

'''simple docstring'''

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 	A_			(    lowerCamelCase__							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[torch.FloatTensor]  		=  None
     UpperCAmelCase_						:     torch.FloatTensor  		=  None
     UpperCAmelCase_						:     Optional[Tuple[torch.FloatTensor]]  		=  None
     UpperCAmelCase_						:     Optional[Tuple[torch.FloatTensor]]  		=  None




class 	A_			(    lowerCamelCase__							):






     '''simple docstring'''



     def __init__(		self			:					Dict					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					int=0					,      lowercase_			:					Dict=2					,      lowercase_			:					List[str]=512					,      lowercase_			:					int="cls"					,      lowercase_			:					int=False					,      lowercase_			:					Dict=True					,      **lowercase_			:					Optional[Any]					,      )						->     Any:
       super().__init__(pad_token_id=lowercase_					,      bos_token_id=lowercase_					,      eos_token_id=lowercase_					,      **lowercase_	)
       UpperCAmelCase	:     str						 =			project_dim
       UpperCAmelCase	:     int						 =			pooler_fn
       UpperCAmelCase	:     Union[str, Any]						 =			learn_encoder
       UpperCAmelCase	:     Union[str, Any]						 =			use_attention_mask




class 	A_			(    lowerCamelCase__							):






     '''simple docstring'''



     UpperCAmelCase_						:     int  		=  [r'pooler', r'logit_scale']
     UpperCAmelCase_						:     Union[str, Any]  		=  [r'position_ids', r'predictions.decoder.bias']
     UpperCAmelCase_						:     Any  		=  'roberta'
     UpperCAmelCase_						:     Optional[Any]  		=  RobertaSeriesConfig


     def __init__(		self			:					Tuple					,      lowercase_			:					str	)						->     Any:
       super().__init__(lowercase_	)
       UpperCAmelCase	:     Optional[Any]						 =			XLMRobertaModel(lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			nn.Linear(config.hidden_size					,      config.project_dim	)
       UpperCAmelCase	:     Optional[int]						 =			getattr(lowercase_					,      'has_pre_transformation'					,      lowercase_	)
       if self.has_pre_transformation:
         UpperCAmelCase	:     str						 =			nn.Linear(config.hidden_size					,      config.project_dim	)
         UpperCAmelCase	:     Union[str, Any]						 =			nn.LayerNorm(config.hidden_size					,      eps=config.layer_norm_eps	)
       self.post_init()




     def        UpperCAmelCase_	(		self			:					List[Any]					,      lowercase_			:					Optional[Any] = None					,      lowercase_			:					Optional[int] = None					,      lowercase_			:					int = None					,      lowercase_			:					List[str] = None					,      lowercase_			:					Optional[Any] = None					,      lowercase_			:					str = None					,      lowercase_			:					List[str] = None					,      lowercase_			:					Union[str, Any] = None					,      lowercase_			:					Union[str, Any] = None					,      lowercase_			:					str = None					,      lowercase_			:					Optional[Any] = None					,      )						->     Union[str, Any]:
       UpperCAmelCase	:     int						 =			return_dict if return_dict is not None else self.config.use_return_dict

       UpperCAmelCase	:     Optional[int]						 =			self.base_model(
           input_ids=lowercase_					,      attention_mask=lowercase_					,      token_type_ids=lowercase_					,      position_ids=lowercase_					,      head_mask=lowercase_					,      inputs_embeds=lowercase_					,      encoder_hidden_states=lowercase_					,      encoder_attention_mask=lowercase_					,      output_attentions=lowercase_					,      output_hidden_states=True if self.has_pre_transformation else output_hidden_states					,      return_dict=lowercase_					,      )

       if self.has_pre_transformation:
         UpperCAmelCase	:     List[str]						 =			outputs['hidden_states'][-2]
         UpperCAmelCase	:     Tuple						 =			self.pre_LN(lowercase_	)
         UpperCAmelCase	:     Optional[Any]						 =			self.transformation_pre(lowercase_	)

         return TransformationModelOutput(
             projection_state=lowercase_					,      last_hidden_state=outputs.last_hidden_state					,      hidden_states=outputs.hidden_states					,      attentions=outputs.attentions					,      )
       else:
         UpperCAmelCase	:     Dict						 =			self.transformation(outputs.last_hidden_state	)
         return TransformationModelOutput(
             projection_state=lowercase_					,      last_hidden_state=outputs.last_hidden_state					,      hidden_states=outputs.hidden_states					,      attentions=outputs.attentions					,      )







'''simple docstring'''

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.num_rows == 4
 assert dataset.num_columns == 1
 assert dataset.column_names == ["text"]
 for feature, expected_dtype in expected_features.items():
  assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'  ,	[False, True]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     Tuple						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 UpperCAmelCase	:     Optional[int]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     Union[str, Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[int]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_  ,	split=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )
 assert dataset.split == split if split else "train"




@pytest.mark.parametrize('path_type'  ,	[str, list]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Tuple						 =			text_path
 elif issubclass(UpperCAmelCase_  ,	UpperCAmelCase_     ):
  UpperCAmelCase	:     Optional[Any]						 =			[text_path]
 UpperCAmelCase	:     List[Any]						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Union[str, Any]						 =			{'text': 'string'}
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_dataset(UpperCAmelCase_  ,	UpperCAmelCase_     )




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_=("train",)     ):
 assert isinstance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 for split in splits:
  UpperCAmelCase	:     Union[str, Any]						 =			dataset_dict[split]
  assert dataset.num_rows == 4
  assert dataset.num_columns == 1
  assert dataset.column_names == ["text"]
  for feature, expected_dtype in expected_features.items():
   assert dataset.features[feature].dtype == expected_dtype




@pytest.mark.parametrize('keep_in_memory'  ,	[False, True]     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Any						 =			tmp_path / 'cache'
 UpperCAmelCase	:     List[str]						 =			{'text': 'string'}
 with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
  UpperCAmelCase	:     int						 =			TextDatasetReader({'train': text_path}  ,	cache_dir=UpperCAmelCase_  ,	keep_in_memory=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize(
    'features'  ,	[
        None,
        {'text': 'string'},
        {'text': 'int32'},
        {'text': 'float32'},
    ]  ,	)
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Union[str, Any]						 =			tmp_path / 'cache'
 # CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
 UpperCAmelCase	:     Tuple						 =			{'text': 'string'}
 UpperCAmelCase	:     Union[str, Any]						 =			features.copy() if features else default_expected_features
 UpperCAmelCase	:     int						 =			(
     Features({feature: Value(UpperCAmelCase_     ) for feature, dtype in features.items()}     ) if features is not None else None
 )
 UpperCAmelCase	:     List[Any]						 =			TextDatasetReader({'train': text_path}  ,	features=UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_     )




@pytest.mark.parametrize('split'  ,	[None, NamedSplit('train'     ), 'train', 'test']     )
def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_  ,	UpperCAmelCase_     ):
 if split:
  UpperCAmelCase	:     int						 =			{split: text_path}
 else:
  UpperCAmelCase	:     int						 =			'train'
  UpperCAmelCase	:     Any						 =			{'train': text_path, 'test': text_path}
 UpperCAmelCase	:     Dict						 =			tmp_path / 'cache'
 UpperCAmelCase	:     Any						 =			{'text': 'string'}
 UpperCAmelCase	:     List[str]						 =			TextDatasetReader(UpperCAmelCase_  ,	cache_dir=UpperCAmelCase_     ).read()
 _check_text_datasetdict(UpperCAmelCase_  ,	UpperCAmelCase_  ,	splits=list(path.keys()     )     )
 assert all(dataset[split].split == split for split in path.keys()     )




