



"""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     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_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     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	def __init__(      self				:       Tuple     ,       _SCREAMING_SNAKE_CASE				:       int=1     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=0     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       Any=512     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]="cls"     ,       _SCREAMING_SNAKE_CASE				:       int=False     ,       _SCREAMING_SNAKE_CASE				:       Dict=True     ,       **_SCREAMING_SNAKE_CASE				:       int     ,       )						->	Dict:





					"""simple docstring"""


					super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     project_dim
					SCREAMING_SNAKE_CASE						:							Dict							=     pooler_fn
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     learn_encoder
					SCREAMING_SNAKE_CASE						:							Tuple							=     use_attention_mask




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		List[Any]  						= [r'''pooler''', r'''logit_scale''']
	_SCREAMING_SNAKE_CASE	:		List[Any]  						= [r'''position_ids''', r'''predictions.decoder.bias''']
	_SCREAMING_SNAKE_CASE	:		Union[str, Any]  						= '''roberta'''
	_SCREAMING_SNAKE_CASE	:		List[Any]  						= RobertaSeriesConfig





	def __init__(      self				:       str     ,       _SCREAMING_SNAKE_CASE				:       Dict    )						->	Tuple:





					"""simple docstring"""


					super().__init__(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							int							=     XLMRobertaModel(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     nn.Linear(config.hidden_size     ,       config.project_dim    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     getattr(_SCREAMING_SNAKE_CASE     ,       'has_pre_transformation'     ,       _SCREAMING_SNAKE_CASE    )
					if self.has_pre_transformation:
									SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     nn.Linear(config.hidden_size     ,       config.project_dim    )
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     nn.LayerNorm(config.hidden_size     ,       eps=config.layer_norm_eps    )
					self.post_init()







	def 			_lowerCAmelCase     (      self				:       List[Any]     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[torch.Tensor] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       _SCREAMING_SNAKE_CASE				:       Optional[bool] = None     ,       )						->	Optional[int]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     return_dict if return_dict is not None else self.config.use_return_dict

					SCREAMING_SNAKE_CASE						:							Tuple							=     self.base_model(
					    input_ids=_SCREAMING_SNAKE_CASE     ,       attention_mask=_SCREAMING_SNAKE_CASE     ,       token_type_ids=_SCREAMING_SNAKE_CASE     ,       position_ids=_SCREAMING_SNAKE_CASE     ,       head_mask=_SCREAMING_SNAKE_CASE     ,       inputs_embeds=_SCREAMING_SNAKE_CASE     ,       encoder_hidden_states=_SCREAMING_SNAKE_CASE     ,       encoder_attention_mask=_SCREAMING_SNAKE_CASE     ,       output_attentions=_SCREAMING_SNAKE_CASE     ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states     ,       return_dict=_SCREAMING_SNAKE_CASE     ,       )

					if self.has_pre_transformation:
									SCREAMING_SNAKE_CASE						:							List[str]							=     outputs['hidden_states'][-2]
									SCREAMING_SNAKE_CASE						:							str							=     self.pre_LN(_SCREAMING_SNAKE_CASE    )
									SCREAMING_SNAKE_CASE						:							List[str]							=     self.transformation_pre(_SCREAMING_SNAKE_CASE    )

									return TransformationModelOutput(
									    projection_state=_SCREAMING_SNAKE_CASE     ,       last_hidden_state=outputs.last_hidden_state     ,       hidden_states=outputs.hidden_states     ,       attentions=outputs.attentions     ,       )
					else:
									SCREAMING_SNAKE_CASE						:							Optional[Any]							=     self.transformation(outputs.last_hidden_state    )
									return TransformationModelOutput(
									    projection_state=_SCREAMING_SNAKE_CASE     ,       last_hidden_state=outputs.last_hidden_state     ,       hidden_states=outputs.hidden_states     ,       attentions=outputs.attentions     ,       )










"""simple docstring"""

import importlib
import os

import fsspec
import pytest
from fsspec import register_implementation
from fsspec.registry import _registry as _fsspec_registry

from datasets.filesystems import COMPRESSION_FILESYSTEMS, HfFileSystem, extract_path_from_uri, is_remote_filesystem

from .utils import require_lza, require_zstandard
def  __snake_case							(	__A      :						str     )     ->       Dict:






				'''simple docstring'''
				assert "mock" in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Union[str, Any]:






				'''simple docstring'''
				assert "mock" not in _fsspec_registry
				assert "bz2" in _fsspec_registry
def  __snake_case							(	)     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     'mock-s3-bucket'
				SCREAMING_SNAKE_CASE						:							Tuple							=     F"""s3://{mock_bucket}"""
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path.startswith('s3://'     ) is False

				SCREAMING_SNAKE_CASE						:							Optional[int]							=     './local/path'
				SCREAMING_SNAKE_CASE						:							str							=     extract_path_from_uri(__A     )
				assert dataset_path == new_dataset_path
def  __snake_case							(	__A      :						Tuple     )     ->       Optional[int]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Tuple							=     is_remote_filesystem(__A     )
				assert is_remote is True

				SCREAMING_SNAKE_CASE						:							Dict							=     fsspec.filesystem('file'     )

				SCREAMING_SNAKE_CASE						:							Dict							=     is_remote_filesystem(__A     )
				assert is_remote is False
@pytest.mark.parametrize('compression_fs_class'							,		__A     )
def  __snake_case							(	__A      :						Any							,		__A      :						Optional[int]							,		__A      :						List[Any]							,		__A      :						Optional[Any]							,		__A      :						str							,		__A      :						Tuple							,		__A      :						Dict     )     ->       Tuple:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     {'gzip': gz_file, 'xz': xz_file, 'zstd': zstd_file, 'bz2': bza_file, 'lz4': lza_file}
				SCREAMING_SNAKE_CASE						:							List[Any]							=     input_paths[compression_fs_class.protocol]
				if input_path is None:
								SCREAMING_SNAKE_CASE						:							str							=     F"""for '{compression_fs_class.protocol}' compression protocol, """
								if compression_fs_class.protocol == "lz4":
												reason += require_lza.kwargs["reason"]
								elif compression_fs_class.protocol == "zstd":
												reason += require_zstandard.kwargs["reason"]
								pytest.skip(__A     )
				SCREAMING_SNAKE_CASE						:							Any							=     fsspec.filesystem(compression_fs_class.protocol							,		fo=__A     )
				assert isinstance(__A							,		__A     )
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     os.path.basename(__A     )
				SCREAMING_SNAKE_CASE						:							Dict							=     expected_filename[: expected_filename.rindex('.'     )]
				assert fs.glob('*'     ) == [expected_filename]
				with fs.open(__A							,		'r'							,		encoding='utf-8'     ) as f, open(__A							,		encoding='utf-8'     ) as expected_file:
								assert f.read() == expected_file.read()
@pytest.mark.parametrize('protocol'							,		['zip', 'gzip']     )
def  __snake_case							(	__A      :						Any							,		__A      :						int							,		__A      :						Union[str, Any]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[Any]							=     {'zip': zip_jsonl_path, 'gzip': jsonl_gz_path}
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     compressed_file_paths[protocol]
				SCREAMING_SNAKE_CASE						:							str							=     'dataset.jsonl'
				SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     F"""{protocol}://{member_file_path}::{compressed_file_path}"""
				SCREAMING_SNAKE_CASE				,						*SCREAMING_SNAKE_CASE						:							Any							=     fsspec.get_fs_token_paths(__A     )
				assert fs.isfile(__A     )
				assert not fs.isfile('non_existing_' + member_file_path     )
@pytest.mark.integration
def  __snake_case							(	__A      :						List[str]							,		__A      :						int							,		__A      :						int							,		__A      :						Optional[int]     )     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							List[str]							=     hf_api.dataset_info(__A							,		token=__A     )
				SCREAMING_SNAKE_CASE						:							str							=     HfFileSystem(repo_info=__A							,		token=__A     )
				assert sorted(hffs.glob('*'     )     ) == [".gitattributes", "data"]
				assert hffs.isdir('data'     )
				assert hffs.isfile('.gitattributes'     ) and hffs.isfile('data/text_data.txt'     )
				with open(__A     ) as f:
								assert hffs.open('data/text_data.txt'							,		'r'     ).read() == f.read()







def  __snake_case							(	)     ->       Union[str, Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE						:							Optional[int]							=     'bz2'

				# Import module
				import datasets.filesystems

				# Overwrite protocol and reload
				register_implementation(__A							,		__A							,		clobber=__A     )
				with pytest.warns(__A     ) as warning_info:
								importlib.reload(datasets.filesystems     )

				assert len(__A     ) == 1
				assert (
				    str(warning_info[0].message     )
				    == F"""A filesystem protocol was already set for {protocol} and will be overwritten."""
				)





