




"""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_				:    Dict										=     '''pt'''
elif is_tf_available():
					a_				:    Optional[int]										=     '''tf'''
else:
					a_				:    Any										=     '''jax'''

class __lowercase(      lowercase__	,	unittest.TestCase				):




					'''simple docstring'''


					__a						:						int     		= PerceiverTokenizer
					__a						:						List[Any]     		= False
					def     snake_case_				(    self     ):
								super().setUp()
								__lowerCamelCase					:	Union[str, Any]	       =				PerceiverTokenizer()
								tokenizer.save_pretrained(self.tmpdirname     )
					@cached_property
					def     snake_case_				(    self     ):
								return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'     )
					def     snake_case_				(    self						,	**__a     ):
								return self.tokenizer_class.from_pretrained(self.tmpdirname						,	**__a     )
					def     snake_case_				(    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					:	Optional[Any]	       =				[]
								for i in range(len(__a     )     ):
											try:
														__lowerCamelCase					:	Tuple	       =				tokenizer.decode([i]						,	clean_up_tokenization_spaces=__a     )
											except UnicodeDecodeError:
														pass
											toks.append((i, tok)     )

								__lowerCamelCase					:	Optional[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					:	Tuple	       =				toks[:max_length]
								if min_length is not None and len(__a     ) < min_length and len(__a     ) > 0:
											while len(__a     ) < min_length:
														__lowerCamelCase					:	str	       =				toks + toks
        # toks_str = [t[1] for t in toks]
								__lowerCamelCase					:	int	       =				[t[0] for t in toks]

								# Ensure consistency
								__lowerCamelCase					:	Optional[Any]	       =				tokenizer.decode(__a						,	clean_up_tokenization_spaces=__a     )
								if " " not in output_txt and len(__a     ) > 1:
											__lowerCamelCase					:	str	       =				(
											    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					:	List[Any]	       =				' ' + output_txt
								__lowerCamelCase					:	Tuple	       =				tokenizer.encode(__a						,	add_special_tokens=__a     )
								return output_txt, output_ids
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	List[Any]	       =				self.perceiver_tokenizer
								__lowerCamelCase					:	Dict	       =				'Unicode €.'
								__lowerCamelCase					:	List[str]	       =				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					:	Dict	       =				tokenizer.decode(__a     )
								self.assertEqual(__a						,	'[CLS]Unicode €.[SEP]'     )

								__lowerCamelCase					:	List[Any]	       =				tokenizer('e è é ê ë'     )
								__lowerCamelCase					:	List[str]	       =				[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
								self.assertEqual(encoded['input_ids']						,	__a     )
								# decoding
								__lowerCamelCase					:	Any	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				self.perceiver_tokenizer
								__lowerCamelCase					:	List[str]	       =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
								# fmt: off
								__lowerCamelCase					:	str	       =				[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[Any]	       =				tokenizer(__a						,	padding=__a						,	return_tensors=__a     )
								self.assertIsInstance(__a						,	__a     )

								if FRAMEWORK != "jax":
											__lowerCamelCase					:	Dict	       =				list(batch.input_ids.numpy()[0]     )
								else:
											__lowerCamelCase					:	Dict	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	str	       =				self.perceiver_tokenizer
								__lowerCamelCase					:	List[str]	       =				['A long paragraph for summarization.', 'Another paragraph for summarization.']
								__lowerCamelCase					:	Optional[int]	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				self.perceiver_tokenizer
								__lowerCamelCase					:	int	       =				[
								    'Summary of the text.',
								    'Another summary.',
								]
								__lowerCamelCase					:	Any	       =				tokenizer(
								    text_target=__a						,	max_length=32						,	padding='max_length'						,	truncation=__a						,	return_tensors=__a     )
								self.assertEqual(32						,	targets['input_ids'].shape[1]     )
					def     snake_case_				(    self     ):
								# safety check on max_len default value so we are sure the test works
								__lowerCamelCase					:	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					:	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					:	str	       =				tempfile.mkdtemp()

														__lowerCamelCase					:	str	       =				' He is very happy, UNwant\u00E9d,running'
														__lowerCamelCase					:	Optional[int]	       =				tokenizer.encode(__a						,	add_special_tokens=__a     )
														tokenizer.save_pretrained(__a     )

														__lowerCamelCase					:	int	       =				tokenizer.__class__.from_pretrained(__a     )
														__lowerCamelCase					:	List[Any]	       =				after_tokenizer.encode(__a						,	add_special_tokens=__a     )
														self.assertListEqual(__a						,	__a     )

														shutil.rmtree(__a     )

								__lowerCamelCase					:	Dict	       =				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					:	Tuple	       =				tempfile.mkdtemp()

														__lowerCamelCase					:	Dict	       =				' He is very happy, UNwant\u00E9d,running'
														tokenizer.add_tokens(['bim', 'bambam']     )
														__lowerCamelCase					:	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}     )
														__lowerCamelCase					:	Any	       =				tokenizer.encode(__a						,	add_special_tokens=__a     )
														tokenizer.save_pretrained(__a     )

														__lowerCamelCase					:	Tuple	       =				tokenizer.__class__.from_pretrained(__a     )
														__lowerCamelCase					:	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					:	Tuple	       =				tokenizer.__class__.from_pretrained(__a						,	model_max_length=43     )
														self.assertEqual(tokenizer.model_max_length						,	43     )

														shutil.rmtree(__a     )
					def     snake_case_				(    self     ):
								__lowerCamelCase					:	int	       =				[]
								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					:	str	       =				json.load(__a     )

														with open(os.path.join(__a						,	'tokenizer_config.json'     )						,	encoding='utf-8'     ) as json_file:
																	__lowerCamelCase					:	Dict	       =				json.load(__a     )

														__lowerCamelCase					:	Tuple	       =				[f'''<extra_id_{i}>''' for i in range(125     )]

														__lowerCamelCase					:	Union[str, Any]	       =				added_tokens_extra_ids + [
														    'an_additional_special_token'
														]
														__lowerCamelCase					:	Union[str, 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					:	Optional[Any]	       =				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					:	Any	       =				added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'						,	lstrip=__a     )]
														__lowerCamelCase					:	List[str]	       =				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     snake_case_				(    self     ):
								__lowerCamelCase					:	Optional[int]	       =				self.perceiver_tokenizer
								self.assertEqual(tokenizer.decode([178]     )						,	'�'     )
					def     snake_case_				(    self     ):
								pass
					def     snake_case_				(    self     ):
								pass
					def     snake_case_				(    self     ):
								pass
					def     snake_case_				(    self     ):
								pass





					def     snake_case_				(    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					:	Dict	       =				self.get_tokenizers(fast=__a						,	do_lower_case=__a     )
								for tokenizer in tokenizers:
											with self.subTest(f'''{tokenizer.__class__.__name__}'''     ):
														__lowerCamelCase					:	Any	       =				['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
														__lowerCamelCase					:	List[Any]	       =				tokenizer.convert_tokens_to_string(__a     )

														self.assertIsInstance(__a						,	__a     )









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


a_				:    List[Any]										=     {
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					a_				:    int										=     [
					    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''EncodecModel''',
					    '''EncodecPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_encodec import (
					    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EncodecConfig,
					)
					from .feature_extraction_encodec import EncodecFeatureExtractor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_encodec import (
										    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EncodecModel,
										    EncodecPreTrainedModel,
										)

else:
					import sys

					a_				:    Tuple										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



