"""simple docstring"""




import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     _a      :		Any           =   'pt'
elif is_tf_available():
     _a      :		List[Any]           =   'tf'
else:
     _a      :		str           =   'jax'
class 						__A							( SCREAMING_SNAKE_CASE_    ,	unittest.TestCase		):
     _UpperCamelCase							:			List[Any]							=		PerceiverTokenizer
     _UpperCamelCase							:			Optional[int]							=		False






     def     __A  (		self  ):
       super().setUp()
       _lowerCAmelCase						:       Union[str, Any]  			=     PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname  )






     @cached_property
     def     __A  (		self  ):
       return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""  )






     def     __A  (		self				,     **a__  ):
       return self.tokenizer_class.from_pretrained(self.tmpdirname				,     **a__  )






     def     __A  (		self				,     a__				,     a__=False				,     a__=20				,     a__=5  ):
       # XXX The default common tokenizer tests assume that every ID is decodable on its own.
       # This assumption is invalid for Perceiver because single bytes might not be
       # valid utf-8 (byte 128 for instance).
       # Here we're overriding the smallest possible method to provide
       # a clean sequence without making the same assumption.

       _lowerCAmelCase						:       List[str]  			=     []
       for i in range(len(a__  )  ):
         try:
           _lowerCAmelCase						:       int  			=     tokenizer.decode([i]				,     clean_up_tokenization_spaces=a__  )
         except UnicodeDecodeError:
           pass
         toks.append((i, tok)  )

       _lowerCAmelCase						:       int  			=     list(filter(lambda a__  : re.match(r"""^[ a-zA-Z]+$"""				,     t[1]  )				,     a__  )  )
       _lowerCAmelCase						:       Union[str, Any]  			=     list(filter(lambda a__  : [t[0]] == tokenizer.encode(t[1]				,     add_special_tokens=a__  )				,     a__  )  )
       if max_length is not None and len(a__  ) > max_length:
         _lowerCAmelCase						:       Optional[int]  			=     toks[:max_length]
       if min_length is not None and len(a__  ) < min_length and len(a__  ) > 0:
         while len(a__  ) < min_length:
           _lowerCAmelCase						:       Dict  			=     toks + toks
        # toks_str = [t[1] for t in toks]
       _lowerCAmelCase						:       Union[str, Any]  			=     [t[0] for t in toks]

       # Ensure consistency
       _lowerCAmelCase						:       Tuple  			=     tokenizer.decode(a__				,     clean_up_tokenization_spaces=a__  )
       if " " not in output_txt and len(a__  ) > 1:
         _lowerCAmelCase						:       Dict  			=     (
             tokenizer.decode([toks_ids[0]]				,     clean_up_tokenization_spaces=a__  )
             + """ """
             + tokenizer.decode(toks_ids[1:]				,     clean_up_tokenization_spaces=a__  )
         )
       if with_prefix_space:
         _lowerCAmelCase						:       Tuple  			=     """ """ + output_txt
       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
       return output_txt, output_ids






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.perceiver_tokenizer
       _lowerCAmelCase						:       Optional[int]  			=     """Unicode €."""
       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer(a__  )
       _lowerCAmelCase						:       List[Any]  			=     [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
       self.assertEqual(encoded["""input_ids"""]				,     a__  )

       # decoding
       _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.decode(a__  )
       self.assertEqual(a__				,     """[CLS]Unicode €.[SEP]"""  )

       _lowerCAmelCase						:       Dict  			=     tokenizer("""e è é ê ë"""  )
       _lowerCAmelCase						:       int  			=     [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
       self.assertEqual(encoded["""input_ids"""]				,     a__  )
       # decoding
       _lowerCAmelCase						:       List[str]  			=     tokenizer.decode(a__  )
       self.assertEqual(a__				,     """[CLS]e è é ê ë[SEP]"""  )

       # encode/decode, but with `encode` instead of `__call__`
       self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""  )  )				,     """[CLS]e è é ê ë[SEP]"""  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       str  			=     self.perceiver_tokenizer
       _lowerCAmelCase						:       Any  			=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       # fmt: off
       _lowerCAmelCase						:       Any  			=     [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
       # fmt: on
       _lowerCAmelCase						:       List[str]  			=     tokenizer(a__				,     padding=a__				,     return_tensors=a__  )
       self.assertIsInstance(a__				,     a__  )

       if FRAMEWORK != "jax":
         _lowerCAmelCase						:       str  			=     list(batch.input_ids.numpy()[0]  )
       else:
         _lowerCAmelCase						:       List[Any]  			=     list(batch.input_ids.tolist()[0]  )

       self.assertListEqual(a__				,     a__  )

       self.assertEqual((2, 38)				,     batch.input_ids.shape  )
       self.assertEqual((2, 38)				,     batch.attention_mask.shape  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[Any]  			=     self.perceiver_tokenizer
       _lowerCAmelCase						:       str  			=     ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       _lowerCAmelCase						:       Optional[Any]  			=     tokenizer(a__				,     padding=a__				,     return_tensors=a__  )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn("""input_ids"""				,     a__  )
       self.assertIn("""attention_mask"""				,     a__  )
       self.assertNotIn("""decoder_input_ids"""				,     a__  )
       self.assertNotIn("""decoder_attention_mask"""				,     a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     self.perceiver_tokenizer
       _lowerCAmelCase						:       int  			=     [
           """Summary of the text.""",
           """Another summary.""",
       ]
       _lowerCAmelCase						:       Optional[int]  			=     tokenizer(
           text_target=a__				,     max_length=32				,     padding="""max_length"""				,     truncation=a__				,     return_tensors=a__  )
       self.assertEqual(32				,     targets["""input_ids"""].shape[1]  )






     def     __A  (		self  ):
       # safety check on max_len default value so we are sure the test works
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           self.assertNotEqual(tokenizer.model_max_length				,     42  )

        # Now let's start the test
       _lowerCAmelCase						:       Union[str, Any]  			=     self.get_tokenizers()
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           # Isolate this from the other tests because we save additional tokens/etc
           _lowerCAmelCase						:       Optional[int]  			=     tempfile.mkdtemp()

           _lowerCAmelCase						:       Dict  			=     """ He is very happy, UNwant\u00E9d,running"""
           _lowerCAmelCase						:       Optional[int]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
           tokenizer.save_pretrained(a__  )

           _lowerCAmelCase						:       Dict  			=     tokenizer.__class__.from_pretrained(a__  )
           _lowerCAmelCase						:       Optional[int]  			=     after_tokenizer.encode(a__				,     add_special_tokens=a__  )
           self.assertListEqual(a__				,     a__  )

           shutil.rmtree(a__  )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizers(model_max_length=42  )
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           # Isolate this from the other tests because we save additional tokens/etc
           _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()

           _lowerCAmelCase						:       List[str]  			=     """ He is very happy, UNwant\u00E9d,running"""
           tokenizer.add_tokens(["""bim""", """bambam"""]  )
           _lowerCAmelCase						:       Optional[Any]  			=     tokenizer.additional_special_tokens
           additional_special_tokens.append("""new_additional_special_token"""  )
           tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}  )
           _lowerCAmelCase						:       Optional[Any]  			=     tokenizer.encode(a__				,     add_special_tokens=a__  )
           tokenizer.save_pretrained(a__  )

           _lowerCAmelCase						:       Tuple  			=     tokenizer.__class__.from_pretrained(a__  )
           _lowerCAmelCase						:       Optional[int]  			=     after_tokenizer.encode(a__				,     add_special_tokens=a__  )
           self.assertListEqual(a__				,     a__  )
           self.assertIn("""new_additional_special_token"""				,     after_tokenizer.additional_special_tokens  )
           self.assertEqual(after_tokenizer.model_max_length				,     42  )

           _lowerCAmelCase						:       Dict  			=     tokenizer.__class__.from_pretrained(a__				,     model_max_length=43  )
           self.assertEqual(tokenizer.model_max_length				,     43  )

           shutil.rmtree(a__  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Dict  			=     []
       if self.test_slow_tokenizer:
         tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())  )

       if self.test_rust_tokenizer:
         tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())  )

       for tokenizer_class, tokenizer_utils in tokenizer_list:
         with tempfile.TemporaryDirectory() as tmp_dir:
           tokenizer_utils.save_pretrained(a__  )

           with open(os.path.join(a__				,     """special_tokens_map.json"""  )				,     encoding="""utf-8"""  ) as json_file:
             _lowerCAmelCase						:       Any  			=     json.load(a__  )

           with open(os.path.join(a__				,     """tokenizer_config.json"""  )				,     encoding="""utf-8"""  ) as json_file:
             _lowerCAmelCase						:       Union[str, Any]  			=     json.load(a__  )

           _lowerCAmelCase						:       int  			=     [F"<extra_id_{i}>" for i in range(125  )]

           _lowerCAmelCase						:       int  			=     added_tokens_extra_ids + [
               """an_additional_special_token"""
           ]
           _lowerCAmelCase						:       Any  			=     added_tokens_extra_ids + [
               """an_additional_special_token"""
           ]

           with open(os.path.join(a__				,     """special_tokens_map.json"""  )				,     """w"""				,     encoding="""utf-8"""  ) as outfile:
             json.dump(a__				,     a__  )
           with open(os.path.join(a__				,     """tokenizer_config.json"""  )				,     """w"""				,     encoding="""utf-8"""  ) as outfile:
             json.dump(a__				,     a__  )

           # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
           # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
           # "special_tokens_map.json" files
           _lowerCAmelCase						:       List[str]  			=     tokenizer_class.from_pretrained(
               a__				,     )
           self.assertIn(
               """an_additional_special_token"""				,     tokenizer_without_change_in_init.additional_special_tokens  )
           self.assertEqual(
               ["""an_additional_special_token"""]				,     tokenizer_without_change_in_init.convert_ids_to_tokens(
                   tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]  )  )				,     )

           # Now we test that we can change the value of additional_special_tokens in the from_pretrained
           _lowerCAmelCase						:       List[Any]  			=     added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""				,     lstrip=a__  )]
           _lowerCAmelCase						:       List[Any]  			=     tokenizer_class.from_pretrained(
               a__				,     additional_special_tokens=a__				,     )

           self.assertIn("""a_new_additional_special_token"""				,     tokenizer.additional_special_tokens  )
           self.assertEqual(
               ["""a_new_additional_special_token"""]				,     tokenizer.convert_ids_to_tokens(
                   tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]  )  )				,     )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([178]  )				,     """�"""  )






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       pass






     def     __A  (		self  ):
       # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
       # strings and special added tokens as tokens
       _lowerCAmelCase						:       int  			=     self.get_tokenizers(fast=a__				,     do_lower_case=a__  )
       for tokenizer in tokenizers:
         with self.subTest(F"{tokenizer.__class__.__name__}"  ):
           _lowerCAmelCase						:       List[str]  			=     ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
           _lowerCAmelCase						:       Union[str, Any]  			=     tokenizer.convert_tokens_to_string(a__  )

           self.assertIsInstance(a__				,     a__  )





"""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							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Union[str, Any]							=		""
     _UpperCamelCase							:			str							=		"hf-legacy"  # "hf://"" is reserved for hffs






     def __init__(		self				,     a__ = None				,     a__ = None				,     **a__				,     ):
       super().__init__(self				,     **a__  )
       _lowerCAmelCase						:       Any  			=     repo_info
       _lowerCAmelCase						:       Optional[Any]  			=     token
       _lowerCAmelCase						:       Optional[int]  			=     None






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






     def     __A  (		self				,     a__				,     a__ = "rb"				,     **a__				,     ):
       if not isinstance(self.repo_info				,     a__  ):
         raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"  )
       _lowerCAmelCase						:       Tuple  			=     hf_hub_url(self.repo_info.id				,     a__				,     revision=self.repo_info.sha  )
       return fsspec.open(
           a__				,     mode=a__				,     headers=get_authentication_headers_for_url(a__				,     use_auth_token=self.token  )				,     client_kwargs={"""trust_env""": True}				,     ).open()






     def     __A  (		self				,     a__				,     **a__  ):
       self._get_dirs()
       _lowerCAmelCase						:       Union[str, Any]  			=     self._strip_protocol(a__  )
       if path in self.dir_cache:
         return self.dir_cache[path]
       else:
         raise FileNotFoundError(a__  )






     def     __A  (		self				,     a__				,     a__=False				,     **a__  ):
       self._get_dirs()
       _lowerCAmelCase						:       Any  			=     PurePosixPath(path.strip("""/"""  )  )
       _lowerCAmelCase						:       List[str]  			=     {}
       for p, f in self.dir_cache.items():
         _lowerCAmelCase						:       Any  			=     PurePosixPath(p.strip("""/"""  )  )
         _lowerCAmelCase						:       Optional[int]  			=     p.parent
         if root == path:
           _lowerCAmelCase						:       Dict  			=     f
       _lowerCAmelCase						:       Union[str, Any]  			=     list(paths.values()  )
       if detail:
         return out
       else:
         return sorted(f["""name"""] for f in out  )




