


"""simple docstring"""






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, 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


_lowerCamelCase  =	logging.get_logger(__name__)


class       lowerCamelCase_		(      lowercase ):





				"""simple docstring"""







				_lowerCAmelCase   :   str   				=       ["pixel_values"]



				def __init__(    self							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = PIL.Image.NEAREST							,		UpperCAmelCase__ = False							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = 1 / 255							,		UpperCAmelCase__ = False							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = True							,		**UpperCAmelCase__							,		):
								super().__init__(**UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					size if size is not None else {"height": 346, "width": 346}
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					crop_size if crop_size is not None else {"height": 289, "width": 289}
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(UpperCAmelCase__							,		param_name="crop_size"  )

								SCREAMING_SNAKE_CASE__  				=					do_resize
								SCREAMING_SNAKE_CASE__  				=					size
								SCREAMING_SNAKE_CASE__  				=					resample
								SCREAMING_SNAKE_CASE__  				=					do_center_crop
								SCREAMING_SNAKE_CASE__  				=					crop_size
								SCREAMING_SNAKE_CASE__  				=					do_rescale
								SCREAMING_SNAKE_CASE__  				=					rescale_factor
								SCREAMING_SNAKE_CASE__  				=					rescale_offset
								SCREAMING_SNAKE_CASE__  				=					do_normalize
								SCREAMING_SNAKE_CASE__  				=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								SCREAMING_SNAKE_CASE__  				=					image_std if image_std is not None else IMAGENET_STANDARD_STD
								SCREAMING_SNAKE_CASE__  				=					include_top



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = PIL.Image.NEAREST							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(UpperCAmelCase__  )
								if "height" not in size or "width" not in size:
												raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''  )
								return resize(
								    UpperCAmelCase__							,		size=(size["height"], size["width"])							,		resample=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(UpperCAmelCase__  )
								if "height" not in size or "width" not in size:
												raise ValueError(f'''The size dictionary must have keys \'height\' and \'width\'. Got {size.keys()}'''  )
								return center_crop(UpperCAmelCase__							,		size=(size["height"], size["width"])							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__  )



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = True							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								if offset:
												SCREAMING_SNAKE_CASE__  				=					(image - 127.5) * scale
												if data_format is not None:
																SCREAMING_SNAKE_CASE__  				=					to_channel_dimension_format(UpperCAmelCase__							,		UpperCAmelCase__  )
												SCREAMING_SNAKE_CASE__  				=					rescaled_image.astype(np.floataa  )
								else:
												SCREAMING_SNAKE_CASE__  				=					rescale(UpperCAmelCase__							,		scale=UpperCAmelCase__							,		data_format=UpperCAmelCase__							,		**UpperCAmelCase__  )
								return rescaled_image



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__							,		UpperCAmelCase__ = None							,		**UpperCAmelCase__							,		):
								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__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = None							,		UpperCAmelCase__ = ChannelDimension.FIRST							,		**UpperCAmelCase__							,		):
								SCREAMING_SNAKE_CASE__  				=					do_resize if do_resize is not None else self.do_resize
								SCREAMING_SNAKE_CASE__  				=					resample if resample is not None else self.resample
								SCREAMING_SNAKE_CASE__  				=					do_center_crop if do_center_crop is not None else self.do_center_crop
								SCREAMING_SNAKE_CASE__  				=					do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE__  				=					rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE__  				=					rescale_offset if rescale_offset is not None else self.rescale_offset
								SCREAMING_SNAKE_CASE__  				=					do_normalize if do_normalize is not None else self.do_normalize
								SCREAMING_SNAKE_CASE__  				=					image_mean if image_mean is not None else self.image_mean
								SCREAMING_SNAKE_CASE__  				=					image_std if image_std is not None else self.image_std
								SCREAMING_SNAKE_CASE__  				=					include_top if include_top is not None else self.include_top

								SCREAMING_SNAKE_CASE__  				=					size if size is not None else self.size
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(UpperCAmelCase__  )
								SCREAMING_SNAKE_CASE__  				=					crop_size if crop_size is not None else self.crop_size
								SCREAMING_SNAKE_CASE__  				=					get_size_dict(UpperCAmelCase__							,		param_name="crop_size"  )

								SCREAMING_SNAKE_CASE__  				=					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_center_crop and crop_size is None:
												raise ValueError("Crop size must be specified if do_center_crop 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."  )

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

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

								if do_center_crop:
												SCREAMING_SNAKE_CASE__  				=					[self.center_crop(image=UpperCAmelCase__							,		size=UpperCAmelCase__  ) for image in images]

								if do_rescale:
												SCREAMING_SNAKE_CASE__  				=					[self.rescale(image=UpperCAmelCase__							,		scale=UpperCAmelCase__							,		offset=UpperCAmelCase__  ) for image in images]

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

								if include_top:
												SCREAMING_SNAKE_CASE__  				=					[self.normalize(image=UpperCAmelCase__							,		mean=[0, 0, 0]							,		std=UpperCAmelCase__  ) for image in images]

								SCREAMING_SNAKE_CASE__  				=					[to_channel_dimension_format(UpperCAmelCase__							,		UpperCAmelCase__  ) for image in images]

								SCREAMING_SNAKE_CASE__  				=					{"pixel_values": images}
								return BatchFeature(data=UpperCAmelCase__							,		tensor_type=UpperCAmelCase__  )




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase  =	{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizer']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	['LlamaTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase  =	[
	    'OpenLlamaForCausalLM',
	    'OpenLlamaModel',
	    'OpenLlamaPreTrainedModel',
	    'OpenLlamaForSequenceClassification',
	]


if TYPE_CHECKING:
	from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from transformers import LlamaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_open_llama import (
		    OpenLlamaForCausalLM,
		    OpenLlamaForSequenceClassification,
		    OpenLlamaModel,
		    OpenLlamaPreTrainedModel,
		)


else:
	import sys

	_lowerCamelCase  =	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
