



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							):
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Dict						     =     tempfile.mkdtemp()

          # fmt: off
          UpperCamelCase   :  Tuple						     =     ['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   :  List[str]						     =     dict(zip(SCREAMING_SNAKE_CASE_,      range(len(SCREAMING_SNAKE_CASE_	)	)	)	)
          UpperCamelCase   :  Dict						     =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
          UpperCamelCase   :  Optional[int]						     =     {'unk_token': '<unk>'}

          UpperCamelCase   :  List[Any]						     =     os.path.join(self.tmpdirname,      VOCAB_FILES_NAMES['vocab_file']	)
          UpperCamelCase   :  str						     =     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   :  Dict						     =     {
              'do_resize': True,
              'size': 20,
              'do_center_crop': True,
              'crop_size': 18,
              'do_normalize': True,
              'image_mean': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
              'image_std': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
          }
          UpperCamelCase   :  Dict						     =     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  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		int:
          return CLIPTokenizer.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		str:
          return CLIPTokenizerFast.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
          return CLIPImageProcessor.from_pretrained(self.tmpdirname,      **SCREAMING_SNAKE_CASE_	)
       def  snake_case_	(      self	)					->		Optional[int]:
          shutil.rmtree(self.tmpdirname	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  List[str]						     =     [np.random.randint(255,      size=(3, 30, 400),      dtype=np.uinta	)]

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

          return image_inputs
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  str						     =     self.get_tokenizer()
          UpperCamelCase   :  Optional[int]						     =     self.get_rust_tokenizer()
          UpperCamelCase   :  int						     =     self.get_image_processor()

          UpperCamelCase   :  str						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          processor_slow.save_pretrained(self.tmpdirname	)
          UpperCamelCase   :  Tuple						     =     CLIPProcessor.from_pretrained(self.tmpdirname,      use_fast=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[int]						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          processor_fast.save_pretrained(self.tmpdirname	)
          UpperCamelCase   :  Optional[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,      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  snake_case_	(      self	)					->		Any:
          UpperCamelCase   :  str						     =     CLIPProcessor(tokenizer=self.get_tokenizer(),      image_processor=self.get_image_processor()	)
          processor.save_pretrained(self.tmpdirname	)

          UpperCamelCase   :  Any						     =     self.get_tokenizer(bos_token='(BOS)',      eos_token='(EOS)'	)
          UpperCamelCase   :  Optional[Any]						     =     self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_,      padding_value=1.0	)

          UpperCamelCase   :  List[str]						     =     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  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  List[str]						     =     self.get_image_processor()
          UpperCamelCase   :  List[Any]						     =     self.get_tokenizer()

          UpperCamelCase   :  int						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     self.prepare_image_inputs()

          UpperCamelCase   :  Dict						     =     image_processor(SCREAMING_SNAKE_CASE_,      return_tensors='np'	)
          UpperCamelCase   :  Optional[Any]						     =     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  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  int						     =     self.get_image_processor()
          UpperCamelCase   :  List[Any]						     =     self.get_tokenizer()

          UpperCamelCase   :  Optional[int]						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     'lower newer'

          UpperCamelCase   :  Dict						     =     processor(text=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     tokenizer(SCREAMING_SNAKE_CASE_	)

          for key in encoded_tok.keys():
             self.assertListEqual(encoded_tok[key],      encoded_processor[key]	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Tuple						     =     self.get_image_processor()
          UpperCamelCase   :  Optional[int]						     =     self.get_tokenizer()

          UpperCamelCase   :  Any						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     'lower newer'
          UpperCamelCase   :  Optional[int]						     =     self.prepare_image_inputs()

          UpperCamelCase   :  str						     =     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  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  List[str]						     =     self.get_image_processor()
          UpperCamelCase   :  int						     =     self.get_tokenizer()

          UpperCamelCase   :  Any						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          UpperCamelCase   :  int						     =     processor.batch_decode(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

          self.assertListEqual(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)






       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Any						     =     self.get_image_processor()
          UpperCamelCase   :  List[str]						     =     self.get_tokenizer()

          UpperCamelCase   :  Any						     =     CLIPProcessor(tokenizer=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     'lower newer'
          UpperCamelCase   :  Optional[Any]						     =     self.prepare_image_inputs()

          UpperCamelCase   :  str						     =     processor(text=SCREAMING_SNAKE_CASE_,      images=SCREAMING_SNAKE_CASE_	)

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








from abc import ABC, abstractmethod
from argparse import ArgumentParser



class     lowerCAmelCase_		(  a__							):
       @staticmethod
       @abstractmethod
       def  snake_case_	(      SCREAMING_SNAKE_CASE_	)					->		int:
          raise NotImplementedError()






       @abstractmethod
       def  snake_case_	(      self	)					->		str:
          raise NotImplementedError()



