




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta 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, filter_roberta_detectors


@require_tokenizers
class 							_lowerCAmelCase		(     UpperCamelCase__  ,   unittest.TestCase	):
				lowerCamelCase__									=    BartTokenizer
				lowerCamelCase__									=    BartTokenizerFast
				lowerCamelCase__									=    True
				lowerCamelCase__									=    filter_roberta_detectors




				def     __a		( self							)    ->      Any:
											super().setUp()
											SCREAMING_SNAKE_CASE			: str	     =[
											    '''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(snake_case_		,		range(len(snake_case_							)							)							)							)
											SCREAMING_SNAKE_CASE			: Any	     =['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
											SCREAMING_SNAKE_CASE			: Dict	     ={'''unk_token''': '''<unk>'''}

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




				def     __a		( self		,		**snake_case_							)    ->      int:
											kwargs.update(self.special_tokens_map							)
											return self.tokenizer_class.from_pretrained(self.tmpdirname		,		**snake_case_							)




				def     __a		( self		,		**snake_case_							)    ->      Tuple:
											kwargs.update(self.special_tokens_map							)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,		**snake_case_							)




				def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
											return "lower newer", "lower newer"




				@cached_property
				def     __a		( self							)    ->      List[str]:
											return BartTokenizer.from_pretrained('''facebook/bart-large'''							)




				@cached_property
				def     __a		( self							)    ->      Any:
											return BartTokenizerFast.from_pretrained('''facebook/bart-large'''							)




				@require_torch
				def     __a		( self							)    ->      str:
											SCREAMING_SNAKE_CASE			: int	     =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
											SCREAMING_SNAKE_CASE			: Optional[Any]	     =[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

																		self.assertEqual((2, 9)		,		batch.input_ids.shape							)
																		self.assertEqual((2, 9)		,		batch.attention_mask.shape							)
																		SCREAMING_SNAKE_CASE			: Optional[Any]	     =batch.input_ids.tolist()[0]
																		self.assertListEqual(snake_case_		,		snake_case_							)
																		# Test that special tokens are reset




				@require_torch
				def     __a		( self							)    ->      int:
											SCREAMING_SNAKE_CASE			: Any	     =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE			: Any	     =tokenizer(snake_case_		,		padding=snake_case_		,		return_tensors='''pt'''							)
																		# check if input_ids are returned and no labels
																		self.assertIn('''input_ids'''		,		snake_case_							)
																		self.assertIn('''attention_mask'''		,		snake_case_							)
																		self.assertNotIn('''labels'''		,		snake_case_							)
																		self.assertNotIn('''decoder_attention_mask'''		,		snake_case_							)




				@require_torch
				def     __a		( self							)    ->      List[Any]:
											SCREAMING_SNAKE_CASE			: List[Any]	     =[
											    '''Summary of the text.''',
											    '''Another summary.''',
											]
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE			: int	     =tokenizer(text_target=snake_case_		,		max_length=32		,		padding='''max_length'''		,		return_tensors='''pt'''							)
																		self.assertEqual(32		,		targets['''input_ids'''].shape[1]							)




				@require_torch
				def     __a		( self							)    ->      Optional[int]:
											for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																		SCREAMING_SNAKE_CASE			: Optional[int]	     =tokenizer(
																		    ['''I am a small frog''' * 1_024, '''I am a small frog''']		,		padding=snake_case_		,		truncation=snake_case_		,		return_tensors='''pt'''							)
																		self.assertIsInstance(snake_case_		,		snake_case_							)
																		self.assertEqual(batch.input_ids.shape		,		(2, 1_024)							)




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




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







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

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r['''token_type_ids''']							)		,		sum(tokens_p['''token_type_ids''']							)							)

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									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			: Optional[int]	     =tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']							)
																									SCREAMING_SNAKE_CASE			: int	     =tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']							)

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p['''input_ids''']		,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)
																									self.assertSequenceEqual(tokens_r['''input_ids''']		,		[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]							)

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












def 						lowerCAmelCase_       (	__a = "mytext.txt"	,	__a = False )       ->			None:





							"""simple docstring"""





							import socket

							SCREAMING_SNAKE_CASE			: int	     =12312  # Reserve a port for your service.
							SCREAMING_SNAKE_CASE			: Dict	     =socket.socket()  # Create a socket object
							SCREAMING_SNAKE_CASE			: Any	     =socket.gethostname()  # Get local machine name
							sock.bind((host, port) )  # Bind to the port
							sock.listen(5 )  # Now wait for client connection.

							print('''Server listening....''' )

							while True:
														SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Optional[int]	     =sock.accept()  # Establish connection with client.
														print(f'Got connection from {addr}' )
														SCREAMING_SNAKE_CASE			: List[str]	     =conn.recv(1024 )
														print(f'Server received: {data = }' )

														with open(__a	,	'''rb''' ) as in_file:
																					SCREAMING_SNAKE_CASE			: Dict	     =in_file.read(1024 )
																					while data:
																												conn.send(__a )
																												print(f'Sent {data!r}' )
																												SCREAMING_SNAKE_CASE			: str	     =in_file.read(1024 )

														print('''Done sending''' )
														conn.close()
														if testing:  # Allow the test to complete
																					break

							sock.shutdown(1 )
							sock.close()


if __name__ == "__main__":
							send_file()






