






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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_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


__UpperCAmelCase							:   Tuple							=  logging.get_logger(__name__)


class 					_snake_case						(			_A ):
	_A          =   ['pixel_values']






	def __init__(		self					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = PILImageResampling.BICUBIC					,UpperCamelCase = True					,UpperCamelCase = 1 / 255					,UpperCamelCase = True					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = True					,**UpperCamelCase					,)						->    None:
					super().__init__(**UpperCamelCase       )
					snake_case__      :Optional[Any]         =   size if size is not None else {"height": 384, "width": 384}
					snake_case__      :Tuple         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )

					snake_case__      :int         =   do_resize
					snake_case__      :Optional[int]         =   size
					snake_case__      :Optional[int]         =   resample
					snake_case__      :List[str]         =   do_rescale
					snake_case__      :Tuple         =   rescale_factor
					snake_case__      :Optional[Any]         =   do_normalize
					snake_case__      :Optional[int]         =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
					snake_case__      :List[str]         =   image_std if image_std is not None else OPENAI_CLIP_STD
					snake_case__      :Tuple         =   do_convert_rgb






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = PILImageResampling.BICUBIC					,UpperCamelCase = None					,**UpperCamelCase					,)						->    np.ndarray:
					snake_case__      :Optional[Any]         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )
					if "height" not in size or "width" not in size:
									raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'       )
					snake_case__      :str         =   (size["height"], size["width"])
					return resize(UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase					,UpperCamelCase = None					,**UpperCamelCase					,)						->    str:
					return rescale(UpperCamelCase					,scale=UpperCamelCase					,data_format=UpperCamelCase					,**UpperCamelCase       )






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






	def    lowerCAmelCase_							(		self					,UpperCamelCase					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = None					,UpperCamelCase = ChannelDimension.FIRST					,**UpperCamelCase					,)						->    PIL.Image.Image:
					snake_case__      :str         =   do_resize if do_resize is not None else self.do_resize
					snake_case__      :List[Any]         =   resample if resample is not None else self.resample
					snake_case__      :List[Any]         =   do_rescale if do_rescale is not None else self.do_rescale
					snake_case__      :Union[str, Any]         =   rescale_factor if rescale_factor is not None else self.rescale_factor
					snake_case__      :Any         =   do_normalize if do_normalize is not None else self.do_normalize
					snake_case__      :int         =   image_mean if image_mean is not None else self.image_mean
					snake_case__      :Tuple         =   image_std if image_std is not None else self.image_std
					snake_case__      :str         =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

					snake_case__      :Optional[Any]         =   size if size is not None else self.size
					snake_case__      :Union[str, Any]         =   get_size_dict(UpperCamelCase					,default_to_square=UpperCamelCase       )

					snake_case__      :Optional[Any]         =   make_list_of_images(UpperCamelCase       )

					if not valid_images(UpperCamelCase       ):
									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_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."       )

					# PIL RGBA images are converted to RGB
					if do_convert_rgb:
									snake_case__      :List[Any]         =   [convert_to_rgb(UpperCamelCase       ) for image in images]

					# All transformations expect numpy arrays.
					snake_case__      :str         =   [to_numpy_array(UpperCamelCase       ) for image in images]

					if do_resize:
									snake_case__      :List[Any]         =   [self.resize(image=UpperCamelCase					,size=UpperCamelCase					,resample=UpperCamelCase       ) for image in images]

					if do_rescale:
									snake_case__      :int         =   [self.rescale(image=UpperCamelCase					,scale=UpperCamelCase       ) for image in images]

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

					snake_case__      :str         =   [to_channel_dimension_format(UpperCamelCase					,UpperCamelCase       ) for image in images]

					snake_case__      :List[Any]         =   BatchFeature(data={"pixel_values": images}					,tensor_type=UpperCamelCase       )

					return encoded_outputs







import pytest


__UpperCAmelCase							:   int							=  "__dummy_dataset1__"

__UpperCAmelCase							:   int							=  "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n                \"ner_tags\": datasets.Sequence(\n                    datasets.features.ClassLabel(\n                        names=[\n                            \"O\",\n                            \"B-PER\",\n                            \"I-PER\",\n                            \"B-ORG\",\n                            \"I-ORG\",\n                            \"B-LOC\",\n                            \"I-LOC\",\n                        ]\n                    )\n                ),\n                \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n                \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n            }\n        )\n        return datasets.DatasetInfo(features=features)\n\n    def _split_generators(self, dl_manager):\n        dl_path = dl_manager.download(URLS)\n        return [\n            datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n            datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n        ]\n\n    def _generate_examples(self, filepath):\n        with open(filepath, \"r\", encoding=\"utf-8\") as f:\n            for i, line in enumerate(f):\n                yield i, json.loads(line)\n"





@pytest.fixture
def 					lowercase_ (    )     -> Optional[Any]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_NAME





@pytest.fixture
def 					lowercase_ (    )     -> Optional[int]:





				'''simple docstring'''


				return DATASET_LOADING_SCRIPT_CODE





@pytest.fixture
def 					lowercase_ (    __snake_case     :							Optional[int]	,	__snake_case     :							List[Any]	,	__snake_case     :							Any      )     -> Dict:





				'''simple docstring'''


				snake_case__      :Optional[Any]         =   dataset_loading_script_name
				snake_case__      :Optional[Any]         =   tmp_path / "datasets" / script_name
				script_dir.mkdir(parents=__snake_case      )
				snake_case__      :List[Any]         =   script_dir / F'{script_name}.py'
				with open(__snake_case	,	"w"      ) as f:
								f.write(__snake_case      )
				return str(__snake_case      )