import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor


@require_vision
class      lowerCamelCase    (unittest.TestCase    ):
  """simple docstring"""







  def       __A   (							self       :				Any      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     tempfile.mkdtemp()

        SCREAMING_SNAKE_CASE_      =     [
            "[UNK]",
            "[CLS]",
            "[SEP]",
            "[PAD]",
            "[MASK]",
            "的",
            "价",
            "格",
            "是",
            "15",
            "便",
            "alex",
            "##andra",
            "，",
            "。",
            "-",
            "t",
            "shirt",
        ]
        SCREAMING_SNAKE_CASE_      =     os.path.join(self.tmpdirname     ,   VOCAB_FILES_NAMES["vocab_file"]      )
        with open(self.vocab_file     ,   "w"     ,   encoding="utf-8"      ) as vocab_writer:
              vocab_writer.write("".join([x + "\n" for x in vocab_tokens]      )      )

        SCREAMING_SNAKE_CASE_      =     {
            "do_resize": True,
            "size": {"height": 224, "width": 224},
            "do_center_crop": True,
            "crop_size": {"height": 18, "width": 18},
            "do_normalize": True,
            "image_mean": [0.4814_5466, 0.457_8275, 0.4082_1073],
            "image_std": [0.2686_2954, 0.2613_0258, 0.2757_7711],
            "do_convert_rgb": True,
        }
        SCREAMING_SNAKE_CASE_      =     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       __A   (							self       :				int     ,   **__magic_name__       :				str      )     ->		List[Any]:
        return BertTokenizer.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				List[Any]     ,   **__magic_name__       :				Optional[int]      )     ->		str:
        return BertTokenizerFast.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				Optional[Any]     ,   **__magic_name__       :				Tuple      )     ->		Union[str, Any]:
        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname     ,   **__magic_name__      )







  def       __A   (							self       :				Optional[int]      )     ->		Optional[Any]:
        shutil.rmtree(self.tmpdirname      )







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

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

        return image_inputs







  def       __A   (							self       :				Optional[int]      )     ->		Any:
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()
        SCREAMING_SNAKE_CASE_      =     self.get_rust_tokenizer()
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=__magic_name__     ,   image_processor=__magic_name__      )
        processor_slow.save_pretrained(self.tmpdirname      )
        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor.from_pretrained(self.tmpdirname     ,   use_fast=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=__magic_name__     ,   image_processor=__magic_name__      )
        processor_fast.save_pretrained(self.tmpdirname      )
        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor.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       __A   (							self       :				int      )     ->		Tuple:
        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=self.get_tokenizer()     ,   image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer(cls_token="(CLS)"     ,   sep_token="(SEP)"      )
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor(do_normalize=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname     ,   cls_token="(CLS)"     ,   sep_token="(SEP)"     ,   do_normalize=__magic_name__      )

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

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=__magic_name__     ,   image_processor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_      =     image_processor(__magic_name__     ,   return_tensors="np"      )
        SCREAMING_SNAKE_CASE_      =     processor(images=__magic_name__     ,   return_tensors="np"      )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()     ,   input_processor[key].sum()     ,   delta=1e-2      )







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

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=__magic_name__     ,   image_processor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "Alexandra，T-shirt的价格是15便士。"

        SCREAMING_SNAKE_CASE_      =     processor(text=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     tokenizer(__magic_name__      )

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







  def       __A   (							self       :				Optional[Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=__magic_name__     ,   image_processor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "Alexandra，T-shirt的价格是15便士。"
        SCREAMING_SNAKE_CASE_      =     self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_      =     processor(text=__magic_name__     ,   images=__magic_name__      )

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

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







  def       __A   (							self       :				str      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=__magic_name__     ,   image_processor=__magic_name__      )

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

        SCREAMING_SNAKE_CASE_      =     processor.batch_decode(__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     tokenizer.batch_decode(__magic_name__      )

        self.assertListEqual(__magic_name__     ,   __magic_name__      )







  def       __A   (							self       :				str      )     ->		Optional[int]:
        SCREAMING_SNAKE_CASE_      =     self.get_image_processor()
        SCREAMING_SNAKE_CASE_      =     self.get_tokenizer()

        SCREAMING_SNAKE_CASE_      =     ChineseCLIPProcessor(tokenizer=__magic_name__     ,   image_processor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     "Alexandra，T-shirt的价格是15便士。"
        SCREAMING_SNAKE_CASE_      =     self.prepare_image_inputs()

        SCREAMING_SNAKE_CASE_      =     processor(text=__magic_name__     ,   images=__magic_name__      )

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



from ...processing_utils import ProcessorMixin


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''feature_extractor''']
  lowerCamelCase__   =  '''TvltImageProcessor'''
  lowerCamelCase__   =  '''TvltFeatureExtractor'''







  def __init__(							self       :				List[str]     ,   __magic_name__       :				Any     ,   __magic_name__       :				Any      )     ->		int:
        super().__init__(image_processor=__magic_name__     ,   feature_extractor=__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     image_processor
        SCREAMING_SNAKE_CASE_      =     feature_extractor







  def __call__(							self       :				List[str]     ,   __magic_name__       :				Optional[Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				int=None     ,   __magic_name__       :				str=None     ,   __magic_name__       :				Any=False     ,   __magic_name__       :				int=False     ,   *__magic_name__       :				int     ,   **__magic_name__       :				Any     ,   )     ->		List[Any]:

        if images is None and audio is None:
              raise ValueError("You need to specify either an `images` or `audio` input to process."      )

        SCREAMING_SNAKE_CASE_      =     None
        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   mask_pixel=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if images_mixed is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   is_mixed=__magic_name__     ,   *__magic_name__     ,   **__magic_name__      )
        if audio is not None:
              SCREAMING_SNAKE_CASE_      =     self.feature_extractor(
                  __magic_name__     ,   *__magic_name__     ,   sampling_rate=__magic_name__     ,   mask_audio=__magic_name__     ,   **__magic_name__      )

        SCREAMING_SNAKE_CASE_      =     {}
        if audio is not None:
              output_dict.update(__magic_name__      )
        if images is not None:
              output_dict.update(__magic_name__      )
        if images_mixed_dict is not None:
              output_dict.update(__magic_name__      )
        return output_dict







  @property
  def       __A   (							self       :				Optional[Any]      )     ->		int:
        SCREAMING_SNAKE_CASE_      =     self.image_processor.model_input_names
        SCREAMING_SNAKE_CASE_      =     self.feature_extractor.model_input_names
        return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names      )      )


