





'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ChineseCLIPImageProcessor







class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def __init__(					self      : str	,    A__      : Dict	,    A__      : str=7	,    A__      : int=3	,    A__      : Optional[Any]=18	,    A__      : Tuple=30	,    A__      : Tuple=400	,    A__      : List[str]=True	,    A__      : Optional[int]=None	,    A__      : Optional[Any]=True	,    A__      : Optional[int]=None	,    A__      : Optional[Any]=True	,    A__      : List[Any]=[0.4814_5466, 0.457_8275, 0.4082_1073]	,    A__      : List[Any]=[0.2686_2954, 0.2613_0258, 0.2757_7711]	,    A__      : Optional[Any]=True	,    ):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				size if size is not None else {'height': 224, 'width': 224}
            __lowerCamelCase						:       Any				   =				crop_size if crop_size is not None else {'height': 18, 'width': 18}
            __lowerCamelCase						:       Optional[int]				   =				parent
            __lowerCamelCase						:       Dict				   =				batch_size
            __lowerCamelCase						:       str				   =				num_channels
            __lowerCamelCase						:       Optional[Any]				   =				image_size
            __lowerCamelCase						:       Optional[int]				   =				min_resolution
            __lowerCamelCase						:       Tuple				   =				max_resolution
            __lowerCamelCase						:       Optional[int]				   =				do_resize
            __lowerCamelCase						:       Optional[int]				   =				size
            __lowerCamelCase						:       Union[str, Any]				   =				do_center_crop
            __lowerCamelCase						:       List[Any]				   =				crop_size
            __lowerCamelCase						:       int				   =				do_normalize
            __lowerCamelCase						:       Optional[Any]				   =				image_mean
            __lowerCamelCase						:       str				   =				image_std
            __lowerCamelCase						:       Optional[Any]				   =				do_convert_rgb




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            return {
                "do_resize": self.do_resize,
                "size": self.size,
                "do_center_crop": self.do_center_crop,
                "crop_size": self.crop_size,
                "do_normalize": self.do_normalize,
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_convert_rgb": self.do_convert_rgb,
            }




       def 					a_					(					self      : Tuple	,    A__      : List[Any]=False	,    A__      : Tuple=False	,    A__      : Any=False					):


            """simple docstring"""







            assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

            if equal_resolution:
                 __lowerCamelCase						:       Optional[int]				   =				[]
                 for i in range(self.batch_size					):
                      image_inputs.append(
                          np.random.randint(
                              255	,    size=(self.num_channels, self.max_resolution, self.max_resolution)	,    dtype=np.uinta					)					)
            else:
                 __lowerCamelCase						:       Dict				   =				[]
                 for i in range(self.batch_size					):
                      __lowerCamelCase						:       Optional[Any]				   =				np.random.choice(np.arange(self.min_resolution	,    self.max_resolution					)	,    2					)
                      image_inputs.append(np.random.randint(255	,    size=(self.num_channels, width, height)	,    dtype=np.uinta					)					)

            if not numpify and not torchify:
                 # PIL expects the channel dimension as last dimension
                 __lowerCamelCase						:       int				   =				[Image.fromarray(np.moveaxis(__a	,    0	,    -1					)					) for x in image_inputs]

            if torchify:
                 __lowerCamelCase						:       List[Any]				   =				[torch.from_numpy(__a					) for x in image_inputs]

            return image_inputs







@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE	(      UpperCamelCase_    ,   unittest.TestCase  ):
       snake_case__    :     List[Any]   		=			ChineseCLIPImageProcessor if is_vision_available() else None




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				ChineseCLIPImageProcessingTester(self	,    do_center_crop=__a					)




       @property
       def 					a_					(					self      : int					):


            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(__a	,    """do_resize"""					)					)
            self.assertTrue(hasattr(__a	,    """size"""					)					)
            self.assertTrue(hasattr(__a	,    """do_center_crop"""					)					)
            self.assertTrue(hasattr(__a	,    """center_crop"""					)					)
            self.assertTrue(hasattr(__a	,    """do_normalize"""					)					)
            self.assertTrue(hasattr(__a	,    """image_mean"""					)					)
            self.assertTrue(hasattr(__a	,    """image_std"""					)					)
            self.assertTrue(hasattr(__a	,    """do_convert_rgb"""					)					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[int]				   =				self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size	,    {"""height""": 224, """width""": 224}					)
            self.assertEqual(image_processor.crop_size	,    {"""height""": 18, """width""": 18}					)

            __lowerCamelCase						:       List[str]				   =				self.image_processing_class.from_dict(self.image_processor_dict	,    size=42	,    crop_size=84					)
            self.assertEqual(image_processor.size	,    {"""shortest_edge""": 42}					)
            self.assertEqual(image_processor.crop_size	,    {"""height""": 84, """width""": 84}					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            pass




       def 					a_					(					self      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            __lowerCamelCase						:       Optional[Any]				   =				self.image_processor_tester.prepare_inputs(equal_resolution=__a					)
            for image in image_inputs:
                 self.assertIsInstance(__a	,    Image.Image					)

            # Test not batched input
            __lowerCamelCase						:       int				   =				image_processing(image_inputs[0]	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       List[Any]				   =				image_processing(__a	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            __lowerCamelCase						:       Optional[int]				   =				self.image_processor_tester.prepare_inputs(equal_resolution=__a	,    numpify=__a					)
            for image in image_inputs:
                 self.assertIsInstance(__a	,    np.ndarray					)

            # Test not batched input
            __lowerCamelCase						:       List[Any]				   =				image_processing(image_inputs[0]	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       int				   =				image_processing(__a	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            __lowerCamelCase						:       Tuple				   =				self.image_processor_tester.prepare_inputs(equal_resolution=__a	,    torchify=__a					)
            for image in image_inputs:
                 self.assertIsInstance(__a	,    torch.Tensor					)

            # Test not batched input
            __lowerCamelCase						:       Any				   =				image_processing(image_inputs[0]	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    1,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       Union[str, Any]				   =				image_processing(__a	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )







@require_torch
@require_vision
class 	SCREAMING_SNAKE_CASE	(      UpperCamelCase_    ,   unittest.TestCase  ):
       snake_case__    :     Dict   		=			ChineseCLIPImageProcessor if is_vision_available() else None




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				ChineseCLIPImageProcessingTester(self	,    num_channels=4	,    do_center_crop=__a					)
            __lowerCamelCase						:       List[Any]				   =				3




       @property
       def 					a_					(					self      : Dict					):


            """simple docstring"""







            return self.image_processor_tester.prepare_image_processor_dict()




       def 					a_					(					self      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				self.image_processing_class(**self.image_processor_dict					)
            self.assertTrue(hasattr(__a	,    """do_resize"""					)					)
            self.assertTrue(hasattr(__a	,    """size"""					)					)
            self.assertTrue(hasattr(__a	,    """do_center_crop"""					)					)
            self.assertTrue(hasattr(__a	,    """center_crop"""					)					)
            self.assertTrue(hasattr(__a	,    """do_normalize"""					)					)
            self.assertTrue(hasattr(__a	,    """image_mean"""					)					)
            self.assertTrue(hasattr(__a	,    """image_std"""					)					)
            self.assertTrue(hasattr(__a	,    """do_convert_rgb"""					)					)




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            pass




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            __lowerCamelCase						:       Union[str, Any]				   =				self.image_processor_tester.prepare_inputs(equal_resolution=__a					)
            for image in image_inputs:
                 self.assertIsInstance(__a	,    Image.Image					)

            # Test not batched input
            __lowerCamelCase						:       Optional[int]				   =				image_processing(image_inputs[0]	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    1,
                    self.expected_encoded_image_num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )

            # Test batched
            __lowerCamelCase						:       Optional[int]				   =				image_processing(__a	,    return_tensors="""pt"""					).pixel_values
            self.assertEqual(
                encoded_images.shape	,    (
                    self.image_processor_tester.batch_size,
                    self.expected_encoded_image_num_channels,
                    self.image_processor_tester.crop_size["""height"""],
                    self.image_processor_tester.crop_size["""width"""],
                )	,    )











'''simple docstring'''

import argparse
import json

from tqdm import tqdm
def     __lowercase  ()   ->     Tuple:


     """simple docstring"""
     __lowerCamelCase						:       Union[str, Any]				   =				argparse.ArgumentParser()

     # Required parameters
     parser.add_argument(
         """--src_path""",						type=_lowercase,						default="""biencoder-nq-dev.json""",						help="""Path to raw DPR training data""",						)
     parser.add_argument(
         """--evaluation_set""",						type=_lowercase,						help="""where to store parsed evaluation_set file""",						)
     parser.add_argument(
         """--gold_data_path""",						type=_lowercase,						help="""where to store parsed gold_data_path file""",						)
     __lowerCamelCase						:       Tuple				   =				parser.parse_args()

     with open(args.src_path,						"""r"""     ) as src_file, open(args.evaluation_set,						"""w"""     ) as eval_file, open(
         args.gold_data_path,						"""w"""     ) as gold_file:
          __lowerCamelCase						:       int				   =				json.load(_lowercase     )
          for dpr_record in tqdm(_lowercase     ):
               __lowerCamelCase						:       List[Any]				   =				dpr_record["""question"""]
               __lowerCamelCase						:       List[str]				   =				[context["""title"""] for context in dpr_record["""positive_ctxs"""]]
               eval_file.write(question + """\n"""     )
               gold_file.write("""\t""".join(_lowercase     ) + """\n"""     )


if __name__ == "__main__":
   main()



