

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


lowerCAmelCase__		      =       logging.get_logger(__name__)

class     a__  (    snake_case	):
			"""simple docstring"""







			__lowerCamelCase       				=				['pixel_values']


			def __init__(			self					,					lowercase = True					,					lowercase = None					,					lowercase = PILImageResampling.BICUBIC					,					lowercase = True					,					lowercase = 1 / 255					,					lowercase = True					,					lowercase = None					,					lowercase = None					,					lowercase = True					,					**lowercase					,					)     ->  None:
							'''simple docstring'''






							super().__init__(**lowercase				)
							A__						=	size if size is not None else {"height": 384, "width": 384}
							A__						=	get_size_dict(lowercase					,					default_to_square=lowercase				)

							A__						=	do_resize
							A__						=	size
							A__						=	resample
							A__						=	do_rescale
							A__						=	rescale_factor
							A__						=	do_normalize
							A__						=	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							A__						=	image_std if image_std is not None else OPENAI_CLIP_STD
							A__						=	do_convert_rgb


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = PILImageResampling.BICUBIC					,					lowercase = None					,					**lowercase					,					)     ->  np.ndarray:
							'''simple docstring'''






							A__						=	get_size_dict(lowercase					,					default_to_square=lowercase				)
							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()}'				)
							A__						=	(size["height"], size["width"])
							return resize(lowercase					,					size=lowercase					,					resample=lowercase					,					data_format=lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase = None					,					**lowercase					,					)     ->  Optional[Any]:
							'''simple docstring'''






							return rescale(lowercase					,					scale=lowercase					,					data_format=lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase					,					lowercase					,					lowercase = None					,					**lowercase					,					)     ->  np.ndarray:
							'''simple docstring'''






							return normalize(lowercase					,					mean=lowercase					,					std=lowercase					,					data_format=lowercase					,					**lowercase				)


			def    UpperCamelCase						(			self					,					lowercase					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = None					,					lowercase = ChannelDimension.FIRST					,					**lowercase					,					)     ->  PIL.Image.Image:
							'''simple docstring'''






							A__						=	do_resize if do_resize is not None else self.do_resize
							A__						=	resample if resample is not None else self.resample
							A__						=	do_rescale if do_rescale is not None else self.do_rescale
							A__						=	rescale_factor if rescale_factor is not None else self.rescale_factor
							A__						=	do_normalize if do_normalize is not None else self.do_normalize
							A__						=	image_mean if image_mean is not None else self.image_mean
							A__						=	image_std if image_std is not None else self.image_std
							A__						=	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							A__						=	size if size is not None else self.size
							A__						=	get_size_dict(lowercase					,					default_to_square=lowercase				)

							A__						=	make_list_of_images(lowercase				)

							if not valid_images(lowercase				):
											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:
											A__						=	[convert_to_rgb(lowercase				) for image in images]

							# All transformations expect numpy arrays.
							A__						=	[to_numpy_array(lowercase				) for image in images]

							if do_resize:
											A__						=	[self.resize(image=lowercase					,					size=lowercase					,					resample=lowercase				) for image in images]

							if do_rescale:
											A__						=	[self.rescale(image=lowercase					,					scale=lowercase				) for image in images]

							if do_normalize:
											A__						=	[self.normalize(image=lowercase					,					mean=lowercase					,					std=lowercase				) for image in images]

							A__						=	[to_channel_dimension_format(lowercase					,					lowercase				) for image in images]

							A__						=	BatchFeature(data={"pixel_values": images}					,					tensor_type=lowercase				)

							return encoded_outputs


# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a  :     Any       =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     List[Any]       =  [
		    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """CpmAntForCausalLM""",
		    """CpmAntModel""",
		    """CpmAntPreTrainedModel""",
		]


if TYPE_CHECKING:
		from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
		from .tokenization_cpmant import CpmAntTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_cpmant import (
				    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntPreTrainedModel,
				)


else:
		import sys

		__a  :     Any       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)