


'''simple docstring'''

from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_UpperCAmelCase				: Any							    =				logging.get_logger(__name__)


class       a__				( __A      ):







				"""simple docstring"""



				__UpperCamelCase    :		List[str]	=	['pixel_values']




				def __init__(self       ,			__lowercase = True       ,			__lowercase = 1 / 2_55       ,			__lowercase = True       ,			__lowercase = 8       ,			**__lowercase       ,			):
										super().__init__(**__lowercase		)

										__lowerCAmelCase							       =				do_rescale
										__lowerCAmelCase							       =				rescale_factor
										__lowerCAmelCase							       =				do_pad
										__lowerCAmelCase							       =				pad_size




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None       ,			**__lowercase		):
										return rescale(__lowercase       ,			scale=__lowercase       ,			data_format=__lowercase       ,			**__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase       ,			__lowercase = None		):
										__lowerCAmelCase       ,					__lowerCAmelCase							       =				get_image_size(__lowercase		)
										__lowerCAmelCase							       =				(old_height // size + 1) * size - old_height
										__lowerCAmelCase							       =				(old_width // size + 1) * size - old_width

										return pad(__lowercase       ,			((0, pad_height), (0, pad_width))       ,			mode='''symmetric'''       ,			data_format=__lowercase		)




				def 					_snake_case  (self       ,			__lowercase       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = None       ,			__lowercase = ChannelDimension.FIRST       ,			**__lowercase       ,			):
										__lowerCAmelCase							       =				do_rescale if do_rescale is not None else self.do_rescale
										__lowerCAmelCase							       =				rescale_factor if rescale_factor is not None else self.rescale_factor
										__lowerCAmelCase							       =				do_pad if do_pad is not None else self.do_pad
										__lowerCAmelCase							       =				pad_size if pad_size is not None else self.pad_size

										__lowerCAmelCase							       =				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_rescale and rescale_factor is None:
																raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

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

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

										if do_pad:
																__lowerCAmelCase							       =				[self.pad(__lowercase       ,			size=__lowercase		) for image in images]

										__lowerCAmelCase							       =				[to_channel_dimension_format(__lowercase       ,			__lowercase		) for image in images]

										__lowerCAmelCase							       =				{'''pixel_values''': images}
										return BatchFeature(data=__lowercase       ,			tensor_type=__lowercase		)





'''simple docstring'''

from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase				: str							    =				{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_UpperCAmelCase				: Union[str, Any]							    =				[
	    """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """GPTNeoXJapaneseForCausalLM""",
	    """GPTNeoXJapaneseLayer""",
	    """GPTNeoXJapaneseModel""",
	    """GPTNeoXJapanesePreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
	from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox_japanese import (
		    GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXJapaneseForCausalLM,
		    GPTNeoXJapaneseLayer,
		    GPTNeoXJapaneseModel,
		    GPTNeoXJapanesePreTrainedModel,
		)


else:
	import sys

	_UpperCAmelCase				: int							    =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

