




from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


_snake_case            = logging.get_logger(__name__)







class 		lowercase							(     UpperCamelCase__	):
     _a											=		["pixel_values"]

     def __init__(						self	,  _a = True	,  _a = None	,  _a = None	,  _a = PILImageResampling.BILINEAR	,  _a = True	,  _a = 1 / 255	,  _a = True	,  _a = None	,  _a = None	,  **_a	,  )		->							None:
           super().__init__(**_a       )
           _A							:  int    =		size if size is not None else {"""shortest_edge""": 384}
           _A							:  str    =		get_size_dict(_a	,  default_to_square=_a       )

           _A							:  str    =		do_resize
           _A							:  Optional[Any]    =		size
           # Default value set here for backwards compatibility where the value in config is None
           _A							:  List[str]    =		crop_pct if crop_pct is not None else 224 / 256
           _A							:  List[Any]    =		resample
           _A							:  Union[str, Any]    =		do_rescale
           _A							:  Optional[int]    =		rescale_factor
           _A							:  List[Any]    =		do_normalize
           _A							:  int    =		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           _A							:  Union[str, Any]    =		image_std if image_std is not None else IMAGENET_STANDARD_STD

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a = PILImageResampling.BICUBIC	,  _a = None	,  **_a	,  )		->							np.ndarray:
           _A							:  Dict    =		get_size_dict(_a	,  default_to_square=_a       )
           if "shortest_edge" not in size:
                 raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''       )
           _A							:  List[str]    =		size["""shortest_edge"""]

           if shortest_edge < 384:
                 # maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
                 _A							:  Any    =		int(shortest_edge / crop_pct       )
                 _A							:  Union[str, Any]    =		get_resize_output_image_size(_a	,  size=_a	,  default_to_square=_a       )
                 _A							:  Any    =		resize(image=_a	,  size=_a	,  resample=_a	,  data_format=_a	,  **_a       )
                 # then crop to (shortest_edge, shortest_edge)
                 return center_crop(image=_a	,  size=(shortest_edge, shortest_edge)	,  data_format=_a	,  **_a       )
           else:
                 # warping (no cropping) when evaluated at 384 or larger
                 return resize(
                     _a	,  size=(shortest_edge, shortest_edge)	,  resample=_a	,  data_format=_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							Dict:
           return rescale(_a	,  scale=_a	,  data_format=_a	,  **_a       )

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a = None	,  **_a	,  )		->							np.ndarray:
           return normalize(_a	,  mean=_a	,  std=_a	,  data_format=_a	,  **_a       )



     def       a__			(						self	,  _a	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = None	,  _a = ChannelDimension.FIRST	,  **_a	,  )		->							PIL.Image.Image:
           _A							:  Optional[int]    =		do_resize if do_resize is not None else self.do_resize
           _A							:  str    =		crop_pct if crop_pct is not None else self.crop_pct
           _A							:  Tuple    =		resample if resample is not None else self.resample
           _A							:  Union[str, Any]    =		do_rescale if do_rescale is not None else self.do_rescale
           _A							:  Any    =		rescale_factor if rescale_factor is not None else self.rescale_factor
           _A							:  Union[str, Any]    =		do_normalize if do_normalize is not None else self.do_normalize
           _A							:  Tuple    =		image_mean if image_mean is not None else self.image_mean
           _A							:  List[str]    =		image_std if image_std is not None else self.image_std

           _A							:  Any    =		size if size is not None else self.size
           _A							:  str    =		get_size_dict(_a	,  default_to_square=_a       )

           _A							:  List[str]    =		make_list_of_images(_a       )

           if not valid_images(_a       ):
                 raise ValueError(
                     """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                     """torch.Tensor, tf.Tensor or jax.ndarray."""       )

           if do_resize and size is None or resample is None:
                 raise ValueError("""Size and resample must be specified if do_resize is True."""       )

           if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
                 raise ValueError("""crop_pct must be specified if size < 384."""       )

           if do_rescale and rescale_factor is None:
                 raise ValueError("""Rescale factor must be specified if do_rescale is True."""       )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("""Image mean and std must be specified if do_normalize is True."""       )

           # All transformations expect numpy arrays.
           _A							:  List[Any]    =		[to_numpy_array(_a       ) for image in images]

           if do_resize:
                 _A							:  Dict    =		[self.resize(image=_a	,  size=_a	,  crop_pct=_a	,  resample=_a       ) for image in images]

           if do_rescale:
                 _A							:  Optional[int]    =		[self.rescale(image=_a	,  scale=_a       ) for image in images]

           if do_normalize:
                 _A							:  Optional[int]    =		[self.normalize(image=_a	,  mean=_a	,  std=_a       ) for image in images]

           _A							:  Union[str, Any]    =		[to_channel_dimension_format(_a	,  _a       ) for image in images]

           _A							:  Tuple    =		{"""pixel_values""": images}
           return BatchFeature(data=_a	,  tensor_type=_a       )







from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							Optional[int]:
           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]

     def       a__			(						self       )		->							Optional[int]:
           _A							:  Union[str, Any]    =		{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
           return Dataset.from_dict(_a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._create_example_records()
           _A							:  List[Any]    =		Dataset.from_list(_a       )
           self.assertListEqual(dset.column_names	,  ["""col_1""", """col_2"""]       )
           for i, r in enumerate(_a       ):
                 self.assertDictEqual(_a	,  example_records[i]       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Dict    =		self._create_example_records()
           _A							:  List[str]    =		Dataset.from_list(_a       )
           _A							:  str    =		Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
           self.assertEqual(dset.info	,  dset_from_dict.info       )

     def       a__			(						self       )		->							str:  # checks what happens with missing columns
           _A							:  List[str]    =		[{"""col_1""": 1}, {"""col_2""": """x"""}]
           _A							:  List[str]    =		Dataset.from_list(_a       )
           self.assertDictEqual(dset[0]	,  {"""col_1""": 1}       )
           self.assertDictEqual(dset[1]	,  {"""col_1""": None}       )  # NB: first record is used for columns

     def       a__			(						self       )		->							Dict:  # checks if the type can be inferred from the second record
           _A							:  List[Any]    =		[{"""col_1""": []}, {"""col_1""": [1, 2]}]
           _A							:  str    =		Dataset.from_list(_a       )
           self.assertEqual(dset.info.features["""col_1"""]	,  Sequence(Value("""int64"""       )       )       )



     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		Dataset.from_list([]       )
           self.assertEqual(len(_a       )	,  0       )
           self.assertListEqual(dset.column_names	,  []       )

