from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case__						      =			logging.get_logger(__name__)

snake_case__						      =			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case__						      =			{
    'vocab_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json',
    },
    'merges_file': {
        'openai/clip-vit-base-patch32': 'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'openai/clip-vit-base-patch32': (
            'https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json'
        ),
    },
}

snake_case__						      =			{
    'openai/clip-vit-base-patch32': 77,
}



class 			UpperCamelCase					(			snake_case__			):

     '''simple docstring'''

     A_					    =	VOCAB_FILES_NAMES
     A_					    =	PRETRAINED_VOCAB_FILES_MAP
     A_					    =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A_					    =	['input_ids', 'attention_mask']
     A_					    =	CLIPTokenizer



     def __init__(       self					,					A_=None					,					A_=None					,					A_=None					,					A_="<|endoftext|>"					,					A_="<|startoftext|>"					,					A_="<|endoftext|>"					,					A_="<|endoftext|>"					,					**A_					,					)			->					List[Any]:






           """simple docstring"""
           super().__init__(
               A_					,					A_					,					tokenizer_file=A_					,					unk_token=A_					,					bos_token=A_					,					eos_token=A_					,					pad_token=A_					,					**A_					,					)

           if not isinstance(self.backend_tokenizer.pre_tokenizer					,					pre_tokenizers.Sequence    ):
                 raise ValueError(
                     '''The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'''
                     ''' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'''
                     ''' to be compatible with this version.The easiest way to do so is'''
                     ''' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'''
                     ''' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'''
                     ''' transformers.'''    )

           self._wrap_decode_method_backend_tokenizer()



     def 		UpperCamelCase_    (       self    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					self.backend_tokenizer.decode

           def new_decode_method(*A_					,					**A_    ):
                 _lowerCamelCase           =					orig_decode_method(*A_					,					**A_    )
                 _lowerCamelCase           =					text.replace(self.backend_tokenizer.model.end_of_word_suffix					,					''' '''    ).strip()
                 return text

           _lowerCamelCase           =					new_decode_method



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					[self.bos_token_id]
           _lowerCamelCase           =					[self.eos_token_id]

           if token_ids_a is None:
                 return bos_token + token_ids_a + eos_token
           return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token



     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					str:






           """simple docstring"""
           _lowerCamelCase           =					[self.bos_token_id]
           _lowerCamelCase           =					[self.eos_token_id]

           if token_ids_a is None:
                 return len(bos_token + token_ids_a + eos_token    ) * [0]
           return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token    ) * [0]





     def 		UpperCamelCase_    (       self					,					A_					,					A_ = None    )			->					Dict:






           """simple docstring"""
           _lowerCamelCase           =					self._tokenizer.model.save(A_					,					name=A_    )
           return tuple(A_    )







import json
import os
import unittest

from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
    VOCAB_FILES_NAMES,
    GPTNeoXJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class __lowerCamelCase					( snake_case__	,			unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								= GPTNeoXJapaneseTokenizer
	UpperCamelCase__								= False
	UpperCamelCase__								= {"do_clean_text": False, "add_prefix_space": False}

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							super().setUp()

							_UpperCAmelCase							   =     [
							    'こん',
							    'こんに',
							    'にちは',
							    'ばんは',
							    '世界,㔺界',
							    '、',
							    '。',
							    '<BR>',
							    '<SP>',
							    '<TAB>',
							    '<URL>',
							    '<EMAIL>',
							    '<TEL>',
							    '<DATE>',
							    '<PRICE>',
							    '<BLOCK>',
							    '<KIGOU>',
							    '<U2000U2BFF>',
							    '<|emoji1|>',
							    '<unk>',
							    '<|startoftext|>',
							    '<|endoftext|>',
							]
							_UpperCAmelCase							   =     {'emoji': {'\ud83d\ude00': '<|emoji1|>'}, 'emoji_inv': {'<|emoji1|>': '\ud83d\ude00'}}  # 😀
							_UpperCAmelCase							   =     {'unk_token': '<unk>'}

							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['vocab_file']       )
							_UpperCAmelCase							   =     os.path.join(self.tmpdirname			,       VOCAB_FILES_NAMES['emoji_file']       )
							with open(self.vocab_file			,       'w'			,       encoding='utf-8'       ) as vocab_writer:
													vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]       )       )
							with open(self.emoji_file			,       'w'       ) as emoji_writer:
													emoji_writer.write(json.dumps(UpperCAmelCase       )       )

	def 			UpperCamelCase       (				self			,       **UpperCAmelCase       ):
							"""simple docstring"""







							kwargs.update(self.special_tokens_map       )
							return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname			,       **UpperCAmelCase       )

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、㔺界。😀'
							_UpperCAmelCase							   =     'こんにちは、世界。 \nこんばんは、世界。😀'
							return input_text, output_text

	def 			UpperCamelCase       (				self			,       UpperCAmelCase       ):
							"""simple docstring"""







							_UpperCAmelCase   ,					_UpperCAmelCase							   =     self.get_input_output_texts(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode(UpperCAmelCase			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.decode(UpperCAmelCase			,       clean_up_tokenization_spaces=UpperCAmelCase       )
							return text, ids

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass  # TODO add if relevant

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_tokenizer()

							# Testing tokenization
							_UpperCAmelCase							   =     'こんにちは、世界。　こんばんは、㔺界。'
							_UpperCAmelCase							   =     ['こん', 'にちは', '、', '世界', '。', '<SP>', 'こん', 'ばんは', '、', '㔺界', '。']
							_UpperCAmelCase							   =     tokenizer.tokenize(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids without special tokens
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

							# Testing conversion to ids with special tokens
							_UpperCAmelCase							   =     tokens + [tokenizer.unk_token]
							_UpperCAmelCase							   =     [0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 19]
							_UpperCAmelCase							   =     tokenizer.convert_tokens_to_ids(UpperCAmelCase       )
							self.assertListEqual(UpperCAmelCase			,       UpperCAmelCase       )

	@slow
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.tokenizer_class.from_pretrained('abeja/gpt-neox-japanese-2.7b'       )

							_UpperCAmelCase							   =     tokenizer.encode('ありがとう。'			,       add_special_tokens=UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.encode('どういたしまして。'			,       add_special_tokens=UpperCAmelCase       )

							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase       )
							_UpperCAmelCase							   =     tokenizer.build_inputs_with_special_tokens(UpperCAmelCase			,       UpperCAmelCase       )

							assert encoded_sentence == ids_a
							assert encoded_pair == ids_a + ids_a

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							pass
