

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     snake_case_			(				_UpperCAmelCase			, unittest.TestCase							):


							"""simple docstring"""





							__UpperCAmelCase       =LEDTokenizer
							__UpperCAmelCase       =LEDTokenizerFast
							__UpperCAmelCase       =True




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

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




							def        A__  ( self					,						**_A							):
											kwargs.update(self.special_tokens_map							)
											return self.tokenizer_class.from_pretrained(self.tmpdirname					,						**lowerCamelCase_							)




							def        A__  ( self					,						**_A							):
											kwargs.update(self.special_tokens_map							)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname					,						**lowerCamelCase_							)




							def        A__  ( self					,						_A							):
											return "lower newer", "lower newer"




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




							@cached_property
							def        A__  ( self							):
											return LEDTokenizerFast.from_pretrained('allenai/led-base-16384'							)




							@require_torch
							def        A__  ( self							):
											__lowerCAmelCase					=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
											__lowerCAmelCase					=							[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					=							tokenizer(lowerCamelCase_					,						max_length=len(lowerCamelCase_							)					,						padding=lowerCamelCase_					,						return_tensors='pt'							)
															self.assertIsInstance(lowerCamelCase_					,						lowerCamelCase_							)

															self.assertEqual((2, 9)					,						batch.input_ids.shape							)
															self.assertEqual((2, 9)					,						batch.attention_mask.shape							)
															__lowerCAmelCase					=							batch.input_ids.tolist()[0]
															self.assertListEqual(lowerCamelCase_					,						lowerCamelCase_							)




							@require_torch
							def        A__  ( self							):
											__lowerCAmelCase					=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__lowerCAmelCase					=							tokenizer(lowerCamelCase_					,						padding=lowerCamelCase_					,						return_tensors='pt'							)
															self.assertIn('input_ids'					,						lowerCamelCase_							)
															self.assertIn('attention_mask'					,						lowerCamelCase_							)
															self.assertNotIn('labels'					,						lowerCamelCase_							)
															self.assertNotIn('decoder_attention_mask'					,						lowerCamelCase_							)




							@require_torch
							def        A__  ( self							):
											__lowerCAmelCase					=							[
											    '''Summary of the text.''',
											    '''Another summary.''',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__lowerCAmelCase					=							tokenizer(text_target=lowerCamelCase_					,						max_length=3_2					,						padding='max_length'					,						return_tensors='pt'							)
															self.assertEqual(3_2					,						targets['input_ids'].shape[1]							)




							@require_torch
							def        A__  ( self							):




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__lowerCAmelCase					=							tokenizer(
															    ['I am a small frog' * 1_0_2_4, 'I am a small frog']					,						padding=lowerCamelCase_					,						truncation=lowerCamelCase_					,						return_tensors='pt'							)
															self.assertIsInstance(lowerCamelCase_					,						lowerCamelCase_							)
															self.assertEqual(batch.input_ids.shape					,						(2, 5_1_2_2)							)




							@require_torch
							def        A__  ( self							):
											__lowerCAmelCase					=							['''A long paragraph for summarization.''']
											__lowerCAmelCase					=							[
											    '''Summary of the text.''',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__lowerCAmelCase					=							tokenizer(lowerCamelCase_					,						return_tensors='pt'							)
															__lowerCAmelCase					=							tokenizer(text_target=lowerCamelCase_					,						return_tensors='pt'							)
															__lowerCAmelCase					=							inputs['''input_ids''']
															__lowerCAmelCase					=							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        A__  ( self							):




											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__lowerCAmelCase					=							['''Summary of the text.''', '''Another summary.''']
															__lowerCAmelCase					=							[[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, -1, -1]]

															__lowerCAmelCase					=							tokenizer(lowerCamelCase_					,						padding=lowerCamelCase_							)
															__lowerCAmelCase					=							[[0] * len(lowerCamelCase_							) for x in encoded_output['''input_ids''']]
															__lowerCAmelCase					=							tokenizer.pad(lowerCamelCase_							)
															self.assertSequenceEqual(outputs['global_attention_mask']					,						lowerCamelCase_							)




							def        A__  ( self							):
											pass




							def        A__  ( self							):




											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
																			__lowerCAmelCase					=							self.rust_tokenizer_class.from_pretrained(lowerCamelCase_					,						**lowerCamelCase_							)
																			__lowerCAmelCase					=							self.tokenizer_class.from_pretrained(lowerCamelCase_					,						**lowerCamelCase_							)
																			__lowerCAmelCase					=							'''A, <mask> AllenNLP sentence.'''
																			__lowerCAmelCase					=							tokenizer_r.encode_plus(lowerCamelCase_					,						add_special_tokens=lowerCamelCase_					,						return_token_type_ids=lowerCamelCase_							)
																			__lowerCAmelCase					=							tokenizer_p.encode_plus(lowerCamelCase_					,						add_special_tokens=lowerCamelCase_					,						return_token_type_ids=lowerCamelCase_							)
																			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					=							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']							)
																			__lowerCAmelCase					=							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(
																			    lowerCamelCase_					,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)
																			self.assertSequenceEqual(
																			    lowerCamelCase_					,						['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']							)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase      				=     {
    '''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCamelCase      				=     [
		    '''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''LiltForQuestionAnswering''',
		    '''LiltForSequenceClassification''',
		    '''LiltForTokenClassification''',
		    '''LiltModel''',
		    '''LiltPreTrainedModel''',
		]

if TYPE_CHECKING:
		from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_lilt import (
				    LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    LiltForQuestionAnswering,
				    LiltForSequenceClassification,
				    LiltForTokenClassification,
				    LiltModel,
				    LiltPreTrainedModel,
				)

else:
		import sys

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






