




from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
  from PIL import Image

  from ..image_utils import load_image

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__lowerCAmelCase	:       Optional[int]     =	logging.get_logger(__name__)




@add_end_docstrings(_a     )
class 					snake_case__							(_a     ):
    """simple docstring"""



    def __init__(   self       :  List[str]      ,   *__lowerCamelCase       :  Dict      ,   **__lowerCamelCase       :  List[Any]							)      ->  str:
       super().__init__(*snake_case_      ,   **snake_case_							)
       self.check_model_type(snake_case_							)





    def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  str=None      ,   __lowerCamelCase       :  List[Any]=None      ,   __lowerCamelCase       :  Optional[int]=None      ,   **__lowerCamelCase       :  Union[str, Any]							)      ->  Dict:
       a					,	a					=    {}, {}
       if padding is not None:
          a					=    padding
       if truncation is not None:
          a					=    truncation
       if top_k is not None:
          a					=    top_k
       return preprocess_params, {}, postprocess_params





    def __call__(   self       :  List[str]      ,   __lowerCamelCase       :  Any      ,   __lowerCamelCase       :  Union[str, Any] = None      ,   **__lowerCamelCase       :  Tuple							)      ->  Any:
       if isinstance(snake_case_      ,   (Image.Image, str)							) and isinstance(snake_case_      ,   snake_case_							):
          a					=    {"image": image, "question": question}
       else:
          a					=    image
       a					=    super().__call__(snake_case_      ,   **snake_case_							)
       return results





    def 							__UpperCAmelCase							(   self       :  str      ,   __lowerCamelCase       :  str      ,   __lowerCamelCase       :  Optional[int]=False      ,   __lowerCamelCase       :  List[str]=False							)      ->  Tuple:
       a					=    load_image(inputs["image"]							)
       a					=    self.tokenizer(
           inputs["question"]      ,   return_tensors=self.framework      ,   padding=snake_case_      ,   truncation=snake_case_							)
       a					=    self.image_processor(images=snake_case_      ,   return_tensors=self.framework							)
       model_inputs.update(snake_case_							)
       return model_inputs





    def 							__UpperCAmelCase							(   self       :  List[str]      ,   __lowerCamelCase       :  List[str]							)      ->  List[str]:
       a					=    self.model(**snake_case_							)
       return model_outputs





    def 							__UpperCAmelCase							(   self       :  Tuple      ,   __lowerCamelCase       :  Optional[Any]      ,   __lowerCamelCase       :  List[Any]=5							)      ->  int:
       if top_k > self.model.config.num_labels:
          a					=    self.model.config.num_labels

       if self.framework == "pt":
          a					=    model_outputs.logits.sigmoid()[0]
          a					,	a					=    probs.topk(snake_case_							)
       else:
          raise ValueError(f"""Unsupported framework: {self.framework}"""							)

       a					=    scores.tolist()
       a					=    ids.tolist()
       return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(snake_case_      ,   snake_case_							)]












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 					snake_case__							(unittest.TestCase     ):
				"""simple docstring"""



				def 							__UpperCAmelCase							(   self       :  int							)      ->  Dict:
							a					=    tempfile.mkdtemp()

							a					=    [
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "的",
							    "价",
							    "格",
							    "是",
							    "15",
							    "便",
							    "alex",
							    "##andra",
							    "，",
							    "。",
							    "-",
							    "t",
							    "shirt",
							]
							a					=    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]							)							)

							a					=    {
							    "do_resize": True,
							    "size": {"height": 2_24, "width": 2_24},
							    "do_center_crop": True,
							    "crop_size": {"height": 18, "width": 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],
							    "do_convert_rgb": True,
							}
							a					=    os.path.join(self.tmpdirname      ,   __lowerCamelCase							)
							with open(self.image_processor_file      ,   "w"      ,   encoding="utf-8"							) as fp:
										json.dump(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict      ,   **__lowerCamelCase       :  Union[str, Any]							)      ->  List[Any]:
							return BertTokenizer.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  str      ,   **__lowerCamelCase       :  Optional[int]							)      ->  str:
							return BertTokenizerFast.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  List[str]      ,   **__lowerCamelCase       :  Optional[int]							)      ->  Tuple:
							return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname      ,   **__lowerCamelCase							)





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





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							a					=    [np.random.randint(2_55      ,   size=(3, 30, 4_00)      ,   dtype=np.uinta							)]

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

							return image_inputs





				def 							__UpperCAmelCase							(   self       :  int							)      ->  List[str]:
							a					=    self.get_tokenizer()
							a					=    self.get_rust_tokenizer()
							a					=    self.get_image_processor()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							processor_slow.save_pretrained(self.tmpdirname							)
							a					=    ChineseCLIPProcessor.from_pretrained(self.tmpdirname      ,   use_fast=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)
							processor_fast.save_pretrained(self.tmpdirname							)
							a					=    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      ,   __lowerCamelCase							)
							self.assertIsInstance(processor_fast.tokenizer      ,   __lowerCamelCase							)

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





				def 							__UpperCAmelCase							(   self       :  Optional[int]							)      ->  List[Any]:
							a					=    ChineseCLIPProcessor(tokenizer=self.get_tokenizer()      ,   image_processor=self.get_image_processor()							)
							processor.save_pretrained(self.tmpdirname							)

							a					=    self.get_tokenizer(cls_token="(CLS)"      ,   sep_token="(SEP)"							)
							a					=    self.get_image_processor(do_normalize=__lowerCamelCase							)

							a					=    ChineseCLIPProcessor.from_pretrained(
							    self.tmpdirname      ,   cls_token="(CLS)"      ,   sep_token="(SEP)"      ,   do_normalize=__lowerCamelCase							)

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

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





				def 							__UpperCAmelCase							(   self       :  Tuple							)      ->  Dict:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

							a					=    self.prepare_image_inputs()

							a					=    image_processor(__lowerCamelCase      ,   return_tensors="np"							)
							a					=    processor(images=__lowerCamelCase      ,   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 							__UpperCAmelCase							(   self       :  str							)      ->  Optional[int]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor(text=__lowerCamelCase							)

							a					=    tokenizer(__lowerCamelCase							)

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





				def 							__UpperCAmelCase							(   self       :  List[Any]							)      ->  Any:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

							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(__lowerCamelCase							):
										processor()





				def 							__UpperCAmelCase							(   self       :  List[str]							)      ->  Optional[int]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor.batch_decode(__lowerCamelCase							)
							a					=    tokenizer.batch_decode(__lowerCamelCase							)

							self.assertListEqual(__lowerCamelCase      ,   __lowerCamelCase							)





				def 							__UpperCAmelCase							(   self       :  Dict							)      ->  List[str]:
							a					=    self.get_image_processor()
							a					=    self.get_tokenizer()

							a					=    ChineseCLIPProcessor(tokenizer=__lowerCamelCase      ,   image_processor=__lowerCamelCase							)

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

							a					=    processor(text=__lowerCamelCase      ,   images=__lowerCamelCase							)

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






