



'''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 	a__     (				_lowercase,			unittest.TestCase	):
	__magic_name__		:    Union[str, Any]	    =     LEDTokenizer
	__magic_name__		:    Tuple	    =     LEDTokenizerFast
	__magic_name__		:    Union[str, Any]	    =     True


	def 				lowercase__  (self : Any      )     ->			List[Any]:

					"""simple docstring"""

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

					SCREAMING_SNAKE_CASE  :						int      					= os.path.join(self.tmpdirname,					VOCAB_FILES_NAMES['''vocab_file''']      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= 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 : Union[str, Any],					**__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

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


	def 				lowercase__  (self : str,					**__UpperCAmelCase : Union[str, Any]      )     ->			Tuple:

					"""simple docstring"""

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


	def 				lowercase__  (self : Dict,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					return "lower newer", "lower newer"


	@cached_property
	def 				lowercase__  (self : Optional[int]      )     ->			Optional[Any]:

					"""simple docstring"""

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


	@cached_property
	def 				lowercase__  (self : Optional[int]      )     ->			Tuple:

					"""simple docstring"""

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


	@require_torch
	def 				lowercase__  (self : int      )     ->			Tuple:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
					SCREAMING_SNAKE_CASE  :						str      					= [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE  :						int      					= 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      )
									SCREAMING_SNAKE_CASE  :						Dict      					= batch.input_ids.tolist()[0]
									self.assertListEqual(__UpperCAmelCase,					__UpperCAmelCase      )


	@require_torch
	def 				lowercase__  (self : List[str]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE  :						Dict      					= 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 : Dict      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= [
					    '''Summary of the text.''',
					    '''Another summary.''',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE  :						str      					= 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 : Optional[Any]      )     ->			List[Any]:

					"""simple docstring"""

					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE  :						Union[str, 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 : Any      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= ['''A long paragraph for summarization.''']
					SCREAMING_SNAKE_CASE  :						int      					= [
					    '''Summary of the text.''',
					]
					for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
									SCREAMING_SNAKE_CASE  :						str      					= tokenizer(__UpperCAmelCase,					return_tensors='''pt'''      )
									SCREAMING_SNAKE_CASE  :						str      					= tokenizer(text_target=__UpperCAmelCase,					return_tensors='''pt'''      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= inputs['''input_ids''']
									SCREAMING_SNAKE_CASE  :						List[Any]      					= 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 : Dict      )     ->			Tuple:

					"""simple docstring"""

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

									SCREAMING_SNAKE_CASE  :						List[str]      					= tokenizer(__UpperCAmelCase,					padding=__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= [[0] * len(__UpperCAmelCase      ) for x in encoded_output['''input_ids''']]
									SCREAMING_SNAKE_CASE  :						List[Any]      					= tokenizer.pad(__UpperCAmelCase      )
									self.assertSequenceEqual(outputs['''global_attention_mask'''],					__UpperCAmelCase      )


	def 				lowercase__  (self : Dict      )     ->			Dict:

					"""simple docstring"""

					pass






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

					"""simple docstring"""

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

													SCREAMING_SNAKE_CASE  :						Tuple      					= tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']      )
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= 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>''']      )









'''simple docstring'''

from random import randint
from tempfile import TemporaryFile

import numpy as np






def       __lowercase  (_SCREAMING_SNAKE_CASE      :List[str]    ,	_SCREAMING_SNAKE_CASE      :Any    ,	_SCREAMING_SNAKE_CASE      :str       ):
				SCREAMING_SNAKE_CASE  :						int      					= 0
				if start < end:
								SCREAMING_SNAKE_CASE  :						Optional[int]      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[end]
								SCREAMING_SNAKE_CASE  :						List[str]      					= a[pivot]
								SCREAMING_SNAKE_CASE  :						Dict      					= temp

								SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= _in_place_partition(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	p - 1       )
								count += _in_place_quick_sort(_SCREAMING_SNAKE_CASE    ,	p + 1    ,	_SCREAMING_SNAKE_CASE       )
				return count






def       __lowercase  (_SCREAMING_SNAKE_CASE      :str    ,	_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[Any]       ):
				SCREAMING_SNAKE_CASE  :						Any      					= 0
				SCREAMING_SNAKE_CASE  :						Tuple      					= randint(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[end]
				SCREAMING_SNAKE_CASE  :						int      					= a[pivot]
				SCREAMING_SNAKE_CASE  :						Tuple      					= temp
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= start - 1
				for index in range(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE       ):
								count += 1
								if a[index] < a[end]:  # check if current val is less than pivot value
												SCREAMING_SNAKE_CASE  :						Tuple      					= new_pivot_index + 1
												SCREAMING_SNAKE_CASE  :						Dict      					= a[new_pivot_index]
												SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= a[index]
												SCREAMING_SNAKE_CASE  :						Optional[int]      					= temp

				SCREAMING_SNAKE_CASE  :						List[Any]      					= a[new_pivot_index + 1]
				SCREAMING_SNAKE_CASE  :						Any      					= a[end]
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= temp
				return new_pivot_index + 1, count


snake_case_			=     TemporaryFile()
snake_case_			=     100  # 1000 elements are to be sorted


snake_case_			,				snake_case_			=     0, 1  # mean and standard deviation
snake_case_			=     np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("""The array is""")
print(X)


outfile.seek(0)  # using the same array
snake_case_			=     np.load(outfile)
snake_case_			=     len(M) - 1
snake_case_			=     _in_place_quick_sort(M, 0, r)

print(
    """No of Comparisons for 100 elements selected from a standard normal distribution"""
    """is :"""
)




print(z)




