




import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class       a_       (     A__  ,    unittest.TestCase     ):
	A    							= MvpTokenizer
	A    							= MvpTokenizerFast
	A    							= True
	A    							= filter_roberta_detectors





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				super().setUp()
				SCREAMING_SNAKE_CASE_								=						[
				    'l',
				    'o',
				    'w',
				    'e',
				    'r',
				    's',
				    't',
				    'i',
				    'd',
				    'n',
				    '\u0120',
				    '\u0120l',
				    '\u0120n',
				    '\u0120lo',
				    '\u0120low',
				    'er',
				    '\u0120lowest',
				    '\u0120newer',
				    '\u0120wider',
				    '<unk>',
				]
				SCREAMING_SNAKE_CASE_								=						dict(zip(_lowerCamelCase      ,     range(len(_lowerCamelCase      )      )      )      )
				SCREAMING_SNAKE_CASE_								=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
				SCREAMING_SNAKE_CASE_								=						{'unk_token': '<unk>'}

				SCREAMING_SNAKE_CASE_								=						os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['vocab_file']      )
				SCREAMING_SNAKE_CASE_								=						os.path.join(self.tmpdirname      ,     VOCAB_FILES_NAMES['merges_file']      )
				with open(self.vocab_file      ,     'w'      ,     encoding='utf-8'      ) as fp:
							fp.write(json.dumps(_lowerCamelCase      ) + '\n'      )
				with open(self.merges_file      ,     'w'      ,     encoding='utf-8'      ) as fp:
							fp.write('\n'.join(_lowerCamelCase      )      )





	def 						A_(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				kwargs.update(self.special_tokens_map      )
				return self.tokenizer_class.from_pretrained(self.tmpdirname      ,     **_lowerCamelCase      )





	def 						A_(							self      ,     **SCREAMING_SNAKE_CASE      )  ->					Dict:




				"""simple docstring"""



				kwargs.update(self.special_tokens_map      )
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,     **_lowerCamelCase      )





	def 						A_(							self      ,     SCREAMING_SNAKE_CASE      )  ->					Any:




				"""simple docstring"""



				return "lower newer", "lower newer"





	@cached_property
	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )





	@cached_property
	def 						A_(							self      )  ->					Optional[int]:




				"""simple docstring"""



				return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )





	@require_torch
	def 						A_(							self      )  ->					Tuple:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
				SCREAMING_SNAKE_CASE_								=						[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							SCREAMING_SNAKE_CASE_								=						tokenizer(_lowerCamelCase      ,     max_length=len(_lowerCamelCase      )      ,     padding=_lowerCamelCase      ,     return_tensors='pt'      )
							self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase      )

							self.assertEqual((2, 9)      ,     batch.input_ids.shape      )
							self.assertEqual((2, 9)      ,     batch.attention_mask.shape      )
							SCREAMING_SNAKE_CASE_								=						batch.input_ids.tolist()[0]
							self.assertListEqual(_lowerCamelCase      ,     _lowerCamelCase      )
							# Test that special tokens are reset





	@require_torch
	def 						A_(							self      )  ->					str:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						['A long paragraph for summarization.', 'Another paragraph for summarization.']
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							SCREAMING_SNAKE_CASE_								=						tokenizer(_lowerCamelCase      ,     padding=_lowerCamelCase      ,     return_tensors='pt'      )
							# check if input_ids are returned and no labels
							self.assertIn('input_ids'      ,     _lowerCamelCase      )
							self.assertIn('attention_mask'      ,     _lowerCamelCase      )
							self.assertNotIn('labels'      ,     _lowerCamelCase      )
							self.assertNotIn('decoder_attention_mask'      ,     _lowerCamelCase      )





	@require_torch
	def 						A_(							self      )  ->					Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						[
				    'Summary of the text.',
				    'Another summary.',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							SCREAMING_SNAKE_CASE_								=						tokenizer(text_target=_lowerCamelCase      ,     max_length=32      ,     padding='max_length'      ,     return_tensors='pt'      )
							self.assertEqual(32      ,     targets['input_ids'].shape[1]      )





	@require_torch
	def 						A_(							self      )  ->					Dict:




				"""simple docstring"""



				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							SCREAMING_SNAKE_CASE_								=						tokenizer(
							    ['I am a small frog' * 1024, 'I am a small frog']      ,     padding=_lowerCamelCase      ,     truncation=_lowerCamelCase      ,     return_tensors='pt'      )
							self.assertIsInstance(_lowerCamelCase      ,     _lowerCamelCase      )
							self.assertEqual(batch.input_ids.shape      ,     (2, 1024)      )





	@require_torch
	def 						A_(							self      )  ->					Union[str, Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_								=						['A long paragraph for summarization.']
				SCREAMING_SNAKE_CASE_								=						[
				    'Summary of the text.',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							SCREAMING_SNAKE_CASE_								=						tokenizer(_lowerCamelCase      ,     text_target=_lowerCamelCase      ,     return_tensors='pt'      )
							SCREAMING_SNAKE_CASE_								=						inputs['input_ids']
							SCREAMING_SNAKE_CASE_								=						inputs['labels']
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()      )
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()      )
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()      )
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()      )





	def 						A_(							self      )  ->					int:




				"""simple docstring"""



				pass





	def 						A_(							self      )  ->					List[Any]:




				"""simple docstring"""



				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'      ):
										SCREAMING_SNAKE_CASE_								=						self.rust_tokenizer_class.from_pretrained(_lowerCamelCase      ,     **_lowerCamelCase      )
										SCREAMING_SNAKE_CASE_								=						self.tokenizer_class.from_pretrained(_lowerCamelCase      ,     **_lowerCamelCase      )
										SCREAMING_SNAKE_CASE_								=						'A, <mask> AllenNLP sentence.'
										SCREAMING_SNAKE_CASE_								=						tokenizer_r.encode_plus(_lowerCamelCase      ,     add_special_tokens=_lowerCamelCase      ,     return_token_type_ids=_lowerCamelCase      )
										SCREAMING_SNAKE_CASE_								=						tokenizer_p.encode_plus(_lowerCamelCase      ,     add_special_tokens=_lowerCamelCase      ,     return_token_type_ids=_lowerCamelCase      )

										# token_type_ids should put 0 everywhere
										self.assertEqual(sum(tokens_r['token_type_ids']      )      ,     sum(tokens_p['token_type_ids']      )      )

										# attention_mask should put 1 everywhere, so sum over length should be 1
										self.assertEqual(
										    sum(tokens_r['attention_mask']      ) / len(tokens_r['attention_mask']      )      ,     sum(tokens_p['attention_mask']      ) / len(tokens_p['attention_mask']      )      ,     )

										SCREAMING_SNAKE_CASE_								=						tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
										SCREAMING_SNAKE_CASE_								=						tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']      )

										# Rust correctly handles the space before the mask while python doesnt
										self.assertSequenceEqual(tokens_p['input_ids']      ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )
										self.assertSequenceEqual(tokens_r['input_ids']      ,     [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]      )

										self.assertSequenceEqual(
										    _lowerCamelCase      ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )
										self.assertSequenceEqual(
										    _lowerCamelCase      ,     ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']      )


"""simple docstring"""






from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url





class 				a__					(						A__       ):
     UpperCAmelCase__         =      ''''''
     UpperCAmelCase__         =      '''hf-legacy'''  # "hf://"" is reserved for hffs



     def __init__(							self :Dict      ,  _lowerCamelCase :Optional[DatasetInfo] = None      ,  _lowerCamelCase :Optional[str] = None      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(self      ,  **_lowerCamelCase   )
      UpperCamelCase_     :					List[str]   	=repo_info
      UpperCamelCase_     :					Any   	=token
      UpperCamelCase_     :					Tuple   	=None



     def  lowerCamelCase_						(							self :Dict   ):







      '''simple docstring'''



      if self.dir_cache is None:
       UpperCamelCase_     :					Any   	={}
       for hf_file in self.repo_info.siblings:
        # TODO(QL): add sizes
        UpperCamelCase_     :					Optional[Any]   	={
            'name': hf_file.rfilename,
            'size': None,
            'type': 'file',
        }
        self.dir_cache.update(
            {
                str(_lowerCamelCase   ): {'name': str(_lowerCamelCase   ), 'size': None, 'type': 'directory'}
                for d in list(PurePosixPath(hf_file.rfilename   ).parents   )[:-1]
            }   )



     def  lowerCamelCase_						(							self :Union[str, Any]      ,  _lowerCamelCase :str      ,  _lowerCamelCase :str = "rb"      ,  **_lowerCamelCase :str      ,  ):







      '''simple docstring'''



      if not isinstance(self.repo_info      ,  _lowerCamelCase   ):
       raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}'''   )
      UpperCamelCase_     :					List[Any]   	=hf_hub_url(self.repo_info.id      ,  _lowerCamelCase      ,  revision=self.repo_info.sha   )
      return fsspec.open(
          _lowerCamelCase      ,  mode=_lowerCamelCase      ,  headers=get_authentication_headers_for_url(_lowerCamelCase      ,  use_auth_token=self.token   )      ,  client_kwargs={'trust_env': True}      ,  ).open()



     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :Tuple      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					Tuple   	=self._strip_protocol(_lowerCamelCase   )
      if path in self.dir_cache:
       return self.dir_cache[path]
      else:
       raise FileNotFoundError(_lowerCamelCase   )




     def  lowerCamelCase_						(							self :Optional[Any]      ,  _lowerCamelCase :List[str]      ,  _lowerCamelCase :List[Any]=False      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      self._get_dirs()
      UpperCamelCase_     :					str   	=PurePosixPath(path.strip('/'   )   )
      UpperCamelCase_     :					List[str]   	={}
      for p, f in self.dir_cache.items():
       UpperCamelCase_     :					List[Any]   	=PurePosixPath(p.strip('/'   )   )
       UpperCamelCase_     :					Tuple   	=p.parent
       if root == path:
        UpperCamelCase_     :					int   	=f
      UpperCamelCase_     :					Optional[int]   	=list(paths.values()   )
      if detail:
       return out
      else:
       return sorted(f['name'] for f in out   )
