
"""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 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					assert "mock" in _fsspec_registry
					assert "bz2" in _fsspec_registry



def 	UpperCamelCase   (							):
					'''simple docstring'''
					assert "mock" not in _fsspec_registry
					assert "bz2" in _fsspec_registry



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						'''mock-s3-bucket'''
					lowercase						     =						f's3://{mock_bucket}'
					lowercase						     =						extract_path_from_uri(lowerCAmelCase__		)
					assert dataset_path.startswith('''s3://'''		) is False

					lowercase						     =						'''./local/path'''
					lowercase						     =						extract_path_from_uri(lowerCAmelCase__		)
					assert dataset_path == new_dataset_path



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						is_remote_filesystem(lowerCAmelCase__		)
					assert is_remote is True

					lowercase						     =						fsspec.filesystem('''file'''		)

					lowercase						     =						is_remote_filesystem(lowerCAmelCase__		)
					assert is_remote is False



@pytest.mark.parametrize('''compression_fs_class'''			,      lowerCAmelCase__		)
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						{'''gzip''': gz_file, '''xz''': xz_file, '''zstd''': zstd_file, '''bz2''': bza_file, '''lz4''': lza_file}
					lowercase						     =						input_paths[compression_fs_class.protocol]
					if input_path is None:
										lowercase						     =						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(lowerCAmelCase__		)
					lowercase						     =						fsspec.filesystem(compression_fs_class.protocol			,      fo=lowerCAmelCase__		)
					assert isinstance(lowerCAmelCase__			,      lowerCAmelCase__		)
					lowercase						     =						os.path.basename(lowerCAmelCase__		)
					lowercase						     =						expected_filename[: expected_filename.rindex('''.'''		)]
					assert fs.glob('''*'''		) == [expected_filename]
					with fs.open(lowerCAmelCase__			,      '''r'''			,      encoding='''utf-8'''		) as f, open(lowerCAmelCase__			,      encoding='''utf-8'''		) as expected_file:
										assert f.read() == expected_file.read()



@pytest.mark.parametrize('''protocol'''			,      ['''zip''', '''gzip''']		)
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						{'''zip''': zip_jsonl_path, '''gzip''': jsonl_gz_path}
					lowercase						     =						compressed_file_paths[protocol]
					lowercase						     =						'''dataset.jsonl'''
					lowercase						     =						f'{protocol}://{member_file_path}::{compressed_file_path}'
					lowercase     ,						*lowercase						     =						fsspec.get_fs_token_paths(lowerCAmelCase__		)
					assert fs.isfile(lowerCAmelCase__		)
					assert not fs.isfile('''non_existing_''' + member_file_path		)



@pytest.mark.integration
def 	UpperCamelCase   (							lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__			,      lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						hf_api.dataset_info(lowerCAmelCase__			,      token=lowerCAmelCase__		)
					lowercase						     =						HfFileSystem(repo_info=lowerCAmelCase__			,      token=lowerCAmelCase__		)
					assert sorted(hffs.glob('''*'''		)		) == [".gitattributes", "data"]
					assert hffs.isdir('''data'''		)
					assert hffs.isfile('''.gitattributes'''		) and hffs.isfile('''data/text_data.txt'''		)
					with open(lowerCAmelCase__		) as f:
										assert hffs.open('''data/text_data.txt'''			,      '''r'''		).read() == f.read()



def 	UpperCamelCase   (							):
					'''simple docstring'''
					lowercase						     =						'''bz2'''

					# Import module
					import datasets.filesystems

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

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






import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__		:Dict			  =   logging.get_logger(__name__)

lowercase__		:Optional[Any]			  =   "▁"

lowercase__		:str			  =   {"vocab_file": "prophetnet.tokenizer"}

lowercase__		:List[str]			  =   {
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

lowercase__		:Optional[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

lowercase__		:List[Any]			  =   {
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					lowercase						     =						collections.OrderedDict()
					with open(lowerCAmelCase__			,      '''r'''			,      encoding='''utf-8'''		) as reader:
										lowercase						     =						reader.readlines()
					for index, token in enumerate(lowerCAmelCase__		):
										lowercase						     =						token.rstrip('''\n'''		)
										lowercase						     =						index
					return vocab







class     lowercase		( SCREAMING_SNAKE_CASE__		):
							lowercase_	:	Optional[Any]	 =VOCAB_FILES_NAMES
							lowercase_	:	Any	 =PRETRAINED_VOCAB_FILES_MAP
							lowercase_	:	Any	 =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_	:	str	 =['''input_ids''', '''attention_mask''']




							def __init__(    self       ,A__       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[SEP]"       ,A__="[UNK]"       ,A__="[PAD]"       ,A__="[CLS]"       ,A__="[MASK]"       ,A__ = None       ,**A__       ,):
												lowercase						     =						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    bos_token=A__       ,eos_token=A__       ,sep_token=A__       ,unk_token=A__       ,pad_token=A__       ,cls_token=A__       ,mask_token=A__       ,sp_model_kwargs=self.sp_model_kwargs       ,**A__       ,)

												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(str(A__))
												lowercase						     =						vocab_file

												# Original fairseq vocab and spm vocab must be "aligned":
												# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
												# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
												# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
												# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

												# put special tokens and [unused] tokens into the vocab
												lowercase						     =						{'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

												for i in range(1_0):
																	lowercase						     =						f'[unused{i}]'
																	lowercase						     =						5 + i

												# The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
												lowercase						     =						1_2
												lowercase						     =						{v: k for k, v in self.fairseq_tokens_to_ids.items()}
												for k in self.fairseq_tokens_to_ids.keys():
																	self.unique_no_split_tokens.append(A__)




							def __getstate__(    self):
												lowercase						     =						self.__dict__.copy()
												lowercase						     =						None
												return state




							def __setstate__(    self       ,A__):
												lowercase						     =						d
												try:
																	import sentencepiece as spm
												except ImportError:
																	logger.warning(
																	    '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
																	    ''' pip install sentencepiece''')
																	raise

												# for backward compatibility
												if not hasattr(self       ,'''sp_model_kwargs'''):
																	lowercase						     =						{}

												lowercase						     =						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)




							def 			A__				(    self       ,A__       ,A__ = None       ,A__ = False):

												if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=A__       ,token_ids_a=A__       ,already_has_special_tokens=A__)

												if token_ids_a is None:
																	return ([0] * len(A__)) + [1]
												return ([0] * len(A__)) + [1] + ([0] * len(A__)) + [1]




							def 			A__				(    self       ,A__       ,A__ = None):
												lowercase						     =						[self.sep_token_id]

												if token_ids_a is None:
																	return len(token_ids_a + sep) * [0]
												return len(token_ids_a + sep + sep + token_ids_a + sep) * [0]




							@property
							def 			A__				(    self):
												return len(self.sp_model) + self.fairseq_offset




							def 			A__				(    self):
												lowercase						     =						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def 			A__				(    self       ,A__):
												return self.sp_model.encode(A__       ,out_type=A__)




							def 			A__				(    self       ,A__):
												if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
												lowercase						     =						self.sp_model.PieceToId(A__)

												# Need to return unknown token if the SP model returned 0
												return spm_id + self.fairseq_offset if spm_id else self.unk_token_id




							def 			A__				(    self       ,A__):
												if index in self.fairseq_ids_to_tokens:
																	return self.fairseq_ids_to_tokens[index]
												return self.sp_model.IdToPiece(index - self.fairseq_offset)




							def 			A__				(    self       ,A__):
												lowercase						     =						''''''.join(A__).replace(A__       ,''' ''').strip()
												return out_string




							def 			A__				(    self       ,A__       ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase						     =						os.path.join(
												    A__       ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

												if os.path.abspath(self.vocab_file) != os.path.abspath(A__) and os.path.isfile(self.vocab_file):
																	copyfile(self.vocab_file       ,A__)
												elif not os.path.isfile(self.vocab_file):
																	with open(A__       ,'''wb''') as fi:
																						lowercase						     =						self.sp_model.serialized_model_proto()
																						fi.write(A__)

												return (out_vocab_file,)





							def 			A__				(    self       ,A__       ,A__ = None):

												if token_ids_a is None:
																	return token_ids_a + [self.sep_token_id]
												lowercase						     =						[self.sep_token_id]
												return token_ids_a + sep + token_ids_a + sep




