

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     _a						(unittest.TestCase				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Optional[Any]        =		tempfile.mkdtemp()

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

						A__							:    str        =		os.path.join(self.tmpdirname    ,       VOCAB_FILES_NAMES["""vocab_file"""]			)
						A__							:    Dict        =		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(lowercase_			) + """\n"""			)
						with open(self.merges_file    ,       """w"""    ,       encoding="""utf-8"""			) as fp:
							fp.write("""\n""".join(lowercase_			)			)

						A__							:    List[Any]        =		{
						    "do_resize": True,
						    "size": 20,
						    "do_center_crop": True,
						    "crop_size": 18,
						    "do_normalize": True,
						    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
						    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
						}
						A__							:    Tuple        =		os.path.join(self.tmpdirname    ,       lowercase_			)
						with open(self.image_processor_file    ,       """w"""    ,       encoding="""utf-8"""			) as fp:
							json.dump(lowercase_    ,       lowercase_			)






					def   __A			( self    ,       **A__			):
						return CLIPTokenizer.from_pretrained(self.tmpdirname    ,       **lowercase_			)






					def   __A			( self    ,       **A__			):
						return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,       **lowercase_			)






					def   __A			( self    ,       **A__			):
						return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,       **lowercase_			)






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






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

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

						return image_inputs






					def   __A			( self			):
						A__							:    List[Any]        =		self.get_tokenizer()
						A__							:    Tuple        =		self.get_rust_tokenizer()
						A__							:    Tuple        =		self.get_image_processor()

						A__							:    List[str]        =		CLIPProcessor(tokenizer=lowercase_    ,       image_processor=lowercase_			)
						processor_slow.save_pretrained(self.tmpdirname			)
						A__							:    Union[str, Any]        =		CLIPProcessor.from_pretrained(self.tmpdirname    ,       use_fast=lowercase_			)

						A__							:    int        =		CLIPProcessor(tokenizer=lowercase_    ,       image_processor=lowercase_			)
						processor_fast.save_pretrained(self.tmpdirname			)
						A__							:    Union[str, Any]        =		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    ,       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   __A			( self			):
						A__							:    Tuple        =		CLIPProcessor(tokenizer=self.get_tokenizer()    ,       image_processor=self.get_image_processor()			)
						processor.save_pretrained(self.tmpdirname			)

						A__							:    int        =		self.get_tokenizer(bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""			)
						A__							:    Union[str, Any]        =		self.get_image_processor(do_normalize=lowercase_    ,       padding_value=1.0			)

						A__							:    List[Any]        =		CLIPProcessor.from_pretrained(
						    self.tmpdirname    ,       bos_token="""(BOS)"""    ,       eos_token="""(EOS)"""    ,       do_normalize=lowercase_    ,       padding_value=1.0			)

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

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

						A__							:    Union[str, Any]        =		self.prepare_image_inputs()

						A__							:    int        =		image_processor(lowercase_    ,       return_tensors="""np"""			)
						A__							:    List[str]        =		processor(images=lowercase_    ,       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   __A			( self			):
						A__							:    int        =		self.get_image_processor()
						A__							:    str        =		self.get_tokenizer()

						A__							:    int        =		CLIPProcessor(tokenizer=lowercase_    ,       image_processor=lowercase_			)

						A__							:    Any        =		"lower newer"

						A__							:    Tuple        =		processor(text=lowercase_			)

						A__							:    int        =		tokenizer(lowercase_			)

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






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.get_image_processor()
						A__							:    Optional[Any]        =		self.get_tokenizer()

						A__							:    Tuple        =		CLIPProcessor(tokenizer=lowercase_    ,       image_processor=lowercase_			)

						A__							:    Any        =		"lower newer"
						A__							:    Tuple        =		self.prepare_image_inputs()

						A__							:    List[Any]        =		processor(text=lowercase_    ,       images=lowercase_			)

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

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






					def   __A			( self			):
						A__							:    List[Any]        =		self.get_image_processor()
						A__							:    List[str]        =		self.get_tokenizer()

						A__							:    str        =		CLIPProcessor(tokenizer=lowercase_    ,       image_processor=lowercase_			)

						A__							:    Union[str, Any]        =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

						A__							:    Dict        =		processor.batch_decode(lowercase_			)
						A__							:    Tuple        =		tokenizer.batch_decode(lowercase_			)

						self.assertListEqual(lowercase_    ,       lowercase_			)






					def   __A			( self			):
						A__							:    Union[str, Any]        =		self.get_image_processor()
						A__							:    Union[str, Any]        =		self.get_tokenizer()

						A__							:    Any        =		CLIPProcessor(tokenizer=lowercase_    ,       image_processor=lowercase_			)

						A__							:    List[str]        =		"lower newer"
						A__							:    List[Any]        =		self.prepare_image_inputs()

						A__							:    Tuple        =		processor(text=lowercase_    ,       images=lowercase_			)

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





"""simple docstring"""





from __future__ import annotations







def 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   if (voltage, current, resistance).count(0  ) != 1:
      raise ValueError("One and only one argument must be 0"  )
   if resistance < 0:
      raise ValueError("Resistance cannot be negative"  )
   if voltage == 0:
      return {"voltage": float(current * resistance  )}
   elif current == 0:
      return {"current": voltage / resistance}
   elif resistance == 0:
      return {"resistance": voltage / current}
   else:
      raise ValueError("Exactly one argument must be 0"  )


if __name__ == "__main__":
    import doctest

    doctest.testmod()


