

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   _a					(			snake_case_     ,				unittest.TestCase				):




      """simple docstring"""

      _lowerCamelCase       :			Union[str, Any]      		=       CLIPTokenizer
      _lowerCamelCase       :			Optional[int]      		=       CLIPTokenizerFast
      _lowerCamelCase       :			Optional[int]      		=       True
      _lowerCamelCase       :			Tuple      		=       {}
      _lowerCamelCase       :			Optional[Any]      		=       False





      def 		__A	(   self							:				int	):
            super().setUp()

            # fmt: off
            A_						  =				["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_						  =				dict(zip(UpperCAmelCase						,			range(len(UpperCAmelCase	)	)	)	)
            A_						  =				["#version: 0.2", "l o", "lo w</w>", "e r</w>"]
            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(UpperCAmelCase	) + "\n"	)
            with open(self.merges_file						,			"w"						,			encoding="utf-8"	) as fp:
                  fp.write("\n".join(UpperCAmelCase	)	)





      def 		__A	(   self							:				Union[str, Any]						,			**UpperCAmelCase							:				str	):
            kwargs.update(self.special_tokens_map	)
            return CLIPTokenizer.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				Tuple						,			**UpperCAmelCase							:				List[str]	):
            kwargs.update(self.special_tokens_map	)
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,			**UpperCAmelCase	)





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Union[str, Any]	):
            A_						  =				"lower newer"
            A_						  =				"lower newer"
            return input_text, output_text





      def 		__A	(   self							:				Optional[int]	):
            A_						  =				CLIPTokenizer(self.vocab_file						,			self.merges_file						,			**self.special_tokens_map	)
            A_						  =				"lower newer"
            A_						  =				["lo", "w", "er</w>", "n", "e", "w", "er</w>"]
            A_						  =				tokenizer.tokenize(UpperCAmelCase	)
            self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

            A_						  =				tokens + [tokenizer.unk_token]
            A_						  =				[10, 2, 16, 9, 3, 2, 16, 20]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase	)						,			UpperCAmelCase	)





      @require_ftfy
      def 		__A	(   self							:				Optional[Any]	):
            for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''	):
                        A_						  =				self.tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)
                        A_						  =				self.rust_tokenizer_class.from_pretrained(UpperCAmelCase						,			**UpperCAmelCase	)

                        A_						  =				"A\n'll 11p223RF☆ho!!to?'d'd''d of a cat to-$''d."
                        A_						  =				tokenizer_s.tokenize(UpperCAmelCase	)
                        A_						  =				tokenizer_r.tokenize(UpperCAmelCase	)

                        self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

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

                        self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

                        # Test that the tokenization is identical on unicode of space type
                        A_						  =				[
                            "\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_						  =				tokenizer_s.tokenize(UpperCAmelCase	)
                              A_						  =				tokenizer_r.tokenize(UpperCAmelCase	)

                              self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)

                        # Test that the tokenization is identical on unicode of line break type
                        A_						  =				[
                            "\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_						  =				tokenizer_s.tokenize(UpperCAmelCase	)
                              A_						  =				tokenizer_r.tokenize(UpperCAmelCase	)

                              self.assertListEqual(UpperCAmelCase						,			UpperCAmelCase	)





      def 		__A	(   self							:				Optional[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_						  =				"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                        A_						  =				f'''{text_of_1_token} {text_of_1_token}'''

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(0, len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(len(UpperCAmelCase	) + 1, len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)

                        A_						  =				f''' {text}'''

                        A_						  =				self.rust_tokenizer_class.from_pretrained(
                            UpperCAmelCase						,			use_fast=UpperCAmelCase						,			)
                        A_						  =				tokenizer_r(UpperCAmelCase						,			return_offsets_mapping=UpperCAmelCase						,			add_special_tokens=UpperCAmelCase	)
                        self.assertEqual(encoding.offset_mapping[0]						,			(1, 1 + len(UpperCAmelCase	))	)
                        self.assertEqual(
                            encoding.offset_mapping[1]						,			(1 + len(UpperCAmelCase	) + 1, 1 + len(UpperCAmelCase	) + 1 + len(UpperCAmelCase	))						,			)





      def 		__A	(   self							:				List[str]	):
            # 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(UpperCAmelCase	) 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							:				Dict	):
            super().test_tokenization_python_rust_equals()







      def 		__A	(   self							:				List[Any]	):
            # CLIP always lower cases letters
            pass





def lowercase    (  SCREAMING_SNAKE_CASE )			->							int:


			'''simple docstring'''







			if not numbers:
						return 0

			if not isinstance(SCREAMING_SNAKE_CASE       ,    (list, tuple) ) or not all(
			    isinstance(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE ) for number in numbers ):
						raise ValueError('numbers must be an iterable of integers' )

			SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						SCREAMING_SNAKE_CASE_								=						numbers[0]

			for i in range(1       ,    len(SCREAMING_SNAKE_CASE ) ):
						# update the maximum and minimum subarray products
						SCREAMING_SNAKE_CASE_								=						numbers[i]
						if number < 0:
									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_								=						min_till_now, max_till_now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    max_till_now * number )
						SCREAMING_SNAKE_CASE_								=						min(SCREAMING_SNAKE_CASE       ,    min_till_now * number )

						# update the maximum product found till now
						SCREAMING_SNAKE_CASE_								=						max(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			return max_prod
