

def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: List[Any]  		=       generate_pascal_triangle(_UpperCAmelCase    )
						for row_idx in range(_UpperCAmelCase    ):
												# Print left spaces
												for _ in range(num_rows - row_idx - 1    ):
																		print(end=" "    )
												# Print row values
												for col_idx in range(row_idx + 1    ):
																		if col_idx != row_idx:
																								print(triangle[row_idx][col_idx]				,	end=" "    )
																		else:
																								print(triangle[row_idx][col_idx]				,	end=""    )
												print()







def 			A_      (    _UpperCAmelCase    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												raise TypeError("The input value of 'num_rows' should be 'int'"    )

						if num_rows == 0:
												return []
						elif num_rows < 0:
												raise ValueError(
												    "The input value of 'num_rows' should be greater than or equal to 0"    )

						SCREAMING_SNAKE_CASE_: list[list[int]]  		=       []
						for current_row_idx in range(_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: List[Any]  		=       populate_current_row(_UpperCAmelCase				,	_UpperCAmelCase    )
												triangle.append(_UpperCAmelCase    )
						return triangle







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Dict  		=       [-1] * (current_row_idx + 1)
						# first and last elements of current row are equal to 1
						SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       1, 1
						for current_col_idx in range(1				,	_UpperCAmelCase    ):
												calculate_current_element(
												    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )
						return current_row







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	):
						SCREAMING_SNAKE_CASE_: str  		=       triangle[current_row_idx - 1][current_col_idx - 1]
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       triangle[current_row_idx - 1][current_col_idx]
						SCREAMING_SNAKE_CASE_: str  		=       above_to_left_elt + above_to_right_elt







def 			A_      (    _UpperCAmelCase    ):

						if not isinstance(_UpperCAmelCase				,	_UpperCAmelCase    ):
												raise TypeError("The input value of 'num_rows' should be 'int'"    )

						if num_rows == 0:
												return []
						elif num_rows < 0:
												raise ValueError(
												    "The input value of 'num_rows' should be greater than or equal to 0"    )

						SCREAMING_SNAKE_CASE_: list[list[int]]  		=       [[1]]

						for row_index in range(1				,	_UpperCAmelCase    ):
												SCREAMING_SNAKE_CASE_: Any  		=       [0] + result[-1] + [0]
												SCREAMING_SNAKE_CASE_: Tuple  		=       row_index + 1
												# Calculate the number of distinct elements in a row
												SCREAMING_SNAKE_CASE_: Any  		=       sum(divmod(_UpperCAmelCase				,	2    )    )
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       [
												    temp_row[i - 1] + temp_row[i] for i in range(1				,	distinct_elements + 1    )
												]
												SCREAMING_SNAKE_CASE_: Tuple  		=       row_first_half[: (row_index + 1) // 2]
												row_second_half.reverse()
												SCREAMING_SNAKE_CASE_: List[str]  		=       row_first_half + row_second_half
												result.append(_UpperCAmelCase    )

						return result







def 			A_      (    ):
						from collections.abc import Callable
						from timeit import timeit

						def benchmark_a_function(_UpperCAmelCase				,	_UpperCAmelCase    ) -> None:
												SCREAMING_SNAKE_CASE_: int  		=       f"{func.__name__}({value})"
												SCREAMING_SNAKE_CASE_: List[str]  		=       timeit(f"__main__.{call}"				,	setup="import __main__"    )
												# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
												print(f"{call:38} -- {timing:.4f} seconds"    )

						for value in range(15    ):  # (1, 7, 14):
												for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																		benchmark_a_function(_UpperCAmelCase				,	_UpperCAmelCase    )
												print()


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						benchmark()








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   ):




	'''simple docstring'''


	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		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': [0.48_145_466, 0.4_578_275, 0.40_821_073],
		    'image_std': [0.26_862_954, 0.26_130_258, 0.27_577_711],
		    'do_convert_rgb': True,
		}
		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizer.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return BertTokenizerFast.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname		,    **snake_case       )





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





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

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

		return image_inputs





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_tokenizer()
		lowercase       =    self.get_rust_tokenizer()
		lowercase       =    self.get_image_processor()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		processor_slow.save_pretrained(self.tmpdirname       )
		lowercase       =    ChineseCLIPProcessor.from_pretrained(self.tmpdirname		,    use_fast=snake_case       )

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )
		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		,    snake_case       )
		self.assertIsInstance(processor_fast.tokenizer		,    snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		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=snake_case       )

		lowercase       =    ChineseCLIPProcessor.from_pretrained(
		    self.tmpdirname		,    cls_token='(CLS)'		,    sep_token='(SEP)'		,    do_normalize=snake_case       )

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

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

		lowercase       =    self.prepare_image_inputs()

		lowercase       =    image_processor(snake_case		,    return_tensors='np'       )
		lowercase       =    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 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

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

		lowercase       =    processor(text=snake_case       )

		lowercase       =    tokenizer(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

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

		lowercase       =    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 pytest.raises(snake_case       ):
			processor()





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

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

		lowercase       =    processor.batch_decode(snake_case       )
		lowercase       =    tokenizer.batch_decode(snake_case       )

		self.assertListEqual(snake_case		,    snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.get_image_processor()
		lowercase       =    self.get_tokenizer()

		lowercase       =    ChineseCLIPProcessor(tokenizer=snake_case		,    image_processor=snake_case       )

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

		lowercase       =    processor(text=snake_case		,    images=snake_case       )

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

