
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):
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      tempfile.mkdtemp()

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

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

       lowerCamelCase		:	Optional[int]							=      {
           """do_resize""": True,
           """size""": 2_0,
           """do_center_crop""": True,
           """crop_size""": 1_8,
           """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],
       }
       lowerCamelCase		:	List[str]							=      os.path.join(self.tmpdirname    ,  __magic_name__      )
       with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8"""      ) as fp:
        json.dump(__magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return CLIPTokenizer.from_pretrained(self.tmpdirname    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return CLIPImageProcessor.from_pretrained(self.tmpdirname    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       shutil.rmtree(self.tmpdirname      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      [np.random.randint(2_5_5    ,  size=(3, 3_0, 4_0_0)    ,  dtype=np.uinta      )]

       lowerCamelCase		:	Tuple							=      [Image.fromarray(np.moveaxis(__magic_name__    ,  0    ,  -1      )      ) for x in image_inputs]

       return image_inputs
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.get_tokenizer()
       lowerCamelCase		:	Optional[Any]							=      self.get_rust_tokenizer()
       lowerCamelCase		:	Dict							=      self.get_image_processor()

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

       lowerCamelCase		:	Dict							=      CLIPProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )
       processor_fast.save_pretrained(self.tmpdirname      )
       lowerCamelCase		:	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    ,  __magic_name__      )
       self.assertIsInstance(processor_fast.tokenizer    ,  __magic_name__      )

       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    ,  __magic_name__      )
       self.assertIsInstance(processor_fast.image_processor    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      CLIPProcessor(tokenizer=self.get_tokenizer()    ,  image_processor=self.get_image_processor()      )
       processor.save_pretrained(self.tmpdirname      )

       lowerCamelCase		:	List[Any]							=      self.get_tokenizer(bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""      )
       lowerCamelCase		:	Optional[Any]							=      self.get_image_processor(do_normalize=__magic_name__    ,  padding_value=1.0      )

       lowerCamelCase		:	Tuple							=      CLIPProcessor.from_pretrained(
           self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=__magic_name__    ,  padding_value=1.0      )

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

       self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor_add_kwargs.to_json_string()      )
       self.assertIsInstance(processor.image_processor    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.get_image_processor()
       lowerCamelCase		:	Union[str, Any]							=      self.get_tokenizer()

       lowerCamelCase		:	Union[str, Any]							=      CLIPProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	List[str]							=      self.prepare_image_inputs()

       lowerCamelCase		:	Dict							=      image_processor(__magic_name__    ,  return_tensors="""np"""      )
       lowerCamelCase		:	Tuple							=      processor(images=__magic_name__    ,  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 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      self.get_image_processor()
       lowerCamelCase		:	str							=      self.get_tokenizer()

       lowerCamelCase		:	Tuple							=      CLIPProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	Union[str, Any]							=      """lower newer"""

       lowerCamelCase		:	Dict							=      processor(text=__magic_name__      )

       lowerCamelCase		:	Dict							=      tokenizer(__magic_name__      )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      self.get_image_processor()
       lowerCamelCase		:	List[Any]							=      self.get_tokenizer()

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

       lowerCamelCase		:	Optional[Any]							=      """lower newer"""
       lowerCamelCase		:	List[Any]							=      self.prepare_image_inputs()

       lowerCamelCase		:	Union[str, Any]							=      processor(text=__magic_name__    ,  images=__magic_name__      )

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

       # test if it raises when no input is passed
       with pytest.raises(__magic_name__      ):
        processor()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[str]							=      self.get_image_processor()
       lowerCamelCase		:	Dict							=      self.get_tokenizer()

       lowerCamelCase		:	Union[str, Any]							=      CLIPProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

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

       lowerCamelCase		:	Union[str, Any]							=      processor.batch_decode(__magic_name__      )
       lowerCamelCase		:	Optional[int]							=      tokenizer.batch_decode(__magic_name__      )

       self.assertListEqual(__magic_name__    ,  __magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Optional[int]							=      self.get_image_processor()
       lowerCamelCase		:	Tuple							=      self.get_tokenizer()

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

       lowerCamelCase		:	Tuple							=      """lower newer"""
       lowerCamelCase		:	int							=      self.prepare_image_inputs()

       lowerCamelCase		:	Tuple							=      processor(text=__magic_name__    ,  images=__magic_name__      )

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






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_lowerCamelCase					   =logging.get_logger(__name__)





class 							A__		(	__SCREAMING_SNAKE_CASE):
      def __init__(      self    ,  *__magic_name__    ,  **__magic_name__      ):
       warnings.warn(
           """The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
           """ use OwlViTImageProcessor instead."""    ,  __magic_name__    ,  )
       super().__init__(*__magic_name__    ,  **__magic_name__      )




