



'''simple docstring'''

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 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    Dict   				=					LEDTokenizer
							lowerCAmelCase       :    List[str]   				=					LEDTokenizerFast
							lowerCAmelCase       :    List[Any]   				=					True







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

									_a     :      Tuple        =							os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']		)
									_a     :      Optional[Any]        =							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(_UpperCAmelCase		) + '\n'		)
									with open(self.merges_file	,'w'	,encoding='utf-8'		) as fp:
											fp.write('\n'.join(_UpperCAmelCase		)		)







							def 							__lowercase			(							self					:       List[Any]	,**_UpperCAmelCase					:       List[Any]		):
									kwargs.update(self.special_tokens_map		)
									return self.tokenizer_class.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Dict	,**_UpperCAmelCase					:       str		):
									kwargs.update(self.special_tokens_map		)
									return self.rust_tokenizer_class.from_pretrained(self.tmpdirname	,**_UpperCAmelCase		)







							def 							__lowercase			(							self					:       Any	,_UpperCAmelCase					:       Any		):
									return "lower newer", "lower newer"







							@cached_property
							def 							__lowercase			(							self					:       str		):
									return LEDTokenizer.from_pretrained('allenai/led-base-16384'		)







							@cached_property
							def 							__lowercase			(							self					:       Optional[Any]		):
									return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									_a     :      Any        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									_a     :      Optional[Any]        =							[0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      List[Any]        =							tokenizer(_UpperCAmelCase	,max_length=len(_UpperCAmelCase		)	,padding=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)

											self.assertEqual((2, 9)	,batch.input_ids.shape		)
											self.assertEqual((2, 9)	,batch.attention_mask.shape		)
											_a     :      int        =							batch.input_ids.tolist()[0]
											self.assertListEqual(_UpperCAmelCase	,_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       Union[str, Any]		):
									_a     :      Dict        =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      int        =							tokenizer(_UpperCAmelCase	,padding=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIn('input_ids'	,_UpperCAmelCase		)
											self.assertIn('attention_mask'	,_UpperCAmelCase		)
											self.assertNotIn('labels'	,_UpperCAmelCase		)
											self.assertNotIn('decoder_attention_mask'	,_UpperCAmelCase		)







							@require_torch
							def 							__lowercase			(							self					:       str		):
									_a     :      List[Any]        =							[
									    'Summary of the text.',
									    'Another summary.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Dict        =							tokenizer(text_target=_UpperCAmelCase	,max_length=32	,padding='max_length'	,return_tensors='pt'		)
											self.assertEqual(32	,targets['input_ids'].shape[1]		)







							@require_torch
							def 							__lowercase			(							self					:       int		):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Any        =							tokenizer(
											    ['I am a small frog' * 1024, 'I am a small frog']	,padding=_UpperCAmelCase	,truncation=_UpperCAmelCase	,return_tensors='pt'		)
											self.assertIsInstance(_UpperCAmelCase	,_UpperCAmelCase		)
											self.assertEqual(batch.input_ids.shape	,(2, 5122)		)







							@require_torch
							def 							__lowercase			(							self					:       List[str]		):
									_a     :      int        =							['A long paragraph for summarization.']
									_a     :      int        =							[
									    'Summary of the text.',
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Dict        =							tokenizer(_UpperCAmelCase	,return_tensors='pt'		)
											_a     :      List[str]        =							tokenizer(text_target=_UpperCAmelCase	,return_tensors='pt'		)
											_a     :      List[Any]        =							inputs['input_ids']
											_a     :      Tuple        =							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 							__lowercase			(							self					:       Union[str, Any]		):
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											_a     :      Optional[Any]        =							['Summary of the text.', 'Another summary.']
											_a     :      int        =							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

											_a     :      List[Any]        =							tokenizer(_UpperCAmelCase	,padding=_UpperCAmelCase		)
											_a     :      int        =							[[0] * len(_UpperCAmelCase		) for x in encoded_output['input_ids']]
											_a     :      List[Any]        =							tokenizer.pad(_UpperCAmelCase		)
											self.assertSequenceEqual(outputs['global_attention_mask']	,_UpperCAmelCase		)







							def 							__lowercase			(							self					:       List[str]		):
									pass







							def 							__lowercase			(							self					:       int		):
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""		):
													_a     :      Dict        =							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      int        =							self.tokenizer_class.from_pretrained(_UpperCAmelCase	,**_UpperCAmelCase		)
													_a     :      Optional[Any]        =							'A, <mask> AllenNLP sentence.'
													_a     :      int        =							tokenizer_r.encode_plus(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase		)
													_a     :      List[Any]        =							tokenizer_p.encode_plus(_UpperCAmelCase	,add_special_tokens=_UpperCAmelCase	,return_token_type_ids=_UpperCAmelCase		)
													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']		)	,)

													_a     :      Any        =							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']		)
													_a     :      Optional[Any]        =							tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']		)
													self.assertSequenceEqual(tokens_p['input_ids']	,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)
													self.assertSequenceEqual(tokens_r['input_ids']	,[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]		)

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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A__								=						{
    '''configuration_ctrl''': ['''CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CTRLConfig'''],
    '''tokenization_ctrl''': ['''CTRLTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''CTRLForSequenceClassification''',
				    '''CTRLLMHeadModel''',
				    '''CTRLModel''',
				    '''CTRLPreTrainedModel''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A__								=						[
				    '''TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFCTRLForSequenceClassification''',
				    '''TFCTRLLMHeadModel''',
				    '''TFCTRLModel''',
				    '''TFCTRLPreTrainedModel''',
				]


if TYPE_CHECKING:
				from .configuration_ctrl import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP, CTRLConfig
				from .tokenization_ctrl import CTRLTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_ctrl import (
								    CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    CTRLForSequenceClassification,
								    CTRLLMHeadModel,
								    CTRLModel,
								    CTRLPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_ctrl import (
								    TF_CTRL_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFCTRLForSequenceClassification,
								    TFCTRLLMHeadModel,
								    TFCTRLModel,
								    TFCTRLPreTrainedModel,
								)

else:
				import sys

				A__								=						_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

