
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
					from .tokenization_xlnet import XLNetTokenizer
else:
					_UpperCAmelCase					: int      =       None


_UpperCAmelCase					: Tuple      =       logging.get_logger(__name__)

_UpperCAmelCase					: Tuple      =       {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

_UpperCAmelCase					: Union[str, Any]      =       {
    """vocab_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json""",
        """xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json""",
    },
}

_UpperCAmelCase					: Optional[int]      =       {
    """xlnet-base-cased""": None,
    """xlnet-large-cased""": None,
}

_UpperCAmelCase					: Union[str, Any]      =       """▁"""

# Segments (not really needed)
_UpperCAmelCase					: str      =       0
_UpperCAmelCase					: Optional[int]      =       1
_UpperCAmelCase					: Tuple      =       2
_UpperCAmelCase					: Optional[Any]      =       3
_UpperCAmelCase					: List[Any]      =       4


class 				lowercase					(		UpperCAmelCase__   ):
		__lowercase       :       Any	     =				VOCAB_FILES_NAMES
		__lowercase       :       List[str]	     =				PRETRAINED_VOCAB_FILES_MAP
		__lowercase       :       Dict	     =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__lowercase       :       int	     =				"left"
		__lowercase       :       str	     =				XLNetTokenizer



		def __init__( self				,      A_=None				,      A_=None				,      A_=False				,      A_=True				,      A_=False				,      A_="<s>"				,      A_="</s>"				,      A_="<unk>"				,      A_="<sep>"				,      A_="<pad>"				,      A_="<cls>"				,      A_="<mask>"				,      A_=["<eop>", "<eod>"]				,      **A_				,      )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     AddedToken(_a				,      lstrip=_a				,      rstrip=_a       ) if isinstance(_a				,      _a       ) else mask_token

				super().__init__(
				    vocab_file=_a				,      tokenizer_file=_a				,      do_lower_case=_a				,      remove_space=_a				,      keep_accents=_a				,      bos_token=_a				,      eos_token=_a				,      unk_token=_a				,      sep_token=_a				,      pad_token=_a				,      cls_token=_a				,      mask_token=_a				,      additional_special_tokens=_a				,      **_a				,      )

				UpperCamelCase		=     3
				UpperCamelCase		=     do_lower_case
				UpperCamelCase		=     remove_space
				UpperCamelCase		=     keep_accents
				UpperCamelCase		=     vocab_file
				UpperCamelCase		=     False if not self.vocab_file else True



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     [self.sep_token_id]
				UpperCamelCase		=     [self.cls_token_id]
				if token_ids_a is None:
						return token_ids_a + sep + cls
				return token_ids_a + sep + token_ids_a + sep + cls



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     [self.sep_token_id]
				UpperCamelCase		=     [2]

				if token_ids_a is None:
						return len(token_ids_a + sep       ) * [0] + cls_segment_id
				return len(token_ids_a + sep       ) * [0] + len(token_ids_a + sep       ) * [1] + cls_segment_id



		def __UpperCamelCase ( self				,      A_				,      A_ = None       )  ->					Union[str, Any]:
				"""simple docstring"""
				if not self.can_save_slow_tokenizer:
						raise ValueError(
						    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
						    'tokenizer.'       )

				if not os.path.isdir(_a       ):
						logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
						return
				UpperCamelCase		=     os.path.join(
				    _a				,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

				if os.path.abspath(self.vocab_file       ) != os.path.abspath(_a       ):
						copyfile(self.vocab_file				,      _a       )

				return (out_vocab_file,)






"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					tempfile.mkdtemp()

							# fmt: off
							lowerCamelCase			      =					["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
							# fmt: on
							lowerCamelCase			      =					os.path.join(self.tmpdirname		, VOCAB_FILES_NAMES["""vocab_file"""] )
							with open(self.vocab_file		, """w"""		, encoding="""utf-8""" ) as vocab_writer:
										vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )

							lowerCamelCase			      =					{
							    """do_resize""": True,
							    """size""": {"""height""": 18, """width""": 18},
							    """do_normalize""": True,
							    """image_mean""": [0.5, 0.5, 0.5],
							    """image_std""": [0.5, 0.5, 0.5],
							}
							lowerCamelCase			      =					os.path.join(self.tmpdirname		, _a )
							with open(self.image_processor_file		, """w"""		, encoding="""utf-8""" ) as fp:
										json.dump(_a		, _a )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return BertTokenizer.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self		, **_a ):





							"""simple docstring"""
							return ViTImageProcessor.from_pretrained(self.tmpdirname		, **_a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							shutil.rmtree(self.tmpdirname )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					[np.random.randint(255		, size=(3, 30, 400)		, dtype=np.uinta )]

							lowerCamelCase			      =					[Image.fromarray(np.moveaxis(_a		, 0		, -1 ) ) for x in image_inputs]

							return image_inputs





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_tokenizer()
							lowerCamelCase			      =					self.get_image_processor()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							processor.save_pretrained(self.tmpdirname )
							lowerCamelCase			      =					VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname )

							self.assertEqual(processor.tokenizer.get_vocab()		, tokenizer.get_vocab() )
							self.assertIsInstance(processor.tokenizer		, (BertTokenizer, BertTokenizerFast) )

							self.assertEqual(processor.image_processor.to_json_string()		, image_processor.to_json_string() )
							self.assertIsInstance(processor.image_processor		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					VisionTextDualEncoderProcessor(
							    tokenizer=self.get_tokenizer()		, image_processor=self.get_image_processor() )
							processor.save_pretrained(self.tmpdirname )

							lowerCamelCase			      =					self.get_tokenizer(bos_token="""(BOS)"""		, eos_token="""(EOS)""" )
							lowerCamelCase			      =					self.get_image_processor(do_normalize=_a		, padding_value=1.0 )

							lowerCamelCase			      =					VisionTextDualEncoderProcessor.from_pretrained(
							    self.tmpdirname		, bos_token="""(BOS)"""		, eos_token="""(EOS)"""		, do_normalize=_a		, padding_value=1.0 )

							self.assertEqual(processor.tokenizer.get_vocab()		, tokenizer_add_kwargs.get_vocab() )
							self.assertIsInstance(processor.tokenizer		, (BertTokenizer, BertTokenizerFast) )

							self.assertEqual(processor.image_processor.to_json_string()		, image_processor_add_kwargs.to_json_string() )
							self.assertIsInstance(processor.image_processor		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					image_processor(_a		, return_tensors="""np""" )
							lowerCamelCase			      =					processor(images=_a		, return_tensors="""np""" )

							for key in input_feat_extract.keys():
										self.assertAlmostEqual(input_feat_extract[key].sum()		, input_processor[key].sum()		, delta=1e-2 )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""

							lowerCamelCase			      =					processor(text=_a )

							lowerCamelCase			      =					tokenizer(_a )

							for key in encoded_tok.keys():
										self.assertListEqual(encoded_tok[key]		, encoded_processor[key] )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""
							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					processor(text=_a		, images=_a )

							self.assertListEqual(list(inputs.keys() )		, ["""input_ids""", """token_type_ids""", """attention_mask""", """pixel_values"""] )

							# test if it raises when no input is passed
							with self.assertRaises(_a ):
										processor()





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							lowerCamelCase			      =					processor.batch_decode(_a )
							lowerCamelCase			      =					tokenizer.batch_decode(_a )

							self.assertListEqual(_a		, _a )





				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					self.get_image_processor()
							lowerCamelCase			      =					self.get_tokenizer()

							lowerCamelCase			      =					VisionTextDualEncoderProcessor(tokenizer=_a		, image_processor=_a )

							lowerCamelCase			      =					"""lower newer"""
							lowerCamelCase			      =					self.prepare_image_inputs()

							lowerCamelCase			      =					processor(text=_a		, images=_a )

							self.assertListEqual(list(inputs.keys() )		, processor.model_input_names )



