





import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    "files"					,					[
        ["full:README.md", "dataset_infos.json"],
        ["empty:README.md", "dataset_infos.json"],
        ["dataset_infos.json"],
        ["full:README.md"],
    ]					,					)
def   __snake_case    (       _lowerCAmelCase			:			Dict					,					_lowerCAmelCase			:			Optional[int]						)			->    int:
   A_       :					Optional[int]        =      tmp_path_factory.mktemp("dset_infos_dir"						)
   if "full:README.md" in files:
      with open(dataset_infos_dir / "README.md"					,					"w"						) as f:
         f.write("---\ndataset_info:\n  dataset_size: 42\n---"						)
   if "empty:README.md" in files:
      with open(dataset_infos_dir / "README.md"					,					"w"						) as f:
         f.write(""						)
    # we want to support dataset_infos.json for backward compatibility
   if "dataset_infos.json" in files:
      with open(dataset_infos_dir / "dataset_infos.json"					,					"w"						) as f:
         f.write("{\"default\": {\"dataset_size\": 42}}"						)
   A_       :					List[str]        =      DatasetInfosDict.from_directory(snake_case__						)
   assert dataset_infos
   assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    "dataset_info"					,					[
        DatasetInfo(),
        DatasetInfo(
            description="foo"					,					features=Features({"a": Value("int32"						)}						)					,					builder_name="builder"					,					config_name="config"					,					version="1.0.0"					,					splits=[{"name": "train"}]					,					download_size=42					,					),
    ]					,					)
def   __snake_case    (       _lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			DatasetInfo						)			->    int:
   A_       :					Union[str, Any]        =      str(snake_case__						)
   dataset_info.write_to_directory(snake_case__						)
   A_       :					Any        =      DatasetInfo.from_directory(snake_case__						)
   assert dataset_info == reloaded
   assert os.path.exists(os.path.join(snake_case__					,					"dataset_info.json"						)						)

def   __snake_case    (       )			->    List[Any]:
   A_       :					str        =      DatasetInfo(
       description="foo"					,					citation="bar"					,					homepage="https://foo.bar"					,					license="CC0"					,					features=Features({"a": Value("int32"						)}						)					,					post_processed={}					,					supervised_keys=()					,					task_templates=[]					,					builder_name="builder"					,					config_name="config"					,					version="1.0.0"					,					splits=[{"name": "train", "num_examples": 42}]					,					download_checksums={}					,					download_size=1337					,					post_processing_size=442					,					dataset_size=1234					,					size_in_bytes=1337 + 442 + 1234					,					)
   A_       :					Optional[int]        =      dataset_info._to_yaml_dict()
   assert sorted(snake_case__						) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML						)
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      assert key in dataset_info_yaml_dict
      assert isinstance(dataset_info_yaml_dict[key]					,					(list, dict, int, str)						)
   A_       :					Optional[Any]        =      yaml.safe_dump(snake_case__						)
   A_       :					Dict        =      yaml.safe_load(snake_case__						)
   assert dataset_info_yaml_dict == reloaded

def   __snake_case    (       )			->    Dict:
   A_       :					Optional[Any]        =      DatasetInfo()
   A_       :					Union[str, Any]        =      dataset_info._to_yaml_dict()
   assert dataset_info_yaml_dict == {}





@pytest.mark.parametrize(
    "dataset_infos_dict"					,					[
        DatasetInfosDict(),
        DatasetInfosDict({"default": DatasetInfo()}						),
        DatasetInfosDict({"my_config_name": DatasetInfo()}						),
        DatasetInfosDict(
            {
                "default": DatasetInfo(
                    description="foo"					,					features=Features({"a": Value("int32"						)}						)					,					builder_name="builder"					,					config_name="config"					,					version="1.0.0"					,					splits=[{"name": "train"}]					,					download_size=42					,					)
            }						),
        DatasetInfosDict(
            {
                "v1": DatasetInfo(dataset_size=42						),
                "v2": DatasetInfo(dataset_size=1337						),
            }						),
    ]					,					)
def   __snake_case    (       _lowerCAmelCase			:			Optional[Any]					,					_lowerCAmelCase			:			DatasetInfosDict						)			->    List[str]:
   A_       :					Any        =      str(snake_case__						)
   dataset_infos_dict.write_to_directory(snake_case__						)
   A_       :					Union[str, Any]        =      DatasetInfosDict.from_directory(snake_case__						)

   # the config_name of the dataset_infos_dict take over the attribute
   for config_name, dataset_info in dataset_infos_dict.items():
      A_       :					Optional[int]        =      config_name
      # the yaml representation doesn't include fields like description or citation
      # so we just test that we can recover what we can from the yaml
      A_       :					Dict        =      DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()						)
   assert dataset_infos_dict == reloaded

   if dataset_infos_dict:
      assert os.path.exists(os.path.join(snake_case__					,					"README.md"						)						)


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__a         =   logging.get_logger(__name__)

__a         =   {
    'xlm-mlm-en-2048': 'https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json',
    'xlm-mlm-ende-1024': 'https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json',
    'xlm-mlm-enfr-1024': 'https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json',
    'xlm-mlm-enro-1024': 'https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json',
    'xlm-mlm-tlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json',
    'xlm-mlm-xnli15-1024': 'https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json',
    'xlm-clm-enfr-1024': 'https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json',
    'xlm-clm-ende-1024': 'https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json',
    'xlm-mlm-17-1280': 'https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json',
    'xlm-mlm-100-1280': 'https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json',
}
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :List[Any]       	=      'xlm'
	a     :Dict       	=      {
	    'hidden_size': 'emb_dim',
	    'num_attention_heads': 'n_heads',
	    'num_hidden_layers': 'n_layers',
	    'n_words': 'vocab_size',  # For backward compatibility
	}
	def __init__(			self    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Dict=3_0_1_4_5			,				SCREAMING_SNAKE_CASE_    :     Dict=2_0_4_8			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=1_6			,				SCREAMING_SNAKE_CASE_    :     Dict=0.1			,				SCREAMING_SNAKE_CASE_    :     str=0.1			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     Any=False			,				SCREAMING_SNAKE_CASE_    :     str=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=False			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=1			,				SCREAMING_SNAKE_CASE_    :     Dict=True			,				SCREAMING_SNAKE_CASE_    :     Dict=5_1_2			,				SCREAMING_SNAKE_CASE_    :     Optional[int]=2_0_4_8**-0.5			,				SCREAMING_SNAKE_CASE_    :     Dict=1e-12			,				SCREAMING_SNAKE_CASE_    :     Any=0.02			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     List[Any]=1			,				SCREAMING_SNAKE_CASE_    :     List[Any]=2			,				SCREAMING_SNAKE_CASE_    :     int=3			,				SCREAMING_SNAKE_CASE_    :     Dict=5			,				SCREAMING_SNAKE_CASE_    :     int=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]="first"			,				SCREAMING_SNAKE_CASE_    :     Any=True			,				SCREAMING_SNAKE_CASE_    :     List[Any]=None			,				SCREAMING_SNAKE_CASE_    :     str=True			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]=0.1			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=5			,				SCREAMING_SNAKE_CASE_    :     List[str]=5			,				SCREAMING_SNAKE_CASE_    :     Any=0			,				SCREAMING_SNAKE_CASE_    :     str=0			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=2			,				SCREAMING_SNAKE_CASE_    :     Tuple=0			,				**SCREAMING_SNAKE_CASE_    :     Dict			,				)			->	List[Any]:
					lowercase_	    =   vocab_size
					lowercase_	    =   emb_dim
					lowercase_	    =   n_layers
					lowercase_	    =   n_heads
					lowercase_	    =   dropout
					lowercase_	    =   attention_dropout
					lowercase_	    =   gelu_activation
					lowercase_	    =   sinusoidal_embeddings
					lowercase_	    =   causal
					lowercase_	    =   asm
					lowercase_	    =   n_langs
					lowercase_	    =   use_lang_emb
					lowercase_	    =   layer_norm_eps
					lowercase_	    =   bos_index
					lowercase_	    =   eos_index
					lowercase_	    =   pad_index
					lowercase_	    =   unk_index
					lowercase_	    =   mask_index
					lowercase_	    =   is_encoder
					lowercase_	    =   max_position_embeddings
					lowercase_	    =   embed_init_std
					lowercase_	    =   init_std
					lowercase_	    =   summary_type
					lowercase_	    =   summary_use_proj
					lowercase_	    =   summary_activation
					lowercase_	    =   summary_proj_to_labels
					lowercase_	    =   summary_first_dropout
					lowercase_	    =   start_n_top
					lowercase_	    =   end_n_top
					lowercase_	    =   mask_token_id
					lowercase_	    =   lang_id

					if "n_words" in kwargs:
									lowercase_	    =   kwargs['''n_words''']

					super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_			,				bos_token_id=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			)

class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	@property
	def 		_lowercase    (			self    :     int			)			->	Mapping[str, Mapping[int, str]]:
					if self.task == "multiple-choice":
									lowercase_	    =   {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
					else:
									lowercase_	    =   {0: '''batch''', 1: '''sequence'''}
					return OrderedDict(
					    [
					        ('''input_ids''', dynamic_axis),
					        ('''attention_mask''', dynamic_axis),
					        ('''token_type_ids''', dynamic_axis),
					    ]			)




