optional[Any]=0.1  ,     __magic_name__ :       List[str]=True  ,     __magic_name__ :       Optional[int]=512  ,     __magic_name__ :       Dict=16  ,     __magic_name__ :       str=2  ,     __magic_name__ :       Optional[int]=0.02  ,     __magic_name__ :       Tuple=3  ,     __magic_name__ :       Tuple=4  ,     __magic_name__ :       List[str]=None  ,     )  ->	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_input_mask
        SCREAMING_SNAKE_CASE_          =  use_token_type_ids
        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_multiple_size
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  hidden_dropout
        SCREAMING_SNAKE_CASE_          =  attention_dropout
        SCREAMING_SNAKE_CASE_          =  weight_tying
        SCREAMING_SNAKE_CASE_          =  max_position_embeddings
        SCREAMING_SNAKE_CASE_          =  type_vocab_size
        SCREAMING_SNAKE_CASE_          =  type_sequence_label_size
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  num_labels
        SCREAMING_SNAKE_CASE_          =  num_choices
        SCREAMING_SNAKE_CASE_          =  scope







  def    __A     (							self :       Any   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  ids_tensor([self.batch_size, self.seq_length]  ,     self.vocab_size   )

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

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

        SCREAMING_SNAKE_CASE_          =  self.get_config()

        return config, input_ids, input_mask, token_labels







  def    __A     (							self :       Any   )  ->	str:
        return GPTNeoXJapaneseConfig(
            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_multiple_size=self.intermediate_multiple_size  ,     hidden_act=self.hidden_act  ,     hidden_dropout=self.hidden_dropout  ,     attention_dropout=self.attention_dropout  ,     weight_tying=self.weight_tying  ,     max_position_embeddings=self.max_position_embeddings  ,     type_vocab_size=self.type_vocab_size  ,     is_decoder=__magic_name__  ,     initializer_range=self.initializer_range  ,     )







  def    __A     (							self :       str   )  ->	Any:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()

        SCREAMING_SNAKE_CASE_          =  True

        return config, input_ids, input_mask, token_labels







  def    __A     (							self :       str  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Any   )  ->	Any:
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseModel(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__   )
        self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)   )







  def    __A     (							self :       int  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       str  ,     __magic_name__ :       int   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseModel(__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__   )
        self.parent.assertEqual(result.last_hidden_state.shape  ,     (self.batch_size, self.seq_length, self.hidden_size)   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       str  ,     __magic_name__ :       Any  ,     __magic_name__ :       int   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseForCausalLM(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__  ,     labels=__magic_name__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)   )







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       Any  ,     __magic_name__ :       int  ,     __magic_name__ :       Any   )  ->	Dict:
        SCREAMING_SNAKE_CASE_          =  True
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseForCausalLM(config=__magic_name__   )
        model.to(__magic_name__   )
        model.eval()

        # first forward pass
        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__  ,     use_cache=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  outputs.past_key_values

        # create hypothetical multiple next token and extent to next_input_ids
        SCREAMING_SNAKE_CASE_          =  ids_tensor((self.batch_size, 3)  ,     config.vocab_size   )
        SCREAMING_SNAKE_CASE_          =  ids_tensor((self.batch_size, 3)  ,     vocab_size=2   )

        # append to next input_ids and
        SCREAMING_SNAKE_CASE_          =  torch.cat([input_ids, next_tokens]  ,     dim=-1   )
        SCREAMING_SNAKE_CASE_          =  torch.cat([input_mask, next_mask]  ,     dim=-1   )

        SCREAMING_SNAKE_CASE_          =  model(__magic_name__  ,     attention_mask=__magic_name__  ,     output_hidden_states=__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  output_from_no_past["hidden_states"][0]
        SCREAMING_SNAKE_CASE_          =  model(
            __magic_name__  ,     attention_mask=__magic_name__  ,     past_key_values=__magic_name__  ,     output_hidden_states=__magic_name__  ,     )["hidden_states"][0]

        # select random slice
        SCREAMING_SNAKE_CASE_          =  ids_tensor((1,)  ,     output_from_past.shape[-1]   ).item()
        SCREAMING_SNAKE_CASE_          =  output_from_no_past[:, -3:, random_slice_idx].detach()
        SCREAMING_SNAKE_CASE_          =  output_from_past[:, :, random_slice_idx].detach()

        self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]   )

        # test that outputs are equal for slice
        self.parent.assertTrue(torch.allclose(__magic_name__  ,     __magic_name__  ,     atol=1e-3   )   )







  def    __A     (							self :       Optional[Any]   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  config_and_inputs
        SCREAMING_SNAKE_CASE_          =  {"input_ids": input_ids, "attention_mask": input_mask}
        return config, inputs_dict



@require_torch
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						(GPTNeoXJapaneseModel, GPTNeoXJapaneseForCausalLM) if is_torch_available() else ()
  lowerCamelCase__     =						(GPTNeoXJapaneseForCausalLM,) if is_torch_available() else ()
  lowerCamelCase__     =						(
      {'''feature-extraction''': GPTNeoXJapaneseModel, '''text-generation''': GPTNeoXJapaneseForCausalLM}
      if is_torch_available()
      else {}
  )
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False
  lowerCamelCase__     =						False







  def    __A     (							self :       List[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=__magic_name__  ,     hidden_size=37   )







  def    __A     (							self :       Any   )  ->	int:
        self.config_tester.run_common_tests()







  def    __A     (							self :       Optional[Any]   )  ->	str:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Union[str, Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Tuple   )  ->	Optional[int]:
        # This regression test was failing with PyTorch < 1.3
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs_for_decoder()

        SCREAMING_SNAKE_CASE_          =  None

        self.model_tester.create_and_check_model_as_decoder(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       Tuple   )  ->	int:
        SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_decoder_model_past_large_inputs(__magic_name__  ,     __magic_name__  ,     __magic_name__   )







  def    __A     (							self :       str   )  ->	int:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_causal_lm(*__magic_name__   )







  @slow
  def    __A     (							self :       Dict   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  "abeja/gpt-neox-japanese-2.7b"

        SCREAMING_SNAKE_CASE_          =  ["データサイエンティストとは、", "100年後に必要とされる会社は、", "フルリモートの環境で働くために必要なことは、", "国境の長いトンネルを抜けると", "美味しい日本食といえば、"]

        SCREAMING_SNAKE_CASE_          =  [
            "データサイエンティストとは、データを分析し、ビジネスに役立つ知見を導き出す専門家のことです。",
            "100年後に必要とされる会社は、「人」が中心の会社です。",
            "フルリモートの環境で働くために必要なことは、「自分の時間をコントロールする」ことです。",
            "国境の長いトンネルを抜けると、そこは雪国だった。",
            "美味しい日本食といえば、やっぱりお寿司ですよね。",
        ]

        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseTokenizer.from_pretrained(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  GPTNeoXJapaneseForCausalLM.from_pretrained(__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  []
        for prompt in prompts:
              SCREAMING_SNAKE_CASE_          =  tokenizer(__magic_name__  ,     return_tensors="pt"   ).input_ids
              SCREAMING_SNAKE_CASE_          =  model.generate(__magic_name__  ,     max_length=50   )
              SCREAMING_SNAKE_CASE_          =  tokenizer.batch_decode(__magic_name__  ,     skip_special_tokens=__magic_name__   )
              predicted_outputs += generated_string
        self.assertListEqual(__magic_name__  ,     __magic_name__   )



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


