

import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A			:					Union[str, Any]						=			logging.get_logger(__name__)

__A			:					int						=			{
    'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
    'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "xlnet"
   lowerCamelCase__				=      ["mems"]
   lowerCamelCase__				=      {
       "n_token": "vocab_size",  # Backward compatibility
       "hidden_size": "d_model",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }





   def __init__(       self					:			Any     , __lowerCamelCase					:			int=32000     , __lowerCamelCase					:			List[str]=1024     , __lowerCamelCase					:			int=24     , __lowerCamelCase					:			Optional[int]=16     , __lowerCamelCase					:			Any=4096     , __lowerCamelCase					:			Any="gelu"     , __lowerCamelCase					:			Union[str, Any]=True     , __lowerCamelCase					:			Optional[int]="bi"     , __lowerCamelCase					:			Any=0.02     , __lowerCamelCase					:			Union[str, Any]=1e-12     , __lowerCamelCase					:			str=0.1     , __lowerCamelCase					:			List[str]=512     , __lowerCamelCase					:			List[str]=None     , __lowerCamelCase					:			Dict=True     , __lowerCamelCase					:			Any=False     , __lowerCamelCase					:			List[Any]=False     , __lowerCamelCase					:			Tuple=-1     , __lowerCamelCase					:			int=False     , __lowerCamelCase					:			str="last"     , __lowerCamelCase					:			Optional[Any]=True     , __lowerCamelCase					:			int="tanh"     , __lowerCamelCase					:			Any=0.1     , __lowerCamelCase					:			Tuple=5     , __lowerCamelCase					:			Dict=5     , __lowerCamelCase					:			Tuple=5     , __lowerCamelCase					:			Dict=1     , __lowerCamelCase					:			List[str]=2     , **__lowerCamelCase					:			str     , ):
       SCREAMING_SNAKE_CASE										=   vocab_size
       SCREAMING_SNAKE_CASE										=   d_model
       SCREAMING_SNAKE_CASE										=   n_layer
       SCREAMING_SNAKE_CASE										=   n_head
       if d_model % n_head != 0:
           raise ValueError(f"'d_model % n_head' ({d_model % n_head}) should be equal to 0"			)
       if "d_head" in kwargs:
           if kwargs["d_head"] != d_model // n_head:
               raise ValueError(
                   f"`d_head` ({kwargs['d_head']}) should be equal to `d_model // n_head` ({d_model // n_head})"			)
       SCREAMING_SNAKE_CASE										=   d_model // n_head
       SCREAMING_SNAKE_CASE										=   ff_activation
       SCREAMING_SNAKE_CASE										=   d_inner
       SCREAMING_SNAKE_CASE										=   untie_r
       SCREAMING_SNAKE_CASE										=   attn_type

       SCREAMING_SNAKE_CASE										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps

       SCREAMING_SNAKE_CASE										=   dropout
       SCREAMING_SNAKE_CASE										=   mem_len
       SCREAMING_SNAKE_CASE										=   reuse_len
       SCREAMING_SNAKE_CASE										=   bi_data
       SCREAMING_SNAKE_CASE										=   clamp_len
       SCREAMING_SNAKE_CASE										=   same_length

       SCREAMING_SNAKE_CASE										=   summary_type
       SCREAMING_SNAKE_CASE										=   summary_use_proj
       SCREAMING_SNAKE_CASE										=   summary_activation
       SCREAMING_SNAKE_CASE										=   summary_last_dropout
       SCREAMING_SNAKE_CASE										=   start_n_top
       SCREAMING_SNAKE_CASE										=   end_n_top

       SCREAMING_SNAKE_CASE										=   bos_token_id
       SCREAMING_SNAKE_CASE										=   pad_token_id
       SCREAMING_SNAKE_CASE										=   eos_token_id

       if "use_cache" in kwargs:
           warnings.warn(
               "The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
               " instead."     , __lowerCamelCase     , )
           SCREAMING_SNAKE_CASE										=   kwargs["use_cache"]

       SCREAMING_SNAKE_CASE										=   use_mems_eval
       SCREAMING_SNAKE_CASE										=   use_mems_train
       super().__init__(pad_token_id=__lowerCamelCase     , bos_token_id=__lowerCamelCase     , eos_token_id=__lowerCamelCase     , **__lowerCamelCase			)





   @property
   def      _snake_case		(       self					:			Dict			):
       logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit."			)
       return -1





   @max_position_embeddings.setter
   def      _snake_case		(       self					:			str     , __lowerCamelCase					:			Union[str, Any]			):
       # Message copied from Transformer-XL documentation
       raise NotImplementedError(
           f"The model {self.model_type} is one of the few models that has no sequence length limit."			)


import pytest


__A			:					Optional[Any]						=			'__dummy_dataset1__'

__A			:					Optional[int]						=			'\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"\nURLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                "tokens": datasets.Sequence(datasets.Value("string")),\n                "ner_tags": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            "O",\n                            "B-PER",\n                            "I-PER",\n                            "B-ORG",\n                            "I-ORG",\n                            "B-LOC",\n                            "I-LOC",\n                        ]\n                    )\n                ),\n                "langs": datasets.Sequence(datasets.Value("string")),\n                "spans": datasets.Sequence(datasets.Value("string")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, "r", encoding="utf-8") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n'





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 	__a     (      ):
    return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 	__a     (      A__			:  Optional[Any]			,  A__			:  List[str]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   dataset_loading_script_name
    SCREAMING_SNAKE_CASE										=   tmp_path / "datasets" / script_name
    script_dir.mkdir(parents=A__       )
    SCREAMING_SNAKE_CASE										=   script_dir / F"{script_name}.py"
    with open(A__			,  "w"       ) as f:
        f.write(A__       )
    return str(A__       )