






"""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   SCREAMING_SNAKE_CASE	(					unittest.TestCase    ):





	"""simple docstring"""




	def  lowerCamelCase(self				):
		A_		:    Any						     = tempfile.mkdtemp()

		A_		:    Any						     = [
		    """[UNK]""",
		    """[CLS]""",
		    """[SEP]""",
		    """[PAD]""",
		    """[MASK]""",
		    """的""",
		    """价""",
		    """格""",
		    """是""",
		    """15""",
		    """便""",
		    """alex""",
		    """##andra""",
		    """，""",
		    """。""",
		    """-""",
		    """t""",
		    """shirt""",
		]
		A_		:    Optional[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]				)				)

		A_		:    Dict						     = {
		    """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,
		}
		A_		:    Any						     = os.path.join(self.tmpdirname  ,  lowerCAmelCase_				)
		with open(self.image_processor_file  ,  """w"""  ,  encoding="""utf-8"""				) as fp:
			json.dump(lowerCAmelCase_  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		return BertTokenizer.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		return BertTokenizerFast.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self  ,  **lowerCAmelCase_				):
		return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname  ,  **lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		shutil.rmtree(self.tmpdirname				)





	def  lowerCamelCase(self				):
		A_		:    Tuple						     = [np.random.randint(255  ,  size=(3, 30, 400)  ,  dtype=np.uinta				)]

		A_		:    Any						     = [Image.fromarray(np.moveaxis(lowerCAmelCase_  ,  0  ,  -1				)				) for x in image_inputs]

		return image_inputs





	def  lowerCamelCase(self				):
		A_		:    str						     = self.get_tokenizer()
		A_		:    Optional[int]						     = self.get_rust_tokenizer()
		A_		:    List[Any]						     = self.get_image_processor()

		A_		:    Tuple						     = ChineseCLIPProcessor(tokenizer=lowerCAmelCase_  ,  image_processor=lowerCAmelCase_				)
		processor_slow.save_pretrained(self.tmpdirname				)
		A_		:    Optional[Any]						     = ChineseCLIPProcessor.from_pretrained(self.tmpdirname  ,  use_fast=lowerCAmelCase_				)

		A_		:    int						     = ChineseCLIPProcessor(tokenizer=lowerCAmelCase_  ,  image_processor=lowerCAmelCase_				)
		processor_fast.save_pretrained(self.tmpdirname				)
		A_		:    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  ,  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  lowerCamelCase(self				):
		A_		:    List[str]						     = ChineseCLIPProcessor(tokenizer=self.get_tokenizer()  ,  image_processor=self.get_image_processor()				)
		processor.save_pretrained(self.tmpdirname				)

		A_		:    Tuple						     = self.get_tokenizer(cls_token="""(CLS)"""  ,  sep_token="""(SEP)"""				)
		A_		:    Union[str, Any]						     = self.get_image_processor(do_normalize=lowerCAmelCase_				)

		A_		:    Union[str, Any]						     = 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  lowerCamelCase(self				):
		A_		:    Optional[Any]						     = self.get_image_processor()
		A_		:    Optional[int]						     = self.get_tokenizer()

		A_		:    List[str]						     = ChineseCLIPProcessor(tokenizer=lowerCAmelCase_  ,  image_processor=lowerCAmelCase_				)

		A_		:    Optional[Any]						     = self.prepare_image_inputs()

		A_		:    Union[str, Any]						     = image_processor(lowerCAmelCase_  ,  return_tensors="""np"""				)
		A_		:    List[Any]						     = 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  lowerCamelCase(self				):
		A_		:    Dict						     = self.get_image_processor()
		A_		:    List[str]						     = self.get_tokenizer()

		A_		:    Optional[int]						     = ChineseCLIPProcessor(tokenizer=lowerCAmelCase_  ,  image_processor=lowerCAmelCase_				)

		A_		:    Any						     = """Alexandra，T-shirt的价格是15便士。"""

		A_		:    Any						     = processor(text=lowerCAmelCase_				)

		A_		:    List[str]						     = tokenizer(lowerCAmelCase_				)

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





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.get_image_processor()
		A_		:    str						     = self.get_tokenizer()

		A_		:    Dict						     = ChineseCLIPProcessor(tokenizer=lowerCAmelCase_  ,  image_processor=lowerCAmelCase_				)

		A_		:    List[str]						     = """Alexandra，T-shirt的价格是15便士。"""
		A_		:    int						     = self.prepare_image_inputs()

		A_		:    List[str]						     = 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  lowerCamelCase(self				):
		A_		:    Tuple						     = self.get_image_processor()
		A_		:    Optional[Any]						     = self.get_tokenizer()

		A_		:    int						     = ChineseCLIPProcessor(tokenizer=lowerCAmelCase_  ,  image_processor=lowerCAmelCase_				)

		A_		:    Any						     = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

		A_		:    Union[str, Any]						     = processor.batch_decode(lowerCAmelCase_				)
		A_		:    List[Any]						     = tokenizer.batch_decode(lowerCAmelCase_				)

		self.assertListEqual(lowerCAmelCase_  ,  lowerCAmelCase_				)





	def  lowerCamelCase(self				):
		A_		:    Dict						     = self.get_image_processor()
		A_		:    int						     = self.get_tokenizer()

		A_		:    Any						     = ChineseCLIPProcessor(tokenizer=lowerCAmelCase_  ,  image_processor=lowerCAmelCase_				)

		A_		:    Union[str, Any]						     = """Alexandra，T-shirt的价格是15便士。"""
		A_		:    int						     = self.prepare_image_inputs()

		A_		:    Optional[int]						     = processor(text=lowerCAmelCase_  ,  images=lowerCAmelCase_				)

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









"""simple docstring"""
def       __UpperCamelCase						(		snake_case__ = 200       ):
	A_		:    Union[str, Any]						     = [1, 2, 5, 10, 20, 50, 100, 200]
	A_		:    int						     = [0] * (pence + 1)
	A_		:    Tuple						     = 1  # base case: 1 way to make 0 pence

	for coin in coins:
		for i in range(snake_case__ , pence + 1 , 1       ):
			number_of_ways[i] += number_of_ways[i - coin]
	return number_of_ways[pence]


if __name__ == "__main__":
							assert solution(200) == 73_682

