





'''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 			SCREAMING_SNAKE_CASE   (unittest.TestCase							):


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	tempfile.mkdtemp()

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

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

          __A						:			Optional[int]								=	{
              '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],
          }
          __A						:			Union[str, Any]								=	os.path.join(self.tmpdirname						,     _UpperCAmelCase)
          with open(self.image_processor_file						,     'w'						,     encoding='utf-8') as fp:
               json.dump(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self						,     **_UpperCAmelCase):

          '''simple docstring'''






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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          shutil.rmtree(self.tmpdirname)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			List[Any]								=	[np.random.randint(255						,     size=(3, 30, 400)						,     dtype=np.uinta)]

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

          return image_inputs


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

          __A						:			List[str]								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)
          processor_fast.save_pretrained(self.tmpdirname)
          __A						:			List[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						,     _UpperCAmelCase)
          self.assertIsInstance(processor_fast.tokenizer						,     _UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			Optional[Any]								=	CLIPProcessor(tokenizer=self.get_tokenizer()						,     image_processor=self.get_image_processor())
          processor.save_pretrained(self.tmpdirname)

          __A						:			List[str]								=	self.get_tokenizer(bos_token='(BOS)'						,     eos_token='(EOS)')
          __A						:			Dict								=	self.get_image_processor(do_normalize=_UpperCAmelCase						,     padding_value=1.0)

          __A						:			List[Any]								=	CLIPProcessor.from_pretrained(
              self.tmpdirname						,     bos_token='(BOS)'						,     eos_token='(EOS)'						,     do_normalize=_UpperCAmelCase						,     padding_value=1.0)

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

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          __A						:			int								=	self.get_image_processor()
          __A						:			int								=	self.get_tokenizer()

          __A						:			Dict								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Any								=	self.prepare_image_inputs()

          __A						:			str								=	image_processor(_UpperCAmelCase						,     return_tensors='np')
          __A						:			Tuple								=	processor(images=_UpperCAmelCase						,     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 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Dict								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			List[Any]								=	'lower newer'

          __A						:			Union[str, Any]								=	processor(text=_UpperCAmelCase)

          __A						:			Dict								=	tokenizer(_UpperCAmelCase)

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

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

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

          __A						:			Union[str, Any]								=	processor(text=_UpperCAmelCase						,     images=_UpperCAmelCase)

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

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


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Union[str, Any]								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Optional[int]								=	[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

          __A						:			str								=	processor.batch_decode(_UpperCAmelCase)
          __A						:			Optional[Any]								=	tokenizer.batch_decode(_UpperCAmelCase)

          self.assertListEqual(_UpperCAmelCase						,     _UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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

          __A						:			Any								=	CLIPProcessor(tokenizer=_UpperCAmelCase						,     image_processor=_UpperCAmelCase)

          __A						:			Any								=	'lower newer'
          __A						:			str								=	self.prepare_image_inputs()

          __A						:			List[str]								=	processor(text=_UpperCAmelCase						,     images=_UpperCAmelCase)

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






'''simple docstring'''




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	['''image_processor''', '''tokenizer''']
     lowerCAmelCase		      =	'''AutoImageProcessor'''
     lowerCAmelCase		      =	'''AutoTokenizer'''


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__(_UpperCAmelCase						,     _UpperCAmelCase)
          __A						:			Tuple								=	self.image_processor


     def __call__(						self						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     _UpperCAmelCase=None						,     **_UpperCAmelCase):

          '''simple docstring'''






          if text is None and images is None:
               raise ValueError('You have to specify either text or images. Both cannot be none.')

          if text is not None:
               __A						:			Any								=	self.tokenizer(_UpperCAmelCase						,     return_tensors=_UpperCAmelCase						,     **_UpperCAmelCase)

          if images is not None:
               __A						:			Tuple								=	self.image_processor(_UpperCAmelCase						,     return_tensors=_UpperCAmelCase						,     **_UpperCAmelCase)

          if text is not None and images is not None:
               __A						:			Optional[int]								=	image_features.pixel_values
               return encoding
          elif text is not None:
               return encoding
          else:
               return BatchEncoding(data=dict(**_UpperCAmelCase)						,     tensor_type=_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.batch_decode(*_UpperCAmelCase						,     **_UpperCAmelCase)


     def 							SCREAMING_SNAKE_CASE  (						self						,     *_UpperCAmelCase						,     **_UpperCAmelCase):

          '''simple docstring'''






          return self.tokenizer.decode(*_UpperCAmelCase						,     **_UpperCAmelCase)


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






          return ["input_ids", "attention_mask", "pixel_values"]