




from __future__ import annotations

from collections.abc import Sequence
from typing import Literal







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		list(snake_case_  )
      _A							:  List[Any]    =		list(snake_case_  )
      _A							:  Tuple    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count += 1
                  _A							:  Optional[Any]    =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(snake_case_  )







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  Optional[Any]    =		[]
      while True:
            _A							:  int    =		["""$"""] * len(snake_case_  )
            _A							:  Any    =		[]
            for i in range(len(snake_case_  )  ):
                  for j in range(i + 1,len(snake_case_  )  ):
                        _A							:  Tuple    =		compare_string(binary[i],binary[j]  )
                        if k is False:
                              _A							:  str    =		"""*"""
                              _A							:  str    =		"""*"""
                              temp.append("""X"""  )
            for i in range(len(snake_case_  )  ):
                  if checka[i] == "$":
                        pi.append(binary[i]  )
            if len(snake_case_  ) == 0:
                  return pi
            _A							:  Dict    =		list(set(snake_case_  )  )







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  List[str]    =		[]
      for minterm in minterms:
            _A							:  Tuple    =		""""""
            for _ in range(snake_case_  ):
                  _A							:  Optional[Any]    =		str(minterm % 2  ) + string
                  minterm //= 2
            temp.append(snake_case_  )
      return temp







def        lowerCAmelCase_			(  snake_case_,snake_case_,snake_case_  ):
      _A							:  Dict    =		list(snake_case_  )
      _A							:  Tuple    =		list(snake_case_  )
      _A							:  Dict    =		0
      for i in range(len(snake_case_  )  ):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[]
      _A							:  str    =		[0] * len(snake_case_  )
      for i in range(len(chart[0]  )  ):
            _A							:  Union[str, Any]    =		0
            _A							:  Optional[Any]    =		-1
            for j in range(len(snake_case_  )  ):
                  if chart[j][i] == 1:
                        count += 1
                        _A							:  Dict    =		j
            if count == 1:
                  _A							:  int    =		1
      for i in range(len(snake_case_  )  ):
            if select[i] == 1:
                  for j in range(len(chart[0]  )  ):
                        if chart[i][j] == 1:
                              for k in range(len(snake_case_  )  ):
                                    _A							:  int    =		0
                  temp.append(prime_implicants[i]  )
      while True:
            _A							:  Optional[Any]    =		0
            _A							:  Tuple    =		-1
            _A							:  List[Any]    =		0
            for i in range(len(snake_case_  )  ):
                  _A							:  List[str]    =		chart[i].count(1  )
                  if count_n > max_n:
                        _A							:  Optional[int]    =		count_n
                        _A							:  Tuple    =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]  )

            for i in range(len(chart[0]  )  ):
                  if chart[rem][i] == 1:
                        for j in range(len(snake_case_  )  ):
                              _A							:  Optional[int]    =		0







def        lowerCAmelCase_			(  snake_case_,snake_case_  ):
      _A							:  Optional[int]    =		[[0 for x in range(len(snake_case_  )  )] for x in range(len(snake_case_  )  )]
      for i in range(len(snake_case_  )  ):
            _A							:  List[Any]    =		prime_implicants[i].count("""_"""  )
            for j in range(len(snake_case_  )  ):
                  if is_for_table(prime_implicants[i],binary[j],snake_case_  ):
                        _A							:  Union[str, Any]    =		1

      return chart







def        lowerCAmelCase_			(  ):
      _A							:  Dict    =		int(input("""Enter the no. of variables\n"""  )  )
      _A							:  Dict    =		[
          float(snake_case_  )
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""  ).split()
      ]
      _A							:  int    =		decimal_to_binary(snake_case_,snake_case_  )

      _A							:  Optional[Any]    =		check(snake_case_  )
      print("""Prime Implicants are:"""  )
      print(snake_case_  )
      _A							:  int    =		prime_implicant_chart(snake_case_,snake_case_  )

      _A							:  int    =		selection(snake_case_,snake_case_  )
      print("""Essential Prime Implicants are:"""  )
      print(snake_case_  )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()







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 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		CLIPTokenizer
     _a											=		CLIPTokenizerFast
     _a											=		True
     _a											=		{}
     _a											=		False

     def       a__			(						self       )		->							Optional[Any]:
           super().setUp()

           # fmt: off
           _A							:  int    =		["""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
           _A							:  str    =		dict(zip(_a	,  range(len(_a       )       )       )       )
           _A							:  Optional[int]    =		["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>"""]
           _A							:  str    =		{"""unk_token""": """<unk>"""}

           _A							:  Union[str, Any]    =		os.path.join(self.tmpdirname	,  VOCAB_FILES_NAMES["""vocab_file"""]       )
           _A							:  Union[str, 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(_a       ) + """\n"""       )
           with open(self.merges_file	,  """w"""	,  encoding="""utf-8"""       ) as fp:
                 fp.write("""\n""".join(_a       )       )

     def       a__			(						self	,  **_a       )		->							List[Any]:
           kwargs.update(self.special_tokens_map       )
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  **_a       )		->							Union[str, Any]:
           kwargs.update(self.special_tokens_map       )
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,  **_a       )

     def       a__			(						self	,  _a       )		->							str:
           _A							:  Tuple    =		"""lower newer"""
           _A							:  Optional[Any]    =		"""lower newer"""
           return input_text, output_text

     def       a__			(						self       )		->							List[Any]:
           _A							:  Optional[int]    =		CLIPTokenizer(self.vocab_file	,  self.merges_file	,  **self.special_tokens_map       )
           _A							:  str    =		"""lower newer"""
           _A							:  List[str]    =		["""lo""", """w""", """er</w>""", """n""", """e""", """w""", """er</w>"""]
           _A							:  int    =		tokenizer.tokenize(_a       )
           self.assertListEqual(_a	,  _a       )

           _A							:  str    =		tokens + [tokenizer.unk_token]
           _A							:  Dict    =		[10, 2, 16, 9, 3, 2, 16, 20]
           self.assertListEqual(tokenizer.convert_tokens_to_ids(_a       )	,  _a       )

     @require_ftfy
     def       a__			(						self       )		->							Tuple:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                       _A							:  Dict    =		self.tokenizer_class.from_pretrained(_a	,  **_a       )
                       _A							:  str    =		self.rust_tokenizer_class.from_pretrained(_a	,  **_a       )

                       _A							:  List[str]    =		"""A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."""
                       _A							:  Dict    =		tokenizer_s.tokenize(_a       )
                       _A							:  Dict    =		tokenizer_r.tokenize(_a       )

                       self.assertListEqual(_a	,  _a       )

                       # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                       # with Tilde) encoded in 2 different ways
                       _A							:  Tuple    =		"""xa\u0303y""" + """ """ + """x\xe3y"""
                       _A							:  Dict    =		tokenizer_s.tokenize(_a       )
                       _A							:  Dict    =		tokenizer_r.tokenize(_a       )

                       self.assertListEqual(_a	,  _a       )

                       # Test that the tokenization is identical on unicode of space type
                       _A							:  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:
                             _A							:  Optional[int]    =		tokenizer_s.tokenize(_a       )
                             _A							:  List[Any]    =		tokenizer_r.tokenize(_a       )

                             self.assertListEqual(_a	,  _a       )

                       # Test that the tokenization is identical on unicode of line break type
                       _A							:  int    =		[
                           """\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:
                             _A							:  Optional[Any]    =		tokenizer_s.tokenize(_a       )
                             _A							:  Tuple    =		tokenizer_r.tokenize(_a       )

                             self.assertListEqual(_a	,  _a       )

     def       a__			(						self       )		->							Any:
           # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
                       _A							:  List[Any]    =		"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
                       _A							:  str    =		F'''{text_of_1_token} {text_of_1_token}'''

                       _A							:  str    =		self.rust_tokenizer_class.from_pretrained(
                           _a	,  use_fast=_a	,  )
                       _A							:  Dict    =		tokenizer_r(_a	,  return_offsets_mapping=_a	,  add_special_tokens=_a       )
                       self.assertEqual(encoding.offset_mapping[0]	,  (0, len(_a       ))       )
                       self.assertEqual(
                           encoding.offset_mapping[1]	,  (len(_a       ) + 1, len(_a       ) + 1 + len(_a       ))	,  )

                       _A							:  Any    =		F''' {text}'''

                       _A							:  List[Any]    =		self.rust_tokenizer_class.from_pretrained(
                           _a	,  use_fast=_a	,  )
                       _A							:  Optional[int]    =		tokenizer_r(_a	,  return_offsets_mapping=_a	,  add_special_tokens=_a       )
                       self.assertEqual(encoding.offset_mapping[0]	,  (1, 1 + len(_a       ))       )
                       self.assertEqual(
                           encoding.offset_mapping[1]	,  (1 + len(_a       ) + 1, 1 + len(_a       ) + 1 + len(_a       ))	,  )

     def       a__			(						self       )		->							List[Any]:
           # Test related to the breaking change introduced in transformers v4.17.0
           # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
           with self.assertRaises(_a       ) 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       a__			(						self       )		->							str:
           super().test_tokenization_python_rust_equals()



     def       a__			(						self       )		->							Union[str, Any]:
           # CLIP always lower cases letters
           pass

