
'''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 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      def A_			(			self ):
          snake_case__						=				tempfile.mkdtemp()

          # fmt: off
          snake_case__						=				['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
          # fmt: on
          snake_case__						=				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] ) )

          snake_case__						=				{
              '''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],
          }
          snake_case__						=				os.path.join(self.tmpdirname		,	__snake_case )
          with open(self.image_processor_file		,	"w"		,	encoding="utf-8" ) as fp:
              json.dump(__snake_case		,	__snake_case )
      def A_			(			self		,	**lowerCamelCase ):
          return BertTokenizer.from_pretrained(self.tmpdirname		,	**__snake_case )
      def A_			(			self		,	**lowerCamelCase ):
          return ViTImageProcessor.from_pretrained(self.tmpdirname		,	**__snake_case )
      def A_			(			self ):
          shutil.rmtree(self.tmpdirname )
      def A_			(			self ):
          snake_case__						=				[np.random.randint(2_55		,	size=(3, 30, 4_00)		,	dtype=np.uinta )]

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

          return image_inputs
      def A_			(			self ):
          snake_case__						=				self.get_tokenizer()
          snake_case__						=				self.get_image_processor()

          snake_case__						=				VisionTextDualEncoderProcessor(tokenizer=__snake_case		,	image_processor=__snake_case )

          processor.save_pretrained(self.tmpdirname )
          snake_case__						=				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		,	__snake_case )
      def A_			(			self ):
          snake_case__						=				VisionTextDualEncoderProcessor(
              tokenizer=self.get_tokenizer()		,	image_processor=self.get_image_processor() )
          processor.save_pretrained(self.tmpdirname )

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

          snake_case__						=				VisionTextDualEncoderProcessor.from_pretrained(
              self.tmpdirname		,	bos_token="(BOS)"		,	eos_token="(EOS)"		,	do_normalize=__snake_case		,	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		,	__snake_case )
      def A_			(			self ):
          snake_case__						=				self.get_image_processor()
          snake_case__						=				self.get_tokenizer()

          snake_case__						=				VisionTextDualEncoderProcessor(tokenizer=__snake_case		,	image_processor=__snake_case )

          snake_case__						=				self.prepare_image_inputs()

          snake_case__						=				image_processor(__snake_case		,	return_tensors="np" )
          snake_case__						=				processor(images=__snake_case		,	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 A_			(			self ):
          snake_case__						=				self.get_image_processor()
          snake_case__						=				self.get_tokenizer()

          snake_case__						=				VisionTextDualEncoderProcessor(tokenizer=__snake_case		,	image_processor=__snake_case )

          snake_case__						=				'''lower newer'''

          snake_case__						=				processor(text=__snake_case )

          snake_case__						=				tokenizer(__snake_case )

          for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]		,	encoded_processor[key] )
      def A_			(			self ):
          snake_case__						=				self.get_image_processor()
          snake_case__						=				self.get_tokenizer()

          snake_case__						=				VisionTextDualEncoderProcessor(tokenizer=__snake_case		,	image_processor=__snake_case )

          snake_case__						=				'''lower newer'''
          snake_case__						=				self.prepare_image_inputs()

          snake_case__						=				processor(text=__snake_case		,	images=__snake_case )

          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(__snake_case ):
              processor()
      def A_			(			self ):
          snake_case__						=				self.get_image_processor()
          snake_case__						=				self.get_tokenizer()

          snake_case__						=				VisionTextDualEncoderProcessor(tokenizer=__snake_case		,	image_processor=__snake_case )

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

          snake_case__						=				processor.batch_decode(__snake_case )
          snake_case__						=				tokenizer.batch_decode(__snake_case )

          self.assertListEqual(__snake_case		,	__snake_case )



      def A_			(			self ):
          snake_case__						=				self.get_image_processor()
          snake_case__						=				self.get_tokenizer()

          snake_case__						=				VisionTextDualEncoderProcessor(tokenizer=__snake_case		,	image_processor=__snake_case )

          snake_case__						=				'''lower newer'''
          snake_case__						=				self.prepare_image_inputs()

          snake_case__						=				processor(text=__snake_case		,	images=__snake_case )

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





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

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


if is_sentencepiece_available():
     from .tokenization_pegasus import PegasusTokenizer
else:
     __magic_name__            =  None


__magic_name__            =  logging.get_logger(__name__)


__magic_name__            =  '''▁'''

__magic_name__            =  {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__magic_name__            =  {
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

__magic_name__            =  {
    '''google/pegasus-xsum''': 512,
}




class 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase ):
      _A						:		int    	=     VOCAB_FILES_NAMES
      _A						:		Dict    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		Any    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A						:		Tuple    	=     PegasusTokenizer
      _A						:		str    	=     ['input_ids', 'attention_mask']
      def __init__(			self		,	lowerCamelCase=None		,	lowerCamelCase=None		,	lowerCamelCase="<pad>"		,	lowerCamelCase="</s>"		,	lowerCamelCase="<unk>"		,	lowerCamelCase="<mask_2>"		,	lowerCamelCase="<mask_1>"		,	lowerCamelCase=None		,	lowerCamelCase=1_03		,	**lowerCamelCase		,	):
          snake_case__						=				offset

          if additional_special_tokens is not None:
              if not isinstance(lowerCamelCase		,	lowerCamelCase ):
                  raise TypeError(
                      F"""additional_special_tokens should be of type {type(lowerCamelCase )}, but is"""
                      F""" {type(lowerCamelCase )}""" )

              snake_case__						=				(
                  ([mask_token_sent] + additional_special_tokens)
                  if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                  else additional_special_tokens
              )
              # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
              additional_special_tokens_extended += [
                  F"""<unk_{i}>""" for i in range(len(lowerCamelCase )		,	self.offset - 1 )
              ]

              if len(set(lowerCamelCase ) ) != len(lowerCamelCase ):
                  raise ValueError(
                      "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
                      F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
              snake_case__						=				additional_special_tokens_extended
          else:
              snake_case__						=				[mask_token_sent] if mask_token_sent is not None else []
              additional_special_tokens += [F"""<unk_{i}>""" for i in range(2		,	self.offset )]

          super().__init__(
              lowerCamelCase		,	tokenizer_file=lowerCamelCase		,	pad_token=lowerCamelCase		,	eos_token=lowerCamelCase		,	unk_token=lowerCamelCase		,	mask_token=lowerCamelCase		,	mask_token_sent=lowerCamelCase		,	offset=lowerCamelCase		,	additional_special_tokens=lowerCamelCase		,	**lowerCamelCase		,	)
          snake_case__						=				vocab_file
          snake_case__						=				False if not self.vocab_file else True
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				set(self.all_special_ids )  # call it once instead of inside list comp
          all_special_ids.remove(self.unk_token_id )  # <unk> is only sometimes special

          if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
              raise ValueError(
                  "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
                  F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )

          return [1 if x in all_special_ids else 0 for x in seq]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None		,	lowerCamelCase = False ):
          if already_has_special_tokens:
              return self._special_token_mask(lowerCamelCase )
          elif token_ids_a is None:
              return self._special_token_mask(lowerCamelCase ) + [1]
          else:
              return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase=None ):
          if token_ids_a is None:
              return token_ids_a + [self.eos_token_id]
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return token_ids_a + token_ids_a + [self.eos_token_id]



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          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(lowerCamelCase ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          snake_case__						=				os.path.join(
              lowerCamelCase		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

          return (out_vocab_file,)



