

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  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[torch.FloatTensor]       			=			None
				__SCREAMING_SNAKE_CASE					: torch.FloatTensor       			=			None
				__SCREAMING_SNAKE_CASE					: Optional[Tuple[torch.FloatTensor]]       			=			None
				__SCREAMING_SNAKE_CASE					: Optional[Tuple[torch.FloatTensor]]       			=			None







class  lowercase				(       lowercase_			):




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







class  lowercase				(       lowercase_			):
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			[R'''pooler''', R'''logit_scale''']
				__SCREAMING_SNAKE_CASE					: str       			=			[R'''position_ids''', R'''predictions.decoder.bias''']
				__SCREAMING_SNAKE_CASE					: List[str]       			=			'''roberta'''
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			RobertaSeriesConfig




				def __init__(					self      ,   snake_case       ):
											super().__init__(snake_case       )
											snake_case_							=    XLMRobertaModel(snake_case       )
											snake_case_							=    nn.Linear(config.hidden_size      ,   config.project_dim       )
											snake_case_							=    getattr(snake_case      ,   'has_pre_transformation'      ,   snake_case       )
											if self.has_pre_transformation:
																		snake_case_							=    nn.Linear(config.hidden_size      ,   config.project_dim       )
																		snake_case_							=    nn.LayerNorm(config.hidden_size      ,   eps=config.layer_norm_eps       )
											self.post_init()




				def     a							(					self      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   ):
											snake_case_							=    return_dict if return_dict is not None else self.config.use_return_dict

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

											if self.has_pre_transformation:
																		snake_case_							=    outputs['hidden_states'][-2]
																		snake_case_							=    self.pre_LN(snake_case       )
																		snake_case_							=    self.transformation_pre(snake_case       )

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







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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    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}}'      )
							snake_case_							=    DatasetInfosDict.from_directory(UpperCamelCase__      )
							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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( ):







							'''simple docstring'''






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





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    DatasetInfo()
							snake_case_							=    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 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






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

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




