


import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
		from PIL import Image

		from transformers import CLIPSegProcessor, ViTImageProcessor

@require_vision
class 		__a				(    unittest.TestCase      ):
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      tempfile.mkdtemp()

									# fmt: off
									_UpperCAmelCase					=      ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
									# fmt: on
									_UpperCAmelCase					=      dict(zip(_SCREAMING_SNAKE_CASE      ,   range(len(_SCREAMING_SNAKE_CASE						)						)						)						)
									_UpperCAmelCase					=      ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
									_UpperCAmelCase					=      {'unk_token': '<unk>'}

									_UpperCAmelCase					=      os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES['vocab_file']						)
									_UpperCAmelCase					=      os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES['merges_file']						)
									with open(self.vocab_file      ,   'w'      ,   encoding='utf-8'						) as fp:
																fp.write(json.dumps(_SCREAMING_SNAKE_CASE						) + '\n'						)
									with open(self.merges_file      ,   'w'      ,   encoding='utf-8'						) as fp:
																fp.write('\n'.join(_SCREAMING_SNAKE_CASE						)						)

									_UpperCAmelCase					=      {
									    'do_resize': True,
									    'size': 20,
									    'do_center_crop': True,
									    'crop_size': 18,
									    'do_normalize': True,
									    'image_mean': [0.48145466, 0.4578275, 0.40821073],
									    'image_std': [0.26862954, 0.26130258, 0.27577711],
									}
									_UpperCAmelCase					=      os.path.join(self.tmpdirname      ,   _SCREAMING_SNAKE_CASE						)
									with open(self.image_processor_file      ,   'w'      ,   encoding='utf-8'						) as fp:
																json.dump(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									return CLIPTokenizer.from_pretrained(self.tmpdirname      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									return CLIPTokenizerFast.from_pretrained(self.tmpdirname      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									return ViTImageProcessor.from_pretrained(self.tmpdirname      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	int:




									"""simple docstring"""





									shutil.rmtree(self.tmpdirname						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





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

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

									return image_inputs
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





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

									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)
									processor_slow.save_pretrained(self.tmpdirname						)
									_UpperCAmelCase					=      CLIPSegProcessor.from_pretrained(self.tmpdirname      ,   use_fast=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)
									processor_fast.save_pretrained(self.tmpdirname						)
									_UpperCAmelCase					=      CLIPSegProcessor.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      ,   _SCREAMING_SNAKE_CASE						)
									self.assertIsInstance(processor_fast.tokenizer      ,   _SCREAMING_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      ,   _SCREAMING_SNAKE_CASE						)
									self.assertIsInstance(processor_fast.image_processor      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	List[str]:




									"""simple docstring"""





									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()						)
									processor.save_pretrained(self.tmpdirname						)

									_UpperCAmelCase					=      self.get_tokenizer(bos_token='(BOS)'      ,   eos_token='(EOS)'						)
									_UpperCAmelCase					=      self.get_image_processor(do_normalize=_SCREAMING_SNAKE_CASE      ,   padding_value=1.0						)

									_UpperCAmelCase					=      CLIPSegProcessor.from_pretrained(
									    self.tmpdirname      ,   bos_token='(BOS)'      ,   eos_token='(EOS)'      ,   do_normalize=_SCREAMING_SNAKE_CASE      ,   padding_value=1.0						)

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

									self.assertEqual(processor.image_processor.to_json_string()      ,   image_processor_add_kwargs.to_json_string()						)
									self.assertIsInstance(processor.image_processor      ,   _SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.prepare_image_inputs()

									_UpperCAmelCase					=      image_processor(_SCREAMING_SNAKE_CASE      ,   return_tensors='np'						)
									_UpperCAmelCase					=      processor(images=_SCREAMING_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      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      'lower newer'

									_UpperCAmelCase					=      processor(text=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      tokenizer(_SCREAMING_SNAKE_CASE						)

									for key in encoded_tok.keys():
																self.assertListEqual(encoded_tok[key]      ,   encoded_processor[key]						)
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      'lower newer'
									_UpperCAmelCase					=      self.prepare_image_inputs()

									_UpperCAmelCase					=      processor(text=_SCREAMING_SNAKE_CASE      ,   images=_SCREAMING_SNAKE_CASE						)

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

									# test if it raises when no input is passed
									with pytest.raises(_SCREAMING_SNAKE_CASE						):
																processor()
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      self.prepare_image_inputs()
									_UpperCAmelCase					=      self.prepare_image_inputs()

									_UpperCAmelCase					=      processor(images=_SCREAMING_SNAKE_CASE      ,   visual_prompt=_SCREAMING_SNAKE_CASE						)

									self.assertListEqual(list(inputs.keys()						)      ,   ['pixel_values', 'conditional_pixel_values']						)

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






		def      UpperCAmelCase__    (      self						)			->	Tuple:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_image_processor()
									_UpperCAmelCase					=      self.get_tokenizer()

									_UpperCAmelCase					=      CLIPSegProcessor(tokenizer=_SCREAMING_SNAKE_CASE      ,   image_processor=_SCREAMING_SNAKE_CASE						)

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

									_UpperCAmelCase					=      processor.batch_decode(_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      tokenizer.batch_decode(_SCREAMING_SNAKE_CASE						)

									self.assertListEqual(_SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE						)




def 	lowerCAmelCase__			(      a__:		int	)  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      generate_pascal_triangle(a__	)
							for row_idx in range(a__	):
														# 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 	lowerCAmelCase__			(      a__:		int	)  ->		list[list[int]]:


							'''simple docstring'''





							if not isinstance(a__      ,  a__	):
														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'	)

							_UpperCAmelCase					=      []
							for current_row_idx in range(a__	):
														_UpperCAmelCase					=      populate_current_row(a__      ,  a__	)
														triangle.append(a__	)
							return triangle

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		int	)  ->		list[int]:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		list[list[int]]      ,  a__:		list[int]      ,  a__:		int      ,  a__:		int      ,  )  ->		None:


							'''simple docstring'''





							_UpperCAmelCase					=      triangle[current_row_idx - 1][current_col_idx - 1]
							_UpperCAmelCase					=      triangle[current_row_idx - 1][current_col_idx]
							_UpperCAmelCase					=      above_to_left_elt + above_to_right_elt

def 	lowerCAmelCase__			(      a__:		int	)  ->		list[list[int]]:


							'''simple docstring'''





							if not isinstance(a__      ,  a__	):
														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'	)

							_UpperCAmelCase					=      [[1]]

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

							return result

def 	lowerCAmelCase__			(      )  ->		None:


							'''simple docstring'''





							from collections.abc import Callable
							from timeit import timeit

							def benchmark_a_function(a__:		Callable      ,  a__:		int	) -> None:
														_UpperCAmelCase					=      F'''{func.__name__}({value})'''
														_UpperCAmelCase					=      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(1_5	):  # (1, 7, 14):
														for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
																					benchmark_a_function(a__      ,  a__	)
														print()


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		benchmark()
