


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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__						(   lowercase_							,    unittest.TestCase			):
						lowerCAmelCase       =     MvpTokenizer
						lowerCAmelCase       =     MvpTokenizerFast
						lowerCAmelCase       =     True
						lowerCAmelCase       =     filter_roberta_detectors







						def __a						(							self   :    List[Any]		):
								super().setUp()
								A					       = [
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """\u0120""",
								    """\u0120l""",
								    """\u0120n""",
								    """\u0120lo""",
								    """\u0120low""",
								    """er""",
								    """\u0120lowest""",
								    """\u0120newer""",
								    """\u0120wider""",
								    """<unk>""",
								]
								A					       = dict(zip(lowerCamelCase_			,			range(len(lowerCamelCase_		)		)		)		)
								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(lowerCamelCase_		) + '\n'		)
								with open(self.merges_file			,			'w'			,			encoding='utf-8'		) as fp:
										fp.write('\n'.join(lowerCamelCase_		)		)







						def __a						(							self   :    Optional[Any]			,			**_lowercase   :    List[str]		):
								kwargs.update(self.special_tokens_map		)
								return self.tokenizer_class.from_pretrained(self.tmpdirname			,			**lowerCamelCase_		)







						def __a						(							self   :    Tuple			,			**_lowercase   :    Optional[Any]		):
								kwargs.update(self.special_tokens_map		)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,			**lowerCamelCase_		)







						def __a						(							self   :    Optional[Any]			,			_lowercase   :    List[Any]		):
								return "lower newer", "lower newer"







						@cached_property
						def __a						(							self   :    Optional[int]		):
								return MvpTokenizer.from_pretrained('RUCAIBox/mvp'		)







						@cached_property
						def __a						(							self   :    Optional[Any]		):
								return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'		)







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

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

										self.assertEqual((2, 9)			,			batch.input_ids.shape		)
										self.assertEqual((2, 9)			,			batch.attention_mask.shape		)
										A					       = batch.input_ids.tolist()[0]
										self.assertListEqual(lowerCamelCase_			,			lowerCamelCase_		)
										# Test that special tokens are reset







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







						@require_torch
						def __a						(							self   :    Optional[Any]		):
								A					       = [
								    """Summary of the text.""",
								    """Another summary.""",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										A					       = tokenizer(text_target=lowerCamelCase_			,			max_length=32			,			padding='max_length'			,			return_tensors='pt'		)
										self.assertEqual(32			,			targets['input_ids'].shape[1]		)







						@require_torch
						def __a						(							self   :    Tuple		):



								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										A					       = tokenizer(
										    ['I am a small frog' * 1_024, 'I am a small frog']			,			padding=lowerCamelCase_			,			truncation=lowerCamelCase_			,			return_tensors='pt'		)
										self.assertIsInstance(lowerCamelCase_			,			lowerCamelCase_		)
										self.assertEqual(batch.input_ids.shape			,			(2, 1_024)		)







						@require_torch
						def __a						(							self   :    int		):
								A					       = ["""A long paragraph for summarization."""]
								A					       = [
								    """Summary of the text.""",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
										A					       = tokenizer(lowerCamelCase_			,			text_target=lowerCamelCase_			,			return_tensors='pt'		)
										A					       = inputs["""input_ids"""]
										A					       = inputs["""labels"""]
										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   :    Any		):
								pass







						def __a						(							self   :    List[str]		):



								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(lowerCamelCase_			,			**lowerCamelCase_		)
												A					       = self.tokenizer_class.from_pretrained(lowerCamelCase_			,			**lowerCamelCase_		)
												A					       = """A, <mask> AllenNLP sentence."""
												A					       = tokenizer_r.encode_plus(lowerCamelCase_			,			add_special_tokens=lowerCamelCase_			,			return_token_type_ids=lowerCamelCase_		)
												A					       = tokenizer_p.encode_plus(lowerCamelCase_			,			add_special_tokens=lowerCamelCase_			,			return_token_type_ids=lowerCamelCase_		)

												# 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']		)			,			)

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









"""simple docstring"""


from unittest.mock import Mock, patch

from file_transfer.send_file import send_file







@patch('socket.socket'	)
@patch('builtins.open'	)
def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Dict:







		"""simple docstring"""





		A					       = Mock()
		A					       = conn, Mock()
		A					       = iter([1, None]	)
		A					       = lambda UpperCamelCase__	: next(UpperCamelCase__	)

		# ===== invoke =====
		send_file(filename='mytext.txt' ,			testing=UpperCamelCase__	)

		# ===== ensurance =====
		sock.assert_called_once()
		sock.return_value.bind.assert_called_once()
		sock.return_value.listen.assert_called_once()
		sock.return_value.accept.assert_called_once()
		conn.recv.assert_called_once()

		file.return_value.__enter__.assert_called_once()
		file.return_value.__enter__.return_value.read.assert_called()

		conn.send.assert_called_once()
		conn.close.assert_called_once()
		sock.return_value.shutdown.assert_called_once()
		sock.return_value.close.assert_called_once()





