
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     lowerCamelCase      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					int    		=	LEDTokenizer
	UpperCamelCase_  :					Optional[Any]    		=	LEDTokenizerFast
	UpperCamelCase_  :					Optional[int]    		=	True







	def  snake_case__     (     self      :Optional[Any]			)		->      Union[str, Any]:







				"""simple docstring"""






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

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







	def  snake_case__     (     self      :Optional[int]   ,     **lowercase      :Any			)		->      List[Any]:







				"""simple docstring"""






				kwargs.update(self.special_tokens_map			)
				return self.tokenizer_class.from_pretrained(self.tmpdirname   ,     **lowercase			)







	def  snake_case__     (     self      :int   ,     **lowercase      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				kwargs.update(self.special_tokens_map			)
				return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,     **lowercase			)







	def  snake_case__     (     self      :Optional[Any]   ,     lowercase      :Union[str, Any]			)		->      List[str]:







				"""simple docstring"""






				return "lower newer", "lower newer"







	@cached_property
	def  snake_case__     (     self      :Optional[Any]			)		->      List[str]:







				"""simple docstring"""






				return LEDTokenizer.from_pretrained('''allenai/led-base-16384'''			)







	@cached_property
	def  snake_case__     (     self      :int			)		->      Any:







				"""simple docstring"""






				return LEDTokenizerFast.from_pretrained('''allenai/led-base-16384'''			)







	@require_torch
	def  snake_case__     (     self      :Tuple			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				SCREAMING_SNAKE_CASE     						= [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]:
							SCREAMING_SNAKE_CASE     						= tokenizer(lowercase   ,     max_length=len(lowercase			)   ,     padding=lowercase   ,     return_tensors='''pt'''			)
							self.assertIsInstance(lowercase   ,     lowercase			)

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







	@require_torch
	def  snake_case__     (     self      :Optional[Any]			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							SCREAMING_SNAKE_CASE     						= tokenizer(lowercase   ,     padding=lowercase   ,     return_tensors='''pt'''			)
							self.assertIn('''input_ids'''   ,     lowercase			)
							self.assertIn('''attention_mask'''   ,     lowercase			)
							self.assertNotIn('''labels'''   ,     lowercase			)
							self.assertNotIn('''decoder_attention_mask'''   ,     lowercase			)







	@require_torch
	def  snake_case__     (     self      :int			)		->      Optional[int]:







				"""simple docstring"""






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







	@require_torch
	def  snake_case__     (     self      :Optional[int]			)		->      Any:







				"""simple docstring"""






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







	@require_torch
	def  snake_case__     (     self      :Optional[int]			)		->      Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= ['''A long paragraph for summarization.''']
				SCREAMING_SNAKE_CASE     						= [
				    '''Summary of the text.''',
				]
				for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							SCREAMING_SNAKE_CASE     						= tokenizer(lowercase   ,     return_tensors='''pt'''			)
							SCREAMING_SNAKE_CASE     						= tokenizer(text_target=lowercase   ,     return_tensors='''pt'''			)
							SCREAMING_SNAKE_CASE     						= inputs['''input_ids''']
							SCREAMING_SNAKE_CASE     						= 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  snake_case__     (     self      :Optional[Any]			)		->      int:







				"""simple docstring"""






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

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







	def  snake_case__     (     self      :Tuple			)		->      Any:







				"""simple docstring"""






				pass







	def  snake_case__     (     self      :Optional[int]			)		->      Tuple:







				"""simple docstring"""






				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""			):
										SCREAMING_SNAKE_CASE     						= self.rust_tokenizer_class.from_pretrained(lowercase   ,     **lowercase			)
										SCREAMING_SNAKE_CASE     						= self.tokenizer_class.from_pretrained(lowercase   ,     **lowercase			)
										SCREAMING_SNAKE_CASE     						= '''A, <mask> AllenNLP sentence.'''
										SCREAMING_SNAKE_CASE     						= tokenizer_r.encode_plus(lowercase   ,     add_special_tokens=lowercase   ,     return_token_type_ids=lowercase			)
										SCREAMING_SNAKE_CASE     						= tokenizer_p.encode_plus(lowercase   ,     add_special_tokens=lowercase   ,     return_token_type_ids=lowercase			)
										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''']			)   ,     )

										SCREAMING_SNAKE_CASE     						= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']			)
										SCREAMING_SNAKE_CASE     						= 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(
										    lowercase   ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)
										self.assertSequenceEqual(
										    lowercase   ,     ['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']			)

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
							import torch





@require_torch
@require_optimum
@slow
class     lowerCamelCase      (       unittest.TestCase					):







	def  snake_case__     (     self      :List[Any]			)		->      Optional[Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoTokenizer.from_pretrained(lowercase			)
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= tokenizer('''This is me'''   ,     return_tensors='''pt'''			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				SCREAMING_SNAKE_CASE     						= model.generate(**lowercase			)

				SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()

				self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()			)			)

				with tempfile.TemporaryDirectory() as tmpdirname:
							model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

							self.assertFalse(
							    any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()			)			)

							SCREAMING_SNAKE_CASE     						= model_reloaded.generate(**lowercase			)
							self.assertTrue(torch.allclose(lowercase   ,     lowercase			)			)







	def  snake_case__     (     self      :Union[str, Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''hf-internal-testing/tiny-random-t5'''
				SCREAMING_SNAKE_CASE     						= AutoModelForSeqaSeqLM.from_pretrained(lowercase			)

				SCREAMING_SNAKE_CASE     						= model.to_bettertransformer()

				with tempfile.TemporaryDirectory() as tmpdirname:
							with self.assertRaises(lowercase			):
										model.save_pretrained(lowercase			)

							SCREAMING_SNAKE_CASE     						= model.reverse_bettertransformer()
							model.save_pretrained(lowercase			)