
'''simple docstring'''







import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     CLIPTokenizer
					lowerCAmelCase_        =     CLIPTokenizerFast
					lowerCAmelCase_        =     True
					lowerCAmelCase_        =     {}
					lowerCAmelCase_        =     False






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().setUp()

											# fmt: off
											lowercase_	:     List[str]													=       ['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
											# fmt: on
											lowercase_	:     Union[str, Any]													=       dict(zip(__SCREAMING_SNAKE_CASE				,							range(len(__SCREAMING_SNAKE_CASE		)		)		)		)
											lowercase_	:     Union[str, Any]													=       ['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''']
											lowercase_	:     Optional[Any]													=       {'''unk_token''': '''<unk>'''}

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






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											kwargs.update(self.special_tokens_map		)
											return CLIPTokenizer.from_pretrained(self.tmpdirname				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											kwargs.update(self.special_tokens_map		)
											return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Union[str, Any]													=       '''lower newer'''
											lowercase_	:     Optional[int]													=       '''lower newer'''
											return input_text, output_text






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       CLIPTokenizer(self.vocab_file				,							self.merges_file				,							**self.special_tokens_map		)
											lowercase_	:     Optional[Any]													=       '''lower newer'''
											lowercase_	:     Any													=       ['''lo''', '''w''', '''er</w>''', '''n''', '''e''', '''w''', '''er</w>''']
											lowercase_	:     int													=       tokenizer.tokenize(__SCREAMING_SNAKE_CASE		)
											self.assertListEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     List[str]													=       tokens + [tokenizer.unk_token]
											lowercase_	:     Tuple													=       [10, 2, 16, 9, 3, 2, 16, 20]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)				,							__SCREAMING_SNAKE_CASE		)






					@require_ftfy
					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																							lowercase_	:     Union[str, Any]													=       self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     int													=       self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)

																							lowercase_	:     List[Any]													=       '''A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'''
																							lowercase_	:     Tuple													=       tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Tuple													=       tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE		)

																							self.assertListEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

																							# Test that the tokenization is identical on an example containing a character (Latin Small Letter A
																							# with Tilde) encoded in 2 different ways
																							lowercase_	:     Optional[Any]													=       '''xa\u0303y''' + ''' ''' + '''x\xe3y'''
																							lowercase_	:     Any													=       tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     str													=       tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE		)

																							self.assertListEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

																							# Test that the tokenization is identical on unicode of space type
																							lowercase_	:     Any													=       [
																							    '''\u0009''',  # (horizontal tab, '\t')
																							    '''\u000B''',  # (vertical tab)
																							    '''\u000C''',  # (form feed)
																							    '''\u0020''',  # (space, ' ')
																							    '''\u200E''',  # (left-to-right mark):w
																							    '''\u200F''',  # (right-to-left mark)
																							]
																							for unicode_seq in spaces_unicodes:
																													lowercase_	:     int													=       tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE		)
																													lowercase_	:     Optional[Any]													=       tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE		)

																													self.assertListEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

																							# Test that the tokenization is identical on unicode of line break type
																							lowercase_	:     List[Any]													=       [
																							    '''\u000A''',  # (line feed, '\n')
																							    '''\r\n''',  # (carriage return and line feed, '\r\n')
																							    '''\u000D''',  # (carriage return, '\r')
																							    '''\r''',  # (carriage return, '\r')
																							    '''\u000D''',  # (carriage return, '\r')
																							    '''\u2028''',  # (line separator)
																							    '''\u2029''',  # (paragraph separator)
																							    # "\u0085", # (next line)
																							]

																							# The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
																							# it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
																							# space (and thus into an empty list).

																							for unicode_seq in line_break_unicodes:
																													lowercase_	:     int													=       tokenizer_s.tokenize(__SCREAMING_SNAKE_CASE		)
																													lowercase_	:     Union[str, Any]													=       tokenizer_r.tokenize(__SCREAMING_SNAKE_CASE		)

																													self.assertListEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																							lowercase_	:     List[str]													=       '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																							lowercase_	:     Optional[int]													=       F'''{text_of_1_token} {text_of_1_token}'''

																							lowercase_	:     str													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							)
																							lowercase_	:     int													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		) + 1, len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Dict													=       F''' {text}'''

																							lowercase_	:     Optional[Any]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							)
																							lowercase_	:     Dict													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(1, 1 + len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(1 + len(__SCREAMING_SNAKE_CASE		) + 1, 1 + len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											with self.assertRaises(__SCREAMING_SNAKE_CASE		) as context:
																	self.rust_tokenizer_class.from_pretrained('''robot-test/old-clip-tokenizer'''		)

											self.assertTrue(
											    context.exception.args[0].startswith(
											        '''The `backend_tokenizer` provided does not match the expected format.'''		)		)






					@require_ftfy
					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().test_tokenization_python_rust_equals()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass





'''simple docstring'''







import torch

from transformers import AutoModel






class  lowerCAmelCase__   (  torch.nn.Module     ):






					def __init__(   self				,							__SCREAMING_SNAKE_CASE="sayef/fsner-bert-base-uncased"		):


											"""simple docstring"""





											super(__SCREAMING_SNAKE_CASE				,							self		).__init__()

											lowercase_	:     Tuple													=       AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       torch.nn.CosineSimilarity(3				,							1E-0_8		)
											lowercase_	:     Optional[Any]													=       torch.nn.Softmax(dim=1		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.bert(**__SCREAMING_SNAKE_CASE		).last_hidden_state






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return token_embeddings.sum(2				,							keepdim=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=1		):


											"""simple docstring"""





											return self.softmax(T * self.cos(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       W_supports['''sizes'''].tolist()
											lowercase_	:     Dict													=       W_supports['''start_token_id'''].item()
											lowercase_	:     List[Any]													=       W_supports['''end_token_id'''].item()

											del W_supports["sizes"]
											del W_supports["start_token_id"]
											del W_supports["end_token_id"]

											lowercase_	:     List[str]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       None
											lowercase_	:     Dict													=       None

											lowercase_	:     Tuple													=       W_supports['''input_ids'''] == start_token_id
											lowercase_	:     Any													=       W_supports['''input_ids'''] == end_token_id

											for i, size in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == 0:
																							lowercase_	:     List[str]													=       0
																	else:
																							lowercase_	:     List[Any]													=       support_sizes[i - 1]

																	lowercase_	:     str													=       S[s : s + size][start_token_masks[s : s + size]]
																	lowercase_	:     Optional[int]													=       S[s : s + size][end_token_masks[s : s + size]]

																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_start.T		).sum(1		).softmax(0		)
																	lowercase_	:     List[str]													=       torch.matmul(q[i]				,							s_end.T		).sum(1		).softmax(0		)

																	if p_starts is not None:
																							lowercase_	:     Tuple													=       torch.vstack((p_starts, p_start)		)
																							lowercase_	:     Optional[Any]													=       torch.vstack((p_ends, p_end)		)
																	else:
																							lowercase_	:     str													=       p_start
																							lowercase_	:     int													=       p_end

											return p_starts, p_ends



