
"""simple docstring"""




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 CLIPImageProcessor, CLIPProcessor



@require_vision
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tempfile.mkdtemp()

											# fmt: off
											SCREAMING_SNAKE_CASE__		: Dict     =     ["""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
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     dict(zip(SCREAMING_SNAKE_CASE__     , range(len(SCREAMING_SNAKE_CASE__					)					)					)					)
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {"""unk_token""": """<unk>"""}

											SCREAMING_SNAKE_CASE__		: int     =     os.path.join(self.tmpdirname     , VOCAB_FILES_NAMES["""vocab_file"""]					)
											SCREAMING_SNAKE_CASE__		: Tuple     =     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__					)					)

											SCREAMING_SNAKE_CASE__		: str     =     {
											    """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],
											}
											SCREAMING_SNAKE_CASE__		: str     =     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 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	List[Any]:



											"""simple docstring"""




											return CLIPTokenizer.from_pretrained(self.tmpdirname     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											return CLIPTokenizerFast.from_pretrained(self.tmpdirname     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self     , **SCREAMING_SNAKE_CASE__					)      ->	str:



											"""simple docstring"""




											return CLIPImageProcessor.from_pretrained(self.tmpdirname     , **SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											shutil.rmtree(self.tmpdirname					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     [np.random.randint(2_55     , size=(3, 30, 4_00)     , dtype=np.uinta					)]

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

											return image_inputs



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: int     =     self.get_tokenizer()
											SCREAMING_SNAKE_CASE__		: str     =     self.get_rust_tokenizer()
											SCREAMING_SNAKE_CASE__		: Tuple     =     self.get_image_processor()

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)
											processor_slow.save_pretrained(self.tmpdirname					)
											SCREAMING_SNAKE_CASE__		: Dict     =     CLIPProcessor.from_pretrained(self.tmpdirname     , use_fast=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: str     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)
											processor_fast.save_pretrained(self.tmpdirname					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     CLIPProcessor.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 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[str]     =     CLIPProcessor(tokenizer=self.get_tokenizer()     , image_processor=self.get_image_processor()					)
											processor.save_pretrained(self.tmpdirname					)

											SCREAMING_SNAKE_CASE__		: Dict     =     self.get_tokenizer(bos_token="""(BOS)"""     , eos_token="""(EOS)"""					)
											SCREAMING_SNAKE_CASE__		: Dict     =     self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__     , padding_value=1.0					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     CLIPProcessor.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 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: List[Any]     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     image_processor(SCREAMING_SNAKE_CASE__     , return_tensors="""np"""					)
											SCREAMING_SNAKE_CASE__		: Dict     =     processor(images=SCREAMING_SNAKE_CASE__     , return_tensors="""np"""					)

											for key in input_image_proc.keys():
																		self.assertAlmostEqual(input_image_proc[key].sum()     , input_processor[key].sum()     , delta=1E-2					)



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Dict     =     self.get_image_processor()
											SCREAMING_SNAKE_CASE__		: Tuple     =     self.get_tokenizer()

											SCREAMING_SNAKE_CASE__		: List[str]     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     """lower newer"""

											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     processor(text=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Any     =     tokenizer(SCREAMING_SNAKE_CASE__					)

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



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.get_image_processor()
											SCREAMING_SNAKE_CASE__		: Any     =     self.get_tokenizer()

											SCREAMING_SNAKE_CASE__		: Any     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     """lower newer"""
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__		: Tuple     =     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 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     self.get_image_processor()
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.get_tokenizer()

											SCREAMING_SNAKE_CASE__		: Any     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)

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

											SCREAMING_SNAKE_CASE__		: Any     =     processor.batch_decode(SCREAMING_SNAKE_CASE__					)
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE__					)

											self.assertListEqual(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.get_image_processor()
											SCREAMING_SNAKE_CASE__		: List[str]     =     self.get_tokenizer()

											SCREAMING_SNAKE_CASE__		: List[Any]     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE__     , image_processor=SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Any     =     """lower newer"""
											SCREAMING_SNAKE_CASE__		: str     =     self.prepare_image_inputs()

											SCREAMING_SNAKE_CASE__		: int     =     processor(text=SCREAMING_SNAKE_CASE__     , images=SCREAMING_SNAKE_CASE__					)

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






"""simple docstring"""




import torch

def lowercase_						(     ):
							if torch.cuda.is_available():
														SCREAMING_SNAKE_CASE__		: List[Any]     =     torch.cuda.device_count()
							else:
														SCREAMING_SNAKE_CASE__		: int     =     0
							print(f'''Successfully ran on {num_gpus} GPUs'''		)


if __name__ == "__main__":
					main()




