


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 A    (  unittest.TestCase	):






      def 			lowerCamelCase			(   self		:	int					)   ->		List[Any]:
             """simple docstring"""




             _lowerCamelCase			:						int            =tempfile.mkdtemp()

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






      def 			lowerCamelCase			(   self		:	Tuple     ,  **lowercase_		:	str					)   ->		Optional[int]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  **lowercase_		:	List[str]					)   ->		Union[str, Any]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	Optional[int]     ,  **lowercase_		:	Union[str, Any]					)   ->		Optional[Any]:
             """simple docstring"""




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






      def 			lowerCamelCase			(   self		:	str					)   ->		Union[str, Any]:
             """simple docstring"""




             shutil.rmtree(self.tmpdirname					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[Any]:
             """simple docstring"""




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

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

             return image_inputs






      def 			lowerCamelCase			(   self		:	Any					)   ->		Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =self.get_tokenizer()
             _lowerCamelCase			:						str            =self.get_rust_tokenizer()
             _lowerCamelCase			:						Optional[Any]            =self.get_image_processor()

             _lowerCamelCase			:						Dict            =ChineseCLIPProcessor(tokenizer=lowercase_     ,  image_processor=lowercase_					)
             processor_slow.save_pretrained(self.tmpdirname					)
             _lowerCamelCase			:						List[str]            =ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,  use_fast=lowercase_					)

             _lowerCamelCase			:						Optional[Any]            =ChineseCLIPProcessor(tokenizer=lowercase_     ,  image_processor=lowercase_					)
             processor_fast.save_pretrained(self.tmpdirname					)
             _lowerCamelCase			:						Dict            =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     ,  lowercase_					)
             self.assertIsInstance(processor_fast.tokenizer     ,  lowercase_					)

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






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Union[str, Any]            =ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,  image_processor=self.get_image_processor()					)
             processor.save_pretrained(self.tmpdirname					)

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

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

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

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






      def 			lowerCamelCase			(   self		:	int					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						str            =self.get_image_processor()
             _lowerCamelCase			:						Optional[Any]            =self.get_tokenizer()

             _lowerCamelCase			:						Union[str, Any]            =ChineseCLIPProcessor(tokenizer=lowercase_     ,  image_processor=lowercase_					)

             _lowerCamelCase			:						Optional[int]            =self.prepare_image_inputs()

             _lowerCamelCase			:						str            =image_processor(lowercase_     ,  return_tensors='np'					)
             _lowerCamelCase			:						str            =processor(images=lowercase_     ,  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		:	Tuple					)   ->		Dict:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.get_image_processor()
             _lowerCamelCase			:						List[Any]            =self.get_tokenizer()

             _lowerCamelCase			:						str            =ChineseCLIPProcessor(tokenizer=lowercase_     ,  image_processor=lowercase_					)

             _lowerCamelCase			:						int            ='Alexandra，T-shirt的价格是15便士。'

             _lowerCamelCase			:						List[Any]            =processor(text=lowercase_					)

             _lowerCamelCase			:						str            =tokenizer(lowercase_					)

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






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Tuple:
             """simple docstring"""




             _lowerCamelCase			:						int            =self.get_image_processor()
             _lowerCamelCase			:						Union[str, Any]            =self.get_tokenizer()

             _lowerCamelCase			:						int            =ChineseCLIPProcessor(tokenizer=lowercase_     ,  image_processor=lowercase_					)

             _lowerCamelCase			:						Union[str, Any]            ='Alexandra，T-shirt的价格是15便士。'
             _lowerCamelCase			:						Union[str, Any]            =self.prepare_image_inputs()

             _lowerCamelCase			:						Union[str, Any]            =processor(text=lowercase_     ,  images=lowercase_					)

             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(lowercase_					):
                    processor()






      def 			lowerCamelCase			(   self		:	Dict					)   ->		Optional[int]:
             """simple docstring"""




             _lowerCamelCase			:						Dict            =self.get_image_processor()
             _lowerCamelCase			:						Optional[Any]            =self.get_tokenizer()

             _lowerCamelCase			:						List[str]            =ChineseCLIPProcessor(tokenizer=lowercase_     ,  image_processor=lowercase_					)

             _lowerCamelCase			:						Tuple            =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _lowerCamelCase			:						Optional[Any]            =processor.batch_decode(lowercase_					)
             _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_					)

             self.assertListEqual(lowercase_     ,  lowercase_					)






      def 			lowerCamelCase			(   self		:	Tuple					)   ->		int:
             """simple docstring"""




             _lowerCamelCase			:						List[str]            =self.get_image_processor()
             _lowerCamelCase			:						List[Any]            =self.get_tokenizer()

             _lowerCamelCase			:						str            =ChineseCLIPProcessor(tokenizer=lowercase_     ,  image_processor=lowercase_					)

             _lowerCamelCase			:						List[str]            ='Alexandra，T-shirt的价格是15便士。'
             _lowerCamelCase			:						Any            =self.prepare_image_inputs()

             _lowerCamelCase			:						str            =processor(text=lowercase_     ,  images=lowercase_					)

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






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCamelCase		   =			numpy.array([0, 0])
lowerCamelCase		   =			numpy.array([0.5, 0.866_0254])
lowerCamelCase		   =			numpy.array([1, 0])
lowerCamelCase		   =			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]						,     SCREAMING_SNAKE_CASE__   : int	):
       '''simple docstring'''





       _lowerCamelCase			:						List[Any]            =initial_vectors
       for _ in range(SCREAMING_SNAKE_CASE__	):
              _lowerCamelCase			:						List[str]            =iteration_step(SCREAMING_SNAKE_CASE__	)
       return vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Dict            =[]
       for i, start_vector in enumerate(vectors[:-1]	):
              _lowerCamelCase			:						Tuple            =vectors[i + 1]
              new_vectors.append(SCREAMING_SNAKE_CASE__	)
              _lowerCamelCase			:						Tuple            =end_vector - start_vector
              new_vectors.append(start_vector + difference_vector / 3	)
              new_vectors.append(
                  start_vector + difference_vector / 3 + rotate(difference_vector / 3						,     60	)	)
              new_vectors.append(start_vector + difference_vector * 2 / 3	)
       new_vectors.append(vectors[-1]	)
       return new_vectors




def       a_ (       SCREAMING_SNAKE_CASE__   : numpy.ndarray						,     SCREAMING_SNAKE_CASE__   : float	):
       '''simple docstring'''





       _lowerCamelCase			:						Union[str, Any]            =numpy.radians(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase      ,	_lowerCamelCase			:						List[Any]            =numpy.cos(SCREAMING_SNAKE_CASE__	), numpy.sin(SCREAMING_SNAKE_CASE__	)
       _lowerCamelCase			:						str            =numpy.array(((c, -s), (s, c))	)
       return numpy.dot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)




def       a_ (       SCREAMING_SNAKE_CASE__   : list[numpy.ndarray]	):
       '''simple docstring'''





       _lowerCamelCase			:						Optional[Any]            =plt.gca()
       axes.set_aspect('equal'	)

       # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
       # y-coordinates as inputs, which are constructed from the vector-list using
       # zip()
       _lowerCamelCase      ,	_lowerCamelCase			:						Tuple            =zip(*SCREAMING_SNAKE_CASE__	)
       plt.plot(SCREAMING_SNAKE_CASE__						,     SCREAMING_SNAKE_CASE__	)
       plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase		   =			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)


