

import json
import os
import unittest

from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led 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






@require_tokenizers
class 	A_		(	SCREAMING_SNAKE_CASE      ,							unittest.TestCase    ):
							_UpperCAmelCase   :  int										=				LEDTokenizer
							_UpperCAmelCase   :  Dict										=				LEDTokenizerFast
							_UpperCAmelCase   :  Any										=				True
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								super().setUp()
								__lowerCamelCase    :    int 	=			[
								    'l',
								    'o',
								    'w',
								    'e',
								    'r',
								    's',
								    't',
								    'i',
								    'd',
								    'n',
								    '\u0120',
								    '\u0120l',
								    '\u0120n',
								    '\u0120lo',
								    '\u0120low',
								    'er',
								    '\u0120lowest',
								    '\u0120newer',
								    '\u0120wider',
								    '<unk>',
								]
								__lowerCamelCase    :    Optional[Any] 	=			dict(zip(SCREAMING_SNAKE_CASE__       ,range(len(SCREAMING_SNAKE_CASE__))))
								__lowerCamelCase    :    Any 	=			['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
								__lowerCamelCase    :    Optional[int] 	=			{'unk_token': '<unk>'}

								__lowerCamelCase    :    Tuple 	=			os.path.join(self.tmpdirname       ,VOCAB_FILES_NAMES['vocab_file'])
								__lowerCamelCase    :    List[str] 	=			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(SCREAMING_SNAKE_CASE__) + '\n')
								with open(self.merges_file       ,'w'       ,encoding='utf-8') as fp:
									fp.write('\n'.join(SCREAMING_SNAKE_CASE__))
							def    lowerCAmelCase					(		self		:      Any       ,**SCREAMING_SNAKE_CASE__		:      str):
								kwargs.update(self.special_tokens_map)
								return self.tokenizer_class.from_pretrained(self.tmpdirname       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      List[Any]       ,**SCREAMING_SNAKE_CASE__		:      Dict):
								kwargs.update(self.special_tokens_map)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,**SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[int]       ,SCREAMING_SNAKE_CASE__		:      Any):
								return "lower newer", "lower newer"
							@cached_property
							def    lowerCAmelCase					(		self		:      Any):
								return LEDTokenizer.from_pretrained('allenai/led-base-16384')
							@cached_property
							def    lowerCAmelCase					(		self		:      str):
								return LEDTokenizerFast.from_pretrained('allenai/led-base-16384')
							@require_torch
							def    lowerCAmelCase					(		self		:      Tuple):
								__lowerCamelCase    :    str 	=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
								__lowerCamelCase    :    Union[str, Any] 	=			[0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

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

									self.assertEqual((2, 9)       ,batch.input_ids.shape)
									self.assertEqual((2, 9)       ,batch.attention_mask.shape)
									__lowerCamelCase    :    List[Any] 	=			batch.input_ids.tolist()[0]
									self.assertListEqual(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
							@require_torch
							def    lowerCAmelCase					(		self		:      Dict):
								__lowerCamelCase    :    str 	=			['A long paragraph for summarization.', 'Another paragraph for summarization.']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__lowerCamelCase    :    Optional[int] 	=			tokenizer(SCREAMING_SNAKE_CASE__       ,padding=SCREAMING_SNAKE_CASE__       ,return_tensors='pt')
									self.assertIn('input_ids'       ,SCREAMING_SNAKE_CASE__)
									self.assertIn('attention_mask'       ,SCREAMING_SNAKE_CASE__)
									self.assertNotIn('labels'       ,SCREAMING_SNAKE_CASE__)
									self.assertNotIn('decoder_attention_mask'       ,SCREAMING_SNAKE_CASE__)
							@require_torch
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								__lowerCamelCase    :    List[Any] 	=			[
								    'Summary of the text.',
								    'Another summary.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__lowerCamelCase    :    Tuple 	=			tokenizer(text_target=SCREAMING_SNAKE_CASE__       ,max_length=3_2       ,padding='max_length'       ,return_tensors='pt')
									self.assertEqual(3_2       ,targets['input_ids'].shape[1])
							@require_torch
							def    lowerCAmelCase					(		self		:      Optional[int]):
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(
									    ['I am a small frog' * 1_0_2_4, 'I am a small frog']       ,padding=SCREAMING_SNAKE_CASE__       ,truncation=SCREAMING_SNAKE_CASE__       ,return_tensors='pt')
									self.assertIsInstance(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__)
									self.assertEqual(batch.input_ids.shape       ,(2, 5_1_2_2))
							@require_torch
							def    lowerCAmelCase					(		self		:      int):
								__lowerCamelCase    :    int 	=			['A long paragraph for summarization.']
								__lowerCamelCase    :    Any 	=			[
								    'Summary of the text.',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__lowerCamelCase    :    Union[str, Any] 	=			tokenizer(SCREAMING_SNAKE_CASE__       ,return_tensors='pt')
									__lowerCamelCase    :    int 	=			tokenizer(text_target=SCREAMING_SNAKE_CASE__       ,return_tensors='pt')
									__lowerCamelCase    :    Union[str, Any] 	=			inputs['input_ids']
									__lowerCamelCase    :    Union[str, Any] 	=			targets['input_ids']
									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())
							@require_torch
							def    lowerCAmelCase					(		self		:      Union[str, Any]):
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									__lowerCamelCase    :    Tuple 	=			['Summary of the text.', 'Another summary.']
									__lowerCamelCase    :    Any 	=			[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

									__lowerCamelCase    :    Dict 	=			tokenizer(SCREAMING_SNAKE_CASE__       ,padding=SCREAMING_SNAKE_CASE__)
									__lowerCamelCase    :    Any 	=			[[0] * len(SCREAMING_SNAKE_CASE__) for x in encoded_output['input_ids']]
									__lowerCamelCase    :    str 	=			tokenizer.pad(SCREAMING_SNAKE_CASE__)
									self.assertSequenceEqual(outputs['global_attention_mask']       ,SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Any):
								pass


							def    lowerCAmelCase					(		self		:      Dict):
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
									with self.subTest(F"{tokenizer.__class__.__name__} ({pretrained_name})"):
										__lowerCamelCase    :    Optional[int] 	=			self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    int 	=			self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__       ,**SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Union[str, Any] 	=			'A, <mask> AllenNLP sentence.'
										__lowerCamelCase    :    List[Any] 	=			tokenizer_r.encode_plus(SCREAMING_SNAKE_CASE__       ,add_special_tokens=SCREAMING_SNAKE_CASE__       ,return_token_type_ids=SCREAMING_SNAKE_CASE__)
										__lowerCamelCase    :    Optional[int] 	=			tokenizer_p.encode_plus(SCREAMING_SNAKE_CASE__       ,add_special_tokens=SCREAMING_SNAKE_CASE__       ,return_token_type_ids=SCREAMING_SNAKE_CASE__)
										self.assertEqual(sum(tokens_r['token_type_ids'])       ,sum(tokens_p['token_type_ids']))
										self.assertEqual(
										    sum(tokens_r['attention_mask']) / len(tokens_r['attention_mask'])       ,sum(tokens_p['attention_mask']) / len(tokens_p['attention_mask'])       ,)

										__lowerCamelCase    :    List[Any] 	=			tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
										__lowerCamelCase    :    str 	=			tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'])
										self.assertSequenceEqual(tokens_p['input_ids']       ,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
										self.assertSequenceEqual(tokens_r['input_ids']       ,[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

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







import os
import unittest

from huggingface_hub.utils import are_progress_bars_disabled

import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar






class 	A_		(	unittest.TestCase    ):
							def    lowerCAmelCase					(		self		:      Optional[int]):
								__lowerCamelCase    :    List[Any] 	=			logging.get_logger()

								# the current default level is logging.WARNING
								__lowerCamelCase    :    Dict 	=			logging.get_verbosity()

								logging.set_verbosity_error()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_warning()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_info()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								logging.set_verbosity_debug()
								self.assertEqual(logger.getEffectiveLevel()       ,logging.get_verbosity())

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							def    lowerCAmelCase					(		self		:      Optional[Any]):
								__lowerCamelCase    :    Optional[int] 	=			logging.get_verbosity()

								__lowerCamelCase    :    str 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Tuple 	=			'Testing 1, 2, 3'

								# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
								if level_origin <= logging.WARNING:
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')

								# this is setting the level for all of `transformers.*` loggers
								logging.set_verbosity_error()

								# should not be able to log warnings
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,'')

								# should be able to log warnings again
								logging.set_verbosity_warning()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									logger.warning(SCREAMING_SNAKE_CASE__)
								self.assertEqual(cl.out       ,msg + '\n')

								# restore to the original level
								logging.set_verbosity(SCREAMING_SNAKE_CASE__)
							@mockenv(TRANSFORMERS_VERBOSITY='error')
							def    lowerCAmelCase					(		self		:      Tuple):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								# this action activates the env var
								__lowerCamelCase    :    int 	=			logging.get_logger('transformers.models.bart.tokenization_bart')

								__lowerCamelCase    :    int 	=			os.getenv('TRANSFORMERS_VERBOSITY'       ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase    :    Optional[Any] 	=			logging.log_levels[env_level_str]

								__lowerCamelCase    :    Tuple 	=			logging.get_verbosity()
								self.assertEqual(
								    SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,F"TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}"       ,)

								# restore to the original level
								__lowerCamelCase    :    List[str] 	=			''
								transformers.utils.logging._reset_library_root_logger()
							@mockenv(TRANSFORMERS_VERBOSITY='super-error')
							def    lowerCAmelCase					(		self		:      List[Any]):
								# reset for the env var to take effect, next time some logger call is made
								transformers.utils.logging._reset_library_root_logger()
								__lowerCamelCase    :    List[str] 	=			logging.logging.getLogger()
								with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
									# this action activates the env var
									logging.get_logger('transformers.models.bart.tokenization_bart')
								self.assertIn('Unknown option TRANSFORMERS_VERBOSITY=super-error'       ,cl.out)

								# no need to restore as nothing was changed


							def    lowerCAmelCase					(		self		:      Any):
								# testing `logger.warning_advice()`
								transformers.utils.logging._reset_library_root_logger()

								__lowerCamelCase    :    Tuple 	=			logging.get_logger('transformers.models.bart.tokenization_bart')
								__lowerCamelCase    :    Optional[int] 	=			'Testing 1, 2, 3'

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS='1'):
									# nothing should be logged as env var disables this method
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,'')

								with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS=''):
									# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
									with CaptureLogger(SCREAMING_SNAKE_CASE__) as cl:
										logger.warning_advice(SCREAMING_SNAKE_CASE__)
									self.assertEqual(cl.out       ,msg + '\n')






def        SCREAMING_SNAKE_CASE__	(  )  ->					Any:
	disable_progress_bar()
	assert are_progress_bars_disabled()

	enable_progress_bar()
	assert not are_progress_bars_disabled()




