
import json
import os
import unittest

from transformers import AutoTokenizer, GPTaTokenizer, GPTaTokenizerFast
from transformers.models.gpta.tokenization_gpta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 			__A( snake_case__  ,   unittest.TestCase			):
				snake_case_						   =    GPTaTokenizer
				snake_case_						   =    GPTaTokenizerFast
				snake_case_						   =    True
				snake_case_						   =    {"""add_prefix_space""": True}
				snake_case_						   =    False




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								__a										=    [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								    '''<|endoftext|>''',
								]
								__a										=    dict(zip(_A		,     range(len(_A				)				)				)				)
								__a										=    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								__a										=    {'''unk_token''': '''<unk>'''}

								__a										=    os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']				)
								__a										=    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 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizer.from_pretrained(self.tmpdirname		,     **_A				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     **_snake_case				)    ->      Any:


								'''simple docstring'''




								kwargs.update(self.special_tokens_map				)
								return GPTaTokenizerFast.from_pretrained(self.tmpdirname		,     **_A				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								__a										=    '''lower newer'''
								__a										=    '''lower newer'''
								return input_text, output_text




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    GPTaTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map				)
								__a										=    '''lower newer'''
								__a										=    ['''\u0120low''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
								__a										=    tokenizer.tokenize(_A		,     add_prefix_space=_A				)
								self.assertListEqual(_A		,     _A				)

								__a										=    tokens + [tokenizer.unk_token]
								__a										=    [14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(_A				)		,     _A				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Tuple:


								'''simple docstring'''




								if not self.test_rust_tokenizer:
												return

								__a										=    self.get_tokenizer()
								__a										=    self.get_rust_tokenizer(add_prefix_space=_A				)

								__a										=    '''lower newer'''

								# Testing tokenization
								__a										=    tokenizer.tokenize(_A		,     add_prefix_space=_A				)
								__a										=    rust_tokenizer.tokenize(_A				)
								self.assertListEqual(_A		,     _A				)

								# Testing conversion to ids without special tokens
								__a										=    tokenizer.encode(_A		,     add_special_tokens=_A		,     add_prefix_space=_A				)
								__a										=    rust_tokenizer.encode(_A		,     add_special_tokens=_A				)
								self.assertListEqual(_A		,     _A				)

								# Testing conversion to ids with special tokens
								__a										=    self.get_rust_tokenizer(add_prefix_space=_A				)
								__a										=    tokenizer.encode(_A		,     add_prefix_space=_A				)
								__a										=    rust_tokenizer.encode(_A				)
								self.assertListEqual(_A		,     _A				)

								# Testing the unknown token
								__a										=    tokens + [rust_tokenizer.unk_token]
								__a										=    [14, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(rust_tokenizer.convert_tokens_to_ids(_A				)		,     _A				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Union[str, Any]:


								'''simple docstring'''




								pass




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case=15				)    ->      List[str]:


								'''simple docstring'''




								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
												with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""				):
																__a										=    self.rust_tokenizer_class.from_pretrained(_A		,     **_A				)

																# Simple input
																__a										=    '''This is a simple input'''
																__a										=    ['''This is a simple input 1''', '''This is a simple input 2''']
																__a										=    ('''This is a simple input''', '''This is a pair''')
																__a										=    [
																    ('''This is a simple input 1''', '''This is a simple input 2'''),
																    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																]

																# Simple input tests
																self.assertRaises(_A		,     tokenizer_r.encode		,     _A		,     max_length=_A		,     padding='''max_length'''				)

																# Simple input
																self.assertRaises(_A		,     tokenizer_r.encode_plus		,     _A		,     max_length=_A		,     padding='''max_length'''				)

																# Simple input
																self.assertRaises(
																    _A		,     tokenizer_r.batch_encode_plus		,     _A		,     max_length=_A		,     padding='''max_length'''		,     )

																# Pair input
																self.assertRaises(_A		,     tokenizer_r.encode		,     _A		,     max_length=_A		,     padding='''max_length'''				)

																# Pair input
																self.assertRaises(_A		,     tokenizer_r.encode_plus		,     _A		,     max_length=_A		,     padding='''max_length'''				)

																# Pair input
																self.assertRaises(
																    _A		,     tokenizer_r.batch_encode_plus		,     _A		,     max_length=_A		,     padding='''max_length'''		,     )




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    GPTaTokenizer.from_pretrained(self.tmpdirname		,     pad_token='''<pad>'''				)

								# Simple input
								__a										=    '''This is a simple input'''
								__a										=    ['''This is a simple input looooooooong''', '''This is a simple input''']
								__a										=    ('''This is a simple input''', '''This is a pair''')
								__a										=    [
								    ('''This is a simple input loooooong''', '''This is a simple input'''),
								    ('''This is a simple pair loooooong''', '''This is a simple pair'''),
								]

								__a										=    tokenizer.pad_token_id

								__a										=    tokenizer(_A		,     padding='''max_length'''		,     max_length=30		,     return_tensors='''np'''				)
								__a										=    tokenizer(_A		,     padding=_A		,     truncate=_A		,     return_tensors='''np'''				)
								__a										=    tokenizer(*_A		,     padding='''max_length'''		,     max_length=60		,     return_tensors='''np'''				)
								__a										=    tokenizer(_A		,     padding=_A		,     truncate=_A		,     return_tensors='''np'''				)

								# s
								# test single string max_length padding
								self.assertEqual(out_s['''input_ids'''].shape[-1]		,     30				)
								self.assertTrue(pad_token_id in out_s['''input_ids''']				)
								self.assertTrue(0 in out_s['''attention_mask''']				)

								# s2
								# test automatic padding
								self.assertEqual(out_sa['''input_ids'''].shape[-1]		,     33				)
								# long slice doesn't have padding
								self.assertFalse(pad_token_id in out_sa['''input_ids'''][0]				)
								self.assertFalse(0 in out_sa['''attention_mask'''][0]				)
								# short slice does have padding
								self.assertTrue(pad_token_id in out_sa['''input_ids'''][1]				)
								self.assertTrue(0 in out_sa['''attention_mask'''][1]				)

								# p
								# test single pair max_length padding
								self.assertEqual(out_p['''input_ids'''].shape[-1]		,     60				)
								self.assertTrue(pad_token_id in out_p['''input_ids''']				)
								self.assertTrue(0 in out_p['''attention_mask''']				)

								# p2
								# test automatic padding pair
								self.assertEqual(out_pa['''input_ids'''].shape[-1]		,     52				)
								# long slice pair doesn't have padding
								self.assertFalse(pad_token_id in out_pa['''input_ids'''][0]				)
								self.assertFalse(0 in out_pa['''attention_mask'''][0]				)
								# short slice pair does have padding
								self.assertTrue(pad_token_id in out_pa['''input_ids'''][1]				)
								self.assertTrue(0 in out_pa['''attention_mask'''][1]				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[int]:


								'''simple docstring'''




								__a										=    '''$$$'''
								__a										=    GPTaTokenizer.from_pretrained(self.tmpdirname		,     bos_token=_A		,     add_bos_token=_A				)

								__a										=    '''This is a simple input'''
								__a										=    ['''This is a simple input 1''', '''This is a simple input 2''']

								__a										=    tokenizer.bos_token_id

								__a										=    tokenizer(_A				)
								__a										=    tokenizer(_A				)

								self.assertEqual(out_s.input_ids[0]		,     _A				)
								self.assertTrue(all(o[0] == bos_token_id for o in out_sa.input_ids				)				)

								__a										=    tokenizer.decode(out_s.input_ids				)
								__a										=    tokenizer.batch_decode(out_sa.input_ids				)

								self.assertEqual(decode_s.split()[0]		,     _A				)
								self.assertTrue(all(d.split()[0] == bos_token for d in decode_sa				)				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								pass




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


								'''simple docstring'''




								__a										=    [self.get_tokenizer(do_lower_case=_A		,     add_bos_token=_A				)]
								for tokenizer in tokenizers:
												with self.subTest(F"""{tokenizer.__class__.__name__}"""				):
																__a										=    '''Encode this.'''
																__a										=    '''This one too please.'''
																__a										=    tokenizer.encode(_A		,     add_special_tokens=_A				)
																encoded_sequence += tokenizer.encode(_A		,     add_special_tokens=_A				)
																__a										=    tokenizer.encode_plus(
																    _A		,     _A		,     add_special_tokens=_A		,     return_special_tokens_mask=_A		,     )
																__a										=    encoded_sequence_dict['''input_ids''']
																__a										=    encoded_sequence_dict['''special_tokens_mask''']
																self.assertEqual(len(_A				)		,     len(_A				)				)

																__a										=    [
																    (x if not special_tokens_mask[i] else None) for i, x in enumerate(_A				)
																]
																__a										=    [x for x in filtered_sequence if x is not None]
																self.assertEqual(_A		,     _A				)







@require_tokenizers
class 			__A( unittest.TestCase			):




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


								'''simple docstring'''




								__a										=    AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,     from_slow=_A				)
								__a										=    '''A photo of a cat'''

								__a										=    tokenizer.encode(
								    _A		,     )
								self.assertEqual(_A		,     [2, 250, 1_345, 9, 10, 4_758]				)
								tokenizer.save_pretrained('''test_opt'''				)

								__a										=    AutoTokenizer.from_pretrained('''./test_opt'''				)
								__a										=    tokenizer.encode(
								    _A		,     )
								self.assertEqual(_A		,     [2, 250, 1_345, 9, 10, 4_758]				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      int:


								'''simple docstring'''




								__a										=    AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,     use_slow=_A				)
								__a										=    '''A photo of a cat'''

								__a										=    tokenizer.encode(
								    _A		,     )
								# Same as above
								self.assertEqual(_A		,     [2, 250, 1_345, 9, 10, 4_758]				)




				@unittest.skip('''This test is failing because of a bug in the fast tokenizer'''				)
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      List[str]:


								'''simple docstring'''




								__a										=    AutoTokenizer.from_pretrained('''facebook/opt-350m'''		,     from_slow=_A				)

								__a										=    '''bos'''
								__a										=    tokenizer.get_vocab()['''bos''']

								__a										=    '''A photo of a cat'''
								__a										=    tokenizer.encode(
								    _A		,     )
								# We changed the bos token
								self.assertEqual(_A		,     [31_957, 250, 1_345, 9, 10, 4_758]				)
								tokenizer.save_pretrained('''./tok'''				)
								__a										=    AutoTokenizer.from_pretrained('''./tok'''				)
								self.assertTrue(tokenizer.is_fast				)
								__a										=    tokenizer.encode(
								    _A		,     )
								self.assertEqual(_A		,     [31_957, 250, 1_345, 9, 10, 4_758]				)





from random import randint
from tempfile import TemporaryFile

import numpy as np







def 	_a      (						SCREAMING_SNAKE_CASE_							:				int      ,      SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[str]     ):
					__lowerCAmelCase				   =       0
					if start < end:
										__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
										__lowerCAmelCase				   =       a[end]
										__lowerCAmelCase				   =       a[pivot]
										__lowerCAmelCase				   =       temp

										__lowerCAmelCase   ,							__lowerCAmelCase				   =       _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 	_a      (						SCREAMING_SNAKE_CASE_							:				str      ,      SCREAMING_SNAKE_CASE_							:				List[Any]      ,      SCREAMING_SNAKE_CASE_							:				Optional[int]     ):
					__lowerCAmelCase				   =       0
					__lowerCAmelCase				   =       randint(SCREAMING_SNAKE_CASE_      ,      SCREAMING_SNAKE_CASE_     )
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       a[pivot]
					__lowerCAmelCase				   =       temp
					__lowerCAmelCase				   =       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
															__lowerCAmelCase				   =       new_pivot_index + 1
															__lowerCAmelCase				   =       a[new_pivot_index]
															__lowerCAmelCase				   =       a[index]
															__lowerCAmelCase				   =       temp

					__lowerCAmelCase				   =       a[new_pivot_index + 1]
					__lowerCAmelCase				   =       a[end]
					__lowerCAmelCase				   =       temp
					return new_pivot_index + 1, count


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


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


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

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


print(z)




