





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      _UpperCamelCase  (a__ :str ,      a__ :Union[str, Any]							):






							"""simple docstring"""





							UpperCamelCase__					  =      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}}"""							)
							UpperCamelCase__					  =      DatasetInfosDict.from_directory(a__							)
							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      _UpperCamelCase  (a__ :str ,      a__ :DatasetInfo							):






							"""simple docstring"""





							UpperCamelCase__					  =      str(a__							)
							dataset_info.write_to_directory(a__							)
							UpperCamelCase__					  =      DatasetInfo.from_directory(a__							)
							assert dataset_info == reloaded
							assert os.path.exists(os.path.join(a__ ,      """dataset_info.json"""							)							)





def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      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 ,      )
							UpperCamelCase__					  =      dataset_info._to_yaml_dict()
							assert sorted(a__							) == 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)							)
							UpperCamelCase__					  =      yaml.safe_dump(a__							)
							UpperCamelCase__					  =      yaml.safe_load(a__							)
							assert dataset_info_yaml_dict == reloaded





def      _UpperCamelCase  ():






							"""simple docstring"""





							UpperCamelCase__					  =      DatasetInfo()
							UpperCamelCase__					  =      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      _UpperCamelCase  (a__ :str ,      a__ :DatasetInfosDict							):






							"""simple docstring"""





							UpperCamelCase__					  =      str(a__							)
							dataset_infos_dict.write_to_directory(a__							)
							UpperCamelCase__					  =      DatasetInfosDict.from_directory(a__							)

							# the config_name of the dataset_infos_dict take over the attribute
							for config_name, dataset_info in dataset_infos_dict.items():
														UpperCamelCase__					  =      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
														UpperCamelCase__					  =      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(a__ ,      """README.md"""							)							)











from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput

@dataclass
class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   Optional[torch.FloatTensor]      =							None
							snake_case  :   torch.FloatTensor      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None
							snake_case  :   Optional[Tuple[torch.FloatTensor]]      =							None






class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			__lowerCAmelCase=1	,			__lowerCAmelCase=0	,			__lowerCAmelCase=2	,			__lowerCAmelCase=512	,			__lowerCAmelCase="cls"	,			__lowerCAmelCase=False	,			__lowerCAmelCase=True	,			**__lowerCAmelCase	,			):
														super().__init__(pad_token_id=__lowerCAmelCase	,			bos_token_id=__lowerCAmelCase	,			eos_token_id=__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      project_dim
														UpperCamelCase__					  =      pooler_fn
														UpperCamelCase__					  =      learn_encoder
														UpperCamelCase__					  =      use_attention_mask






class 							__SCREAMING_SNAKE_CASE       (  _a							):
							snake_case  :   int      =							[r"""pooler""", r"""logit_scale"""]
							snake_case  :   Tuple      =							[r"""position_ids""", r"""predictions.decoder.bias"""]
							snake_case  :   str      =							"""roberta"""
							snake_case  :   Dict      =							RobertaSeriesConfig






							def __init__(    self	,			__lowerCAmelCase	):
														super().__init__(__lowerCAmelCase	)
														UpperCamelCase__					  =      XLMRobertaModel(__lowerCAmelCase	)
														UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
														UpperCamelCase__					  =      getattr(__lowerCAmelCase	,			"""has_pre_transformation"""	,			__lowerCAmelCase	)
														if self.has_pre_transformation:
																					UpperCamelCase__					  =      nn.Linear(config.hidden_size	,			config.project_dim	)
																					UpperCamelCase__					  =      nn.LayerNorm(config.hidden_size	,			eps=config.layer_norm_eps	)
														self.post_init()







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			__lowerCAmelCase = None	,			):
														UpperCamelCase__					  =      return_dict if return_dict is not None else self.config.use_return_dict

														UpperCamelCase__					  =      self.base_model(
														    input_ids=__lowerCAmelCase	,			attention_mask=__lowerCAmelCase	,			token_type_ids=__lowerCAmelCase	,			position_ids=__lowerCAmelCase	,			head_mask=__lowerCAmelCase	,			inputs_embeds=__lowerCAmelCase	,			encoder_hidden_states=__lowerCAmelCase	,			encoder_attention_mask=__lowerCAmelCase	,			output_attentions=__lowerCAmelCase	,			output_hidden_states=True if self.has_pre_transformation else output_hidden_states	,			return_dict=__lowerCAmelCase	,			)

														if self.has_pre_transformation:
																					UpperCamelCase__					  =      outputs["""hidden_states"""][-2]
																					UpperCamelCase__					  =      self.pre_LN(__lowerCAmelCase	)
																					UpperCamelCase__					  =      self.transformation_pre(__lowerCAmelCase	)

																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)
														else:
																					UpperCamelCase__					  =      self.transformation(outputs.last_hidden_state	)
																					return TransformationModelOutput(
																					    projection_state=__lowerCAmelCase	,			last_hidden_state=outputs.last_hidden_state	,			hidden_states=outputs.hidden_states	,			attentions=outputs.attentions	,			)




