






"""simple docstring"""




from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 	__UpperCAmelCase		(				_lowerCamelCase	):


	'''simple docstring'''





	lowercase		:	Union[str, Any]							     =				["image_processor", "tokenizer"]
	lowercase		:	Union[str, Any]							     =				"Pix2StructImageProcessor"
	lowercase		:	Optional[Any]							     =				("T5Tokenizer", "T5TokenizerFast")


	def __init__(     self      ,      _A      ,      _A					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=False
		super().__init__(_A      ,      _A					)


	def __call__(     self      ,      _A=None      ,      _A = None      ,      _A = True      ,      _A = False      ,      _A = None      ,      _A = None      ,      _A = 2_0_4_8      ,      _A = 0      ,      _A = None      ,      _A = None      ,      _A = False      ,      _A = False      ,      _A = False      ,      _A = False      ,      _A = False      ,      _A = True      ,      _A = None      ,      **_A      ,      ):



		'''simple docstring'''

		if images is None and text is None:
			raise ValueError('''You have to specify either images or text.'''					)

		# Get only text
		if images is None and not self.image_processor.is_vqa:
			_SCREAMING_SNAKE_CASE						=self.tokenizer
			_SCREAMING_SNAKE_CASE						=self.tokenizer(
			    text=_A      ,      add_special_tokens=_A      ,      padding=_A      ,      truncation=_A      ,      max_length=_A      ,      stride=_A      ,      pad_to_multiple_of=_A      ,      return_attention_mask=_A      ,      return_overflowing_tokens=_A      ,      return_special_tokens_mask=_A      ,      return_offsets_mapping=_A      ,      return_token_type_ids=_A      ,      return_length=_A      ,      verbose=_A      ,      return_tensors=_A      ,      **_A      ,      )
			return text_encoding

		if not self.image_processor.is_vqa:
			# add pixel_values
			_SCREAMING_SNAKE_CASE						=self.image_processor(
			    _A      ,      return_tensors=_A      ,      max_patches=_A      ,      **_A					)
		else:
			# add pixel_values and bbox
			_SCREAMING_SNAKE_CASE						=self.image_processor(
			    _A      ,      return_tensors=_A      ,      max_patches=_A      ,      header_text=_A      ,      **_A					)

		if text is not None and not self.image_processor.is_vqa:
			_SCREAMING_SNAKE_CASE						=self.tokenizer(
			    text=_A      ,      add_special_tokens=_A      ,      padding=_A      ,      truncation=_A      ,      max_length=_A      ,      stride=_A      ,      pad_to_multiple_of=_A      ,      return_attention_mask=_A      ,      return_overflowing_tokens=_A      ,      return_special_tokens_mask=_A      ,      return_offsets_mapping=_A      ,      return_token_type_ids=_A      ,      return_length=_A      ,      verbose=_A      ,      return_tensors=_A      ,      **_A      ,      )

			if "attention_mask" in text_encoding:
				_SCREAMING_SNAKE_CASE						=text_encoding.pop('''attention_mask'''					)
			if "input_ids" in text_encoding:
				_SCREAMING_SNAKE_CASE						=text_encoding.pop('''input_ids'''					)
		else:
			_SCREAMING_SNAKE_CASE						=None

		if text_encoding is not None:
			encoding_image_processor.update(_A					)

		return encoding_image_processor


	def 				UpperCamelCase_  (     self      ,      *_A      ,      **_A					):



		'''simple docstring'''

		return self.tokenizer.batch_decode(*_A      ,      **_A					)


	def 				UpperCamelCase_  (     self      ,      *_A      ,      **_A					):



		'''simple docstring'''

		return self.tokenizer.decode(*_A      ,      **_A					)







	@property
	def 				UpperCamelCase_  (     self					):



		'''simple docstring'''

		_SCREAMING_SNAKE_CASE						=self.tokenizer.model_input_names
		_SCREAMING_SNAKE_CASE						=self.image_processor.model_input_names
		return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

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


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor





@require_vision
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()

        _lowercase      			=[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            '的',
            '价',
            '格',
            '是',
            '15',
            '便',
            'alex',
            '##andra',
            '，',
            '。',
            '-',
            't',
            'shirt',
        ]
        _lowercase      			=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]   )   )

        _lowercase      			={
            'do_resize': True,
            'size': {'height': 224, 'width': 224},
            'do_center_crop': True,
            'crop_size': {'height': 18, 'width': 18},
            'do_normalize': True,
            'image_mean': [0.48145466, 0.4578275, 0.40821073],
            'image_std': [0.26862954, 0.26130258, 0.27577711],
            'do_convert_rgb': True,
        }
        _lowercase      			=os.path.join(self.tmpdirname				,  lowerCAmelCase   )
        with open(self.image_processor_file				,  'w'				,  encoding='utf-8'   ) as fp:
              json.dump(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        return BertTokenizer.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        return BertTokenizerFast.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=[np.random.randint(255				,  size=(3, 30, 400)				,  dtype=np.uinta   )]

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

        return image_inputs




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_tokenizer()
        _lowercase      			=self.get_rust_tokenizer()
        _lowercase      			=self.get_image_processor()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_slow.save_pretrained(self.tmpdirname   )
        _lowercase      			=ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,  use_fast=lowerCAmelCase   )

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_fast.save_pretrained(self.tmpdirname   )
        _lowercase      			=ChineseCLIPProcessor.from_pretrained(self.tmpdirname   )

        self.assertEqual(processor_slow.tokenizer.get_vocab()				,  tokenizer_slow.get_vocab()   )
        self.assertEqual(processor_fast.tokenizer.get_vocab()				,  tokenizer_fast.get_vocab()   )
        self.assertEqual(tokenizer_slow.get_vocab()				,  tokenizer_fast.get_vocab()   )
        self.assertIsInstance(processor_slow.tokenizer				,  lowerCAmelCase   )
        self.assertIsInstance(processor_fast.tokenizer				,  lowerCAmelCase   )

        self.assertEqual(processor_slow.image_processor.to_json_string()				,  image_processor.to_json_string()   )
        self.assertEqual(processor_fast.image_processor.to_json_string()				,  image_processor.to_json_string()   )
        self.assertIsInstance(processor_slow.image_processor				,  lowerCAmelCase   )
        self.assertIsInstance(processor_fast.image_processor				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,  image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        _lowercase      			=self.get_tokenizer(cls_token='(CLS)'				,  sep_token='(SEP)'   )
        _lowercase      			=self.get_image_processor(do_normalize=lowerCAmelCase   )

        _lowercase      			=ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname				,  cls_token='(CLS)'				,  sep_token='(SEP)'				,  do_normalize=lowerCAmelCase   )

        self.assertEqual(processor.tokenizer.get_vocab()				,  tokenizer_add_kwargs.get_vocab()   )
        self.assertIsInstance(processor.tokenizer				,  lowerCAmelCase   )

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




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=image_processor(lowerCAmelCase				,  return_tensors='np'   )
        _lowercase      			=processor(images=lowerCAmelCase				,  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   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'

        _lowercase      			=processor(text=lowerCAmelCase   )

        _lowercase      			=tokenizer(lowerCAmelCase   )

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




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'
        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=processor(text=lowerCAmelCase				,  images=lowerCAmelCase   )

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

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase   ):
              processor()




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

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

        _lowercase      			=processor.batch_decode(lowerCAmelCase   )
        _lowercase      			=tokenizer.batch_decode(lowerCAmelCase   )

        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'
        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=processor(text=lowerCAmelCase				,  images=lowerCAmelCase   )

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


