



'''simple docstring'''





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	):
  """simple docstring"""




  def 		_lowercase	(					self :      Any  )   ->   Any:
    __lowerCamelCase			:			Any       			=					tempfile.mkdtemp()

    __lowerCamelCase			:			Optional[int]       			=					[
        '[UNK]',
        '[CLS]',
        '[SEP]',
        '[PAD]',
        '[MASK]',
        '的',
        '价',
        '格',
        '是',
        '15',
        '便',
        'alex',
        '##andra',
        '，',
        '。',
        '-',
        't',
        'shirt',
    ]
    __lowerCamelCase			:			List[Any]       			=					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			:			List[Any]       			=					{
        '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,
    }
    __lowerCamelCase			:			List[str]       			=					os.path.join(self.tmpdirname     ,      _a  )
    with open(self.image_processor_file     ,      'w'     ,      encoding='utf-8'  ) as fp:
      json.dump(_a     ,      _a  )




  def 		_lowercase	(					self :      Tuple     ,      **_a :      Union[str, Any]  )   ->   Any:
    return BertTokenizer.from_pretrained(self.tmpdirname     ,      **_a  )




  def 		_lowercase	(					self :      Dict     ,      **_a :      Optional[int]  )   ->   Optional[int]:
    return BertTokenizerFast.from_pretrained(self.tmpdirname     ,      **_a  )




  def 		_lowercase	(					self :      Optional[Any]     ,      **_a :      int  )   ->   Optional[int]:
    return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,      **_a  )




  def 		_lowercase	(					self :      Tuple  )   ->   str:
    shutil.rmtree(self.tmpdirname  )




  def 		_lowercase	(					self :      int  )   ->   Tuple:
    __lowerCamelCase			:			Tuple       			=					[np.random.randint(255     ,      size=(3, 30, 400)     ,      dtype=np.uinta  )]

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

    return image_inputs




  def 		_lowercase	(					self :      Any  )   ->   int:
    __lowerCamelCase			:			str       			=					self.get_tokenizer()
    __lowerCamelCase			:			Dict       			=					self.get_rust_tokenizer()
    __lowerCamelCase			:			int       			=					self.get_image_processor()

    __lowerCamelCase			:			Optional[int]       			=					ChineseCLIPProcessor(tokenizer=_a     ,      image_processor=_a  )
    processor_slow.save_pretrained(self.tmpdirname  )
    __lowerCamelCase			:			str       			=					ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,      use_fast=_a  )

    __lowerCamelCase			:			Union[str, Any]       			=					ChineseCLIPProcessor(tokenizer=_a     ,      image_processor=_a  )
    processor_fast.save_pretrained(self.tmpdirname  )
    __lowerCamelCase			:			Optional[Any]       			=					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     ,      _a  )
    self.assertIsInstance(processor_fast.tokenizer     ,      _a  )

    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     ,      _a  )
    self.assertIsInstance(processor_fast.image_processor     ,      _a  )




  def 		_lowercase	(					self :      List[str]  )   ->   int:
    __lowerCamelCase			:			Any       			=					ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,      image_processor=self.get_image_processor()  )
    processor.save_pretrained(self.tmpdirname  )

    __lowerCamelCase			:			str       			=					self.get_tokenizer(cls_token='(CLS)'     ,      sep_token='(SEP)'  )
    __lowerCamelCase			:			List[Any]       			=					self.get_image_processor(do_normalize=_a  )

    __lowerCamelCase			:			Any       			=					ChineseCLIPProcessor.from_pretrained(
        self.tmpdirname     ,      cls_token='(CLS)'     ,      sep_token='(SEP)'     ,      do_normalize=_a  )

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

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




  def 		_lowercase	(					self :      Dict  )   ->   Dict:
    __lowerCamelCase			:			Dict       			=					self.get_image_processor()
    __lowerCamelCase			:			Optional[int]       			=					self.get_tokenizer()

    __lowerCamelCase			:			List[Any]       			=					ChineseCLIPProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			int       			=					self.prepare_image_inputs()

    __lowerCamelCase			:			List[Any]       			=					image_processor(_a     ,      return_tensors='np'  )
    __lowerCamelCase			:			Optional[int]       			=					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 		_lowercase	(					self :      Dict  )   ->   Optional[Any]:
    __lowerCamelCase			:			str       			=					self.get_image_processor()
    __lowerCamelCase			:			Union[str, Any]       			=					self.get_tokenizer()

    __lowerCamelCase			:			int       			=					ChineseCLIPProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			str       			=					'Alexandra，T-shirt的价格是15便士。'

    __lowerCamelCase			:			List[str]       			=					processor(text=_a  )

    __lowerCamelCase			:			Any       			=					tokenizer(_a  )

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




  def 		_lowercase	(					self :      Optional[int]  )   ->   Dict:
    __lowerCamelCase			:			List[str]       			=					self.get_image_processor()
    __lowerCamelCase			:			Optional[Any]       			=					self.get_tokenizer()

    __lowerCamelCase			:			Optional[Any]       			=					ChineseCLIPProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			Optional[Any]       			=					'Alexandra，T-shirt的价格是15便士。'
    __lowerCamelCase			:			int       			=					self.prepare_image_inputs()

    __lowerCamelCase			:			Any       			=					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 pytest.raises(_a  ):
      processor()




  def 		_lowercase	(					self :      Optional[int]  )   ->   Optional[Any]:
    __lowerCamelCase			:			List[str]       			=					self.get_image_processor()
    __lowerCamelCase			:			List[str]       			=					self.get_tokenizer()

    __lowerCamelCase			:			List[Any]       			=					ChineseCLIPProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			Optional[int]       			=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

    __lowerCamelCase			:			Tuple       			=					processor.batch_decode(_a  )
    __lowerCamelCase			:			List[str]       			=					tokenizer.batch_decode(_a  )

    self.assertListEqual(_a     ,      _a  )




  def 		_lowercase	(					self :      Optional[Any]  )   ->   Optional[Any]:
    __lowerCamelCase			:			Tuple       			=					self.get_image_processor()
    __lowerCamelCase			:			int       			=					self.get_tokenizer()

    __lowerCamelCase			:			Any       			=					ChineseCLIPProcessor(tokenizer=_a     ,      image_processor=_a  )

    __lowerCamelCase			:			Tuple       			=					'Alexandra，T-shirt的价格是15便士。'
    __lowerCamelCase			:			Any       			=					self.prepare_image_inputs()

    __lowerCamelCase			:			Union[str, Any]       			=					processor(text=_a     ,      images=_a  )

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











'''simple docstring'''





# Algorithm for the pigeonhole sorting



def 			a_	(				_lowerCAmelCase			) -> List[str]:
  __lowerCamelCase			:			int       			=					min(_lowerCAmelCase			)  # min() finds the minimum value
  __lowerCamelCase			:			List[Any]       			=					max(_lowerCAmelCase			)  # max() finds the maximum value

  __lowerCamelCase			:			List[Any]       			=					max_val - min_val + 1  # size is difference of max and min values plus one

  # list of pigeonholes of size equal to the variable size
  __lowerCamelCase			:			List[Any]       			=					[0] * size

  # Populate the pigeonholes.
  for x in a:
    assert isinstance(_lowerCAmelCase			,_lowerCAmelCase			), "integers only please"
    holes[x - min_val] += 1

  # Putting the elements back into the array in an order.
  __lowerCamelCase			:			Tuple       			=					0
  for count in range(_lowerCAmelCase			):
    while holes[count] > 0:
      holes[count] -= 1
      __lowerCamelCase			:			List[Any]       			=					count + min_val
      i += 1

def 			a_	(				) -> str:
  __lowerCamelCase			:			Optional[Any]       			=					[8, 3, 2, 7, 4, 6, 8]
  pigeonhole_sort(_lowerCAmelCase			)
  print('Sorted order is:'			,' '.join(_lowerCAmelCase			)			)


if __name__ == "__main__":
   main()






