


import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD







def 						lowerCamelCase(   a__):
				def wrapper(*a__	,**a__):
								_SCREAMING_SNAKE_CASE			  =timeit.default_timer()
								_SCREAMING_SNAKE_CASE			  =func(*a__	,**a__)
								_SCREAMING_SNAKE_CASE			  =timeit.default_timer() - starttime
								return delta

				_SCREAMING_SNAKE_CASE			  =func.__name__

				return wrapper







def 						lowerCamelCase(   a__	,a__=100	,a__=None):
				_SCREAMING_SNAKE_CASE			  =[]
				_SCREAMING_SNAKE_CASE			  =seq_shapes or {}
				for i in range(a__):
								_SCREAMING_SNAKE_CASE			  ={}
								for col_id, (k, v) in enumerate(features.items()):
												if isinstance(a__	,_ArrayXD):
																_SCREAMING_SNAKE_CASE			  =np.random.rand(*v.shape).astype(v.dtype)
												elif isinstance(a__	,datasets.Value):
																if v.dtype == "string":
																				_SCREAMING_SNAKE_CASE			  ='''The small grey turtle was surprisingly fast when challenged.'''
																else:
																				_SCREAMING_SNAKE_CASE			  =np.random.randint(10	,size=1).astype(v.dtype).item()
												elif isinstance(a__	,datasets.Sequence):
																while isinstance(a__	,datasets.Sequence):
																				_SCREAMING_SNAKE_CASE			  =v.feature
																_SCREAMING_SNAKE_CASE			  =seq_shapes[k]
																_SCREAMING_SNAKE_CASE			  =np.random.rand(*a__).astype(v.dtype)
												_SCREAMING_SNAKE_CASE			  =data

								dummy_data.append((i, example))

				return dummy_data







def 						lowerCamelCase(   a__	,a__	,a__=100	,a__=None):
				_SCREAMING_SNAKE_CASE			  =generate_examples(a__	,num_examples=a__	,seq_shapes=a__)

				with ArrowWriter(features=a__	,path=a__) as writer:
								for key, record in dummy_data:
												_SCREAMING_SNAKE_CASE			  =features.encode_example(a__)
												writer.write(a__)

								_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE			  =writer.finalize()

				if not num_final_examples == num_examples:
								raise ValueError(
								    f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}.")

				_SCREAMING_SNAKE_CASE			  =datasets.Dataset.from_file(filename=a__	,info=datasets.DatasetInfo(features=a__))

				return dataset



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__							(		UpperCamelCase__   ,     unittest.TestCase  ):
						UpperCAmelCase             =						LEDTokenizer
						UpperCAmelCase             =						LEDTokenizerFast
						UpperCAmelCase             =						True




						def 							__UpperCamelCase				(       self					:      Dict  )							->     Optional[int]:


										"""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(_a				,    range(len(_a  )  )  )  )
										_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(_a  ) + '''\n'''  )
										with open(self.merges_file				,    '''w'''				,    encoding='''utf-8'''  ) as fp:
														fp.write('''\n'''.join(_a  )  )




						def 							__UpperCamelCase				(       self					:      str				,    **_a					:      int  )							->     Optional[Any]:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      List[str]				,    **_a					:      List[Any]  )							->     str:


										"""simple docstring"""





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




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      Any  )							->     Dict:


										"""simple docstring"""





										return "lower newer", "lower newer"




						@cached_property
						def 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     Dict:


										"""simple docstring"""





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




						@cached_property
						def 							__UpperCamelCase				(       self					:      List[Any]  )							->     Any:


										"""simple docstring"""





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




						@require_torch
						def 							__UpperCamelCase				(       self					:      str  )							->     Dict:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
										_SCREAMING_SNAKE_CASE			  =[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

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

														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(_a				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     Dict:


										"""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(_a				,    padding=_a				,    return_tensors='''pt'''  )
														self.assertIn('''input_ids'''				,    _a  )
														self.assertIn('''attention_mask'''				,    _a  )
														self.assertNotIn('''labels'''				,    _a  )
														self.assertNotIn('''decoder_attention_mask'''				,    _a  )




						@require_torch
						def 							__UpperCamelCase				(       self					:      Dict  )							->     str:


										"""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=_a				,    max_length=32				,    padding='''max_length'''				,    return_tensors='''pt'''  )
														self.assertEqual(32				,    targets['''input_ids'''].shape[1]  )




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


										"""simple docstring"""





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




						@require_torch
						def 							__UpperCamelCase				(       self					:      int  )							->     Dict:


										"""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(_a				,    return_tensors='''pt'''  )
														_SCREAMING_SNAKE_CASE			  =tokenizer(text_target=_a				,    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 							__UpperCamelCase				(       self					:      Optional[Any]  )							->     str:


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




						def 							__UpperCamelCase				(       self					:      int  )							->     List[str]:


										"""simple docstring"""





										pass




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


										"""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(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  =self.tokenizer_class.from_pretrained(_a				,    **_a  )
																		_SCREAMING_SNAKE_CASE			  ='''A, <mask> AllenNLP sentence.'''
																		_SCREAMING_SNAKE_CASE			  =tokenizer_r.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		_SCREAMING_SNAKE_CASE			  =tokenizer_p.encode_plus(_a				,    add_special_tokens=_a				,    return_token_type_ids=_a  )
																		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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
																		self.assertSequenceEqual(tokens_r['''input_ids''']				,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

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