


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():
			_snake_case  :	List[Any]			      =       "pt"
elif is_tf_available():
			_snake_case  :	Dict			      =       "tf"
else:
			_snake_case  :	Tuple			      =       "jax"

class        a	(_lowerCAmelCase  ,       unittest.TestCase   ):




							"""simple docstring"""


							__UpperCAmelCase					:							List[Any]  						=						PerceiverTokenizer
							__UpperCAmelCase					:							List[Any]  						=						False

							def 					__snake_case      (							self :						str       )			->					Dict:
														super().setUp()
														__snake_case	:    str											=     PerceiverTokenizer()
														tokenizer.save_pretrained(self.tmpdirname       )

							@cached_property
							def 					__snake_case      (							self :						Optional[Any]       )			->					List[str]:
														return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver"       )

							def 					__snake_case      (							self :						Optional[Any]					,			**lowerCamelCase :						List[str]       )			->					PerceiverTokenizer:
														return self.tokenizer_class.from_pretrained(self.tmpdirname					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						List[Any]=False					,			lowerCamelCase :						Tuple=20					,			lowerCamelCase :						List[str]=5       )			->					Tuple[str, list]:
														# 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.

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

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

														# Ensure consistency
														__snake_case	:    List[Any]											=     tokenizer.decode(lowerCamelCase					,			clean_up_tokenization_spaces=lowerCamelCase       )
														if " " not in output_txt and len(lowerCamelCase       ) > 1:
																					__snake_case	:    Optional[Any]											=     (
																					    tokenizer.decode([toks_ids[0]]					,			clean_up_tokenization_spaces=lowerCamelCase       )
																					    + " "
																					    + tokenizer.decode(toks_ids[1:]					,			clean_up_tokenization_spaces=lowerCamelCase       )
																					)
														if with_prefix_space:
																					__snake_case	:    str											=     " " + output_txt
														__snake_case	:    int											=     tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
														return output_txt, output_ids

							def 					__snake_case      (							self :						Any       )			->					List[Any]:
														__snake_case	:    List[str]											=     self.perceiver_tokenizer
														__snake_case	:    Any											=     "Unicode €."
														__snake_case	:    Any											=     tokenizer(lowerCamelCase       )
														__snake_case	:    Any											=     [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
														self.assertEqual(encoded["input_ids"]					,			lowerCamelCase       )

														# decoding
														__snake_case	:    int											=     tokenizer.decode(lowerCamelCase       )
														self.assertEqual(lowerCamelCase					,			"[CLS]Unicode €.[SEP]"       )

														__snake_case	:    List[str]											=     tokenizer("e è é ê ë"       )
														__snake_case	:    Union[str, Any]											=     [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
														self.assertEqual(encoded["input_ids"]					,			lowerCamelCase       )
														# decoding
														__snake_case	:    List[Any]											=     tokenizer.decode(lowerCamelCase       )
														self.assertEqual(lowerCamelCase					,			"[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 					__snake_case      (							self :						Tuple       )			->					Dict:
														__snake_case	:    Tuple											=     self.perceiver_tokenizer
														__snake_case	:    Tuple											=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
														# fmt: off
														__snake_case	:    int											=     [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
														__snake_case	:    Optional[Any]											=     tokenizer(lowerCamelCase					,			padding=lowerCamelCase					,			return_tensors=lowerCamelCase       )
														self.assertIsInstance(lowerCamelCase					,			lowerCamelCase       )

														if FRAMEWORK != "jax":
																					__snake_case	:    Optional[Any]											=     list(batch.input_ids.numpy()[0]       )
														else:
																					__snake_case	:    Tuple											=     list(batch.input_ids.tolist()[0]       )

														self.assertListEqual(lowerCamelCase					,			lowerCamelCase       )

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

							def 					__snake_case      (							self :						int       )			->					Union[str, Any]:
														__snake_case	:    Union[str, Any]											=     self.perceiver_tokenizer
														__snake_case	:    List[Any]											=     ["A long paragraph for summarization.", "Another paragraph for summarization."]
														__snake_case	:    Any											=     tokenizer(lowerCamelCase					,			padding=lowerCamelCase					,			return_tensors=lowerCamelCase       )
														# check if input_ids are returned and no decoder_input_ids
														self.assertIn("input_ids"					,			lowerCamelCase       )
														self.assertIn("attention_mask"					,			lowerCamelCase       )
														self.assertNotIn("decoder_input_ids"					,			lowerCamelCase       )
														self.assertNotIn("decoder_attention_mask"					,			lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[Any]       )			->					Tuple:
														__snake_case	:    Optional[int]											=     self.perceiver_tokenizer
														__snake_case	:    Tuple											=     [
														    "Summary of the text.",
														    "Another summary.",
														]
														__snake_case	:    Tuple											=     tokenizer(
														    text_target=lowerCamelCase					,			max_length=32					,			padding="max_length"					,			truncation=lowerCamelCase					,			return_tensors=lowerCamelCase       )
														self.assertEqual(32					,			targets["input_ids"].shape[1]       )

							def 					__snake_case      (							self :						List[str]       )			->					List[Any]:
														# safety check on max_len default value so we are sure the test works
														__snake_case	:    Optional[int]											=     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
														__snake_case	:    List[str]											=     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
																												__snake_case	:    int											=     tempfile.mkdtemp()

																												__snake_case	:    List[Any]											=     " He is very happy, UNwant\u00E9d,running"
																												__snake_case	:    List[str]											=     tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
																												tokenizer.save_pretrained(lowerCamelCase       )

																												__snake_case	:    Tuple											=     tokenizer.__class__.from_pretrained(lowerCamelCase       )
																												__snake_case	:    str											=     after_tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
																												self.assertListEqual(lowerCamelCase					,			lowerCamelCase       )

																												shutil.rmtree(lowerCamelCase       )

														__snake_case	:    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
																												__snake_case	:    Union[str, Any]											=     tempfile.mkdtemp()

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

																												__snake_case	:    Optional[Any]											=     tokenizer.__class__.from_pretrained(lowerCamelCase       )
																												__snake_case	:    List[str]											=     after_tokenizer.encode(lowerCamelCase					,			add_special_tokens=lowerCamelCase       )
																												self.assertListEqual(lowerCamelCase					,			lowerCamelCase       )
																												self.assertIn("new_additional_special_token"					,			after_tokenizer.additional_special_tokens       )
																												self.assertEqual(after_tokenizer.model_max_length					,			42       )

																												__snake_case	:    str											=     tokenizer.__class__.from_pretrained(lowerCamelCase					,			model_max_length=43       )
																												self.assertEqual(tokenizer.model_max_length					,			43       )

																												shutil.rmtree(lowerCamelCase       )

							def 					__snake_case      (							self :						Union[str, Any]       )			->					int:
														__snake_case	:    Optional[Any]											=     []
														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(lowerCamelCase       )

																												with open(os.path.join(lowerCamelCase					,			"special_tokens_map.json"       )					,			encoding="utf-8"       ) as json_file:
																																			__snake_case	:    str											=     json.load(lowerCamelCase       )

																												with open(os.path.join(lowerCamelCase					,			"tokenizer_config.json"       )					,			encoding="utf-8"       ) as json_file:
																																			__snake_case	:    Tuple											=     json.load(lowerCamelCase       )

																												__snake_case	:    str											=     [F'<extra_id_{i}>' for i in range(125       )]

																												__snake_case	:    List[Any]											=     added_tokens_extra_ids + [
																												    "an_additional_special_token"
																												]
																												__snake_case	:    int											=     added_tokens_extra_ids + [
																												    "an_additional_special_token"
																												]

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

																												# 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
																												__snake_case	:    Optional[int]											=     tokenizer_class.from_pretrained(
																												    lowerCamelCase					,			)
																												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
																												__snake_case	:    Tuple											=     added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"					,			lstrip=lowerCamelCase       )]
																												__snake_case	:    str											=     tokenizer_class.from_pretrained(
																												    lowerCamelCase					,			additional_special_tokens=lowerCamelCase					,			)

																												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 					__snake_case      (							self :						Tuple       )			->					List[Any]:
														__snake_case	:    List[Any]											=     self.perceiver_tokenizer
														self.assertEqual(tokenizer.decode([178]       )					,			"�"       )

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

							def 					__snake_case      (							self :						Optional[int]       )			->					Dict:
														pass

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

							def 					__snake_case      (							self :						int       )			->					int:
														pass






							def 					__snake_case      (							self :						int       )			->					Tuple:
														# The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
														# strings and special added tokens as tokens
														__snake_case	:    Optional[Any]											=     self.get_tokenizers(fast=lowerCamelCase					,			do_lower_case=lowerCamelCase       )
														for tokenizer in tokenizers:
																					with self.subTest(F'{tokenizer.__class__.__name__}'       ):
																												__snake_case	:    Optional[Any]											=     ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
																												__snake_case	:    Tuple											=     tokenizer.convert_tokens_to_string(lowerCamelCase       )

																												self.assertIsInstance(lowerCamelCase					,			lowerCamelCase       )





import logging
import os
from logging import (
    CRITICAL,  # NOQA
    DEBUG,  # NOQA
    ERROR,  # NOQA
    FATAL,  # NOQA
    INFO,  # NOQA
    NOTSET,  # NOQA
    WARN,  # NOQA
    WARNING,  # NOQA
)
from typing import Optional

from tqdm import auto as tqdm_lib


_snake_case  :	int			      =       {
    "debug": logging.DEBUG,
    "info": logging.INFO,
    "warning": logging.WARNING,
    "error": logging.ERROR,
    "critical": logging.CRITICAL,
}

_snake_case  :	Dict			      =       logging.WARNING







def        lowerCAmelCase_  (      ):
							__snake_case	:    Union[str, Any]											=     os.getenv("DATASETS_VERBOSITY"							,			__lowerCamelCase			)
							if env_level_str:
														if env_level_str in log_levels:
																					return log_levels[env_level_str]
														else:
																					logging.getLogger().warning(
																					    F'Unknown option DATASETS_VERBOSITY={env_level_str}, '
																					    F'has to be one of: { ", ".join(log_levels.keys()			) }'			)
							return _default_log_level







def        lowerCAmelCase_  (      ):
							return __name__.split("."			)[0]







def        lowerCAmelCase_  (      ):
							return logging.getLogger(_get_library_name()			)







def        lowerCAmelCase_  (      ):
							# Apply our default configuration to the library root logger.
							__snake_case	:    str											=     _get_library_root_logger()
							library_root_logger.setLevel(_get_default_logging_level()			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    Dict											=     _get_library_root_logger()
							library_root_logger.setLevel(logging.NOTSET			)







def        lowerCAmelCase_  (      __lowerCamelCase = None			):
							if name is None:
														__snake_case	:    Tuple											=     _get_library_name()
							return logging.getLogger(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return _get_library_root_logger().getEffectiveLevel()







def        lowerCAmelCase_  (      __lowerCamelCase			):
							_get_library_root_logger().setLevel(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							return set_verbosity(__lowerCamelCase			)







def        lowerCAmelCase_  (      ):
							__snake_case	:    List[str]											=     False







def        lowerCAmelCase_  (      ):
							__snake_case	:    Tuple											=     True


# Configure the library root logger at the module level (singleton-like)
_configure_library_root_logger()

class        a	:




							"""simple docstring"""


							def __init__(							self :						int					,			*lowerCamelCase :						Optional[Any]					,			**lowerCamelCase :						List[str]       )			->					Optional[int]:  # pylint: disable=unused-argument
														__snake_case	:    int											=     args[0] if args else None

							def __iter__(							self :						Dict       )			->					Optional[int]:
														return iter(self._iterator       )

							def __getattr__(							self :						int					,			lowerCamelCase :						Optional[Any]       )			->					List[Any]:

														def empty_fn(*lowerCamelCase :						Optional[int]					,			**lowerCamelCase :						List[Any]       ):  # pylint: disable=unused-argument
																					return

														return empty_fn

							def __enter__(							self :						List[str]       )			->					int:
														return self






							def __exit__(							self :						Dict					,			lowerCamelCase :						Any					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						str       )			->					Dict:
														return
_snake_case  :	Optional[Any]			      =       True

class        a	:




							"""simple docstring"""


							def __call__(							self :						str					,			*lowerCamelCase :						Optional[Any]					,			lowerCamelCase :						Tuple=False					,			**lowerCamelCase :						Union[str, Any]       )			->					Optional[Any]:
														if _tqdm_active and not disable:
																					return tqdm_lib.tqdm(*lowerCamelCase					,			**lowerCamelCase       )
														else:
																					return EmptyTqdm(*lowerCamelCase					,			**lowerCamelCase       )

							def 					__snake_case      (							self :						str					,			*lowerCamelCase :						str					,			**lowerCamelCase :						Tuple       )			->					str:
														__snake_case	:    Optional[Any]											=     None
														if _tqdm_active:
																					return tqdm_lib.tqdm.set_lock(*lowerCamelCase					,			**lowerCamelCase       )






							def 					__snake_case      (							self :						List[Any]       )			->					List[Any]:
														if _tqdm_active:
																					return tqdm_lib.tqdm.get_lock()
_snake_case  :	int			      =       _tqdm_cls()







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							return bool(_tqdm_active			)







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    Tuple											=     True







def        lowerCAmelCase_  (      ):
							global _tqdm_active
							__snake_case	:    List[Any]											=     False

