




"""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 				snake_case_			(					lowerCamelCase_	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       LEDTokenizer
   A_				      =       LEDTokenizerFast
   A_				      =       True







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

       UpperCamelCase              =	os.path.join(self.tmpdirname       ,					VOCAB_FILES_NAMES['''vocab_file'''])
       UpperCamelCase              =	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   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   Union[str, Any]:
       kwargs.update(self.special_tokens_map)
       return self.tokenizer_class.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					**lowerCamelCase_)				->   Optional[int]:
       kwargs.update(self.special_tokens_map)
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,					**lowerCamelCase_)







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_)				->   Tuple:
       return "lower newer", "lower newer"







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







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







   @require_torch
   def   UpperCAmelCase__			(		self)				->   str:
       UpperCamelCase              =	['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
       UpperCamelCase              =	[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]:
           UpperCamelCase              =	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)
           UpperCamelCase              =	batch.input_ids.tolist()[0]
           self.assertListEqual(lowerCamelCase_       ,					lowerCamelCase_)







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

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







   def   UpperCAmelCase__			(		self)				->   Tuple:
       pass







   def   UpperCAmelCase__			(		self)				->   Any:
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
           with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'):
               UpperCamelCase              =	self.rust_tokenizer_class.from_pretrained(lowerCamelCase_       ,					**lowerCamelCase_)
               UpperCamelCase              =	self.tokenizer_class.from_pretrained(lowerCamelCase_       ,					**lowerCamelCase_)
               UpperCamelCase              =	'''A, <mask> AllenNLP sentence.'''
               UpperCamelCase              =	tokenizer_r.encode_plus(lowerCamelCase_       ,					add_special_tokens=lowerCamelCase_       ,					return_token_type_ids=lowerCamelCase_)
               UpperCamelCase              =	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'''])       ,					)

               UpperCamelCase              =	tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''])
               UpperCamelCase              =	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>'''])


'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_    						=       {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       ["""MBartTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """MBartForCausalLM""",
							    """MBartForConditionalGeneration""",
							    """MBartForQuestionAnswering""",
							    """MBartForSequenceClassification""",
							    """MBartModel""",
							    """MBartPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """TFMBartForConditionalGeneration""",
							    """TFMBartModel""",
							    """TFMBartPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_    						=       [
							    """FlaxMBartForConditionalGeneration""",
							    """FlaxMBartForQuestionAnswering""",
							    """FlaxMBartForSequenceClassification""",
							    """FlaxMBartModel""",
							    """FlaxMBartPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart import MBartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_mbart_fast import MBartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_mbart import (
														    MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MBartForCausalLM,
														    MBartForConditionalGeneration,
														    MBartForQuestionAnswering,
														    MBartForSequenceClassification,
														    MBartModel,
														    MBartPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_mbart import (
														    FlaxMBartForConditionalGeneration,
														    FlaxMBartForQuestionAnswering,
														    FlaxMBartForSequenceClassification,
														    FlaxMBartModel,
														    FlaxMBartPreTrainedModel,
														)

else:
							import sys

							UpperCamelCase_    						=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





