

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				:							Tuple     ):
            A_		   =							tempfile.mkdtemp()

            # fmt: off
            A_		   =							["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_		   =							dict(zip(A__		,       range(len(A__     )     )     )     )
            A_		   =							["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
            A_		   =							{"unk_token": "<unk>"}

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

            A_		   =							{
                "do_resize": True,
                "size": 20,
                "do_center_crop": True,
                "crop_size": 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],
            }
            A_		   =							os.path.join(self.tmpdirname		,       A__     )
            with open(self.image_processor_file		,       "w"		,       encoding="utf-8"     ) as fp:
                  json.dump(A__		,       A__     )





      def 						__A     (					self				:							int		,       **UpperCAmelCase				:							Optional[Any]     ):
            return CLIPTokenizer.from_pretrained(self.tmpdirname		,       **A__     )





      def 						__A     (					self				:							Union[str, Any]		,       **UpperCAmelCase				:							Tuple     ):
            return CLIPTokenizerFast.from_pretrained(self.tmpdirname		,       **A__     )





      def 						__A     (					self				:							Optional[int]		,       **UpperCAmelCase				:							Optional[Any]     ):
            return CLIPImageProcessor.from_pretrained(self.tmpdirname		,       **A__     )





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





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

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

            return image_inputs





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

            A_		   =							CLIPProcessor(tokenizer=A__		,       image_processor=A__     )
            processor_slow.save_pretrained(self.tmpdirname     )
            A_		   =							CLIPProcessor.from_pretrained(self.tmpdirname		,       use_fast=A__     )

            A_		   =							CLIPProcessor(tokenizer=A__		,       image_processor=A__     )
            processor_fast.save_pretrained(self.tmpdirname     )
            A_		   =							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		,       A__     )
            self.assertIsInstance(processor_fast.tokenizer		,       A__     )

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





      def 						__A     (					self				:							Union[str, Any]     ):
            A_		   =							CLIPProcessor(tokenizer=self.get_tokenizer()		,       image_processor=self.get_image_processor()     )
            processor.save_pretrained(self.tmpdirname     )

            A_		   =							self.get_tokenizer(bos_token="(BOS)"		,       eos_token="(EOS)"     )
            A_		   =							self.get_image_processor(do_normalize=A__		,       padding_value=1.0     )

            A_		   =							CLIPProcessor.from_pretrained(
                self.tmpdirname		,       bos_token="(BOS)"		,       eos_token="(EOS)"		,       do_normalize=A__		,       padding_value=1.0     )

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

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





      def 						__A     (					self				:							str     ):
            A_		   =							self.get_image_processor()
            A_		   =							self.get_tokenizer()

            A_		   =							CLIPProcessor(tokenizer=A__		,       image_processor=A__     )

            A_		   =							self.prepare_image_inputs()

            A_		   =							image_processor(A__		,       return_tensors="np"     )
            A_		   =							processor(images=A__		,       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				:							str     ):
            A_		   =							self.get_image_processor()
            A_		   =							self.get_tokenizer()

            A_		   =							CLIPProcessor(tokenizer=A__		,       image_processor=A__     )

            A_		   =							"lower newer"

            A_		   =							processor(text=A__     )

            A_		   =							tokenizer(A__     )

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





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

            A_		   =							CLIPProcessor(tokenizer=A__		,       image_processor=A__     )

            A_		   =							"lower newer"
            A_		   =							self.prepare_image_inputs()

            A_		   =							processor(text=A__		,       images=A__     )

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

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





      def 						__A     (					self				:							Any     ):
            A_		   =							self.get_image_processor()
            A_		   =							self.get_tokenizer()

            A_		   =							CLIPProcessor(tokenizer=A__		,       image_processor=A__     )

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

            A_		   =							processor.batch_decode(A__     )
            A_		   =							tokenizer.batch_decode(A__     )

            self.assertListEqual(A__		,       A__     )







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

            A_		   =							CLIPProcessor(tokenizer=A__		,       image_processor=A__     )

            A_		   =							"lower newer"
            A_		   =							self.prepare_image_inputs()

            A_		   =							processor(text=A__		,       images=A__     )

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

from functools import lru_cache



@lru_cache
def        UpperCamelCase    (							lowerCAmelCase__		):
					'''simple docstring'''
					if num < 0:
										raise ValueError('''Number should not be negative.'''		)

					return 1 if num in (0, 1) else num * factorial(num - 1		)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




