


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   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Optional[Any]										=						['''pixel_values''']


	def __init__(    self				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = PIL.Image.BICUBIC				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = 1 / 2_5_5				,     lowercase__ = True				,     lowercase__ = True				,     lowercase__ = None				,     lowercase__ = None				,     **lowercase__				,     ):
							super().__init__(**lowercase__)
							__UpperCAmelCase			:							Union[str, Any]	    =	size if size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
							__UpperCAmelCase			:							Optional[int]	    =	get_size_dict(lowercase__)
							__UpperCAmelCase			:							str	    =	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
							__UpperCAmelCase			:							Any	    =	get_size_dict(lowercase__				,     param_name='''crop_size''')

							__UpperCAmelCase			:							Dict	    =	do_resize
							__UpperCAmelCase			:							Union[str, Any]	    =	size
							__UpperCAmelCase			:							Optional[Any]	    =	resample
							__UpperCAmelCase			:							Tuple	    =	do_center_crop
							__UpperCAmelCase			:							int	    =	crop_size
							__UpperCAmelCase			:							Tuple	    =	do_rescale
							__UpperCAmelCase			:							Dict	    =	rescale_factor
							__UpperCAmelCase			:							List[str]	    =	do_normalize
							__UpperCAmelCase			:							Optional[Any]	    =	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
							__UpperCAmelCase			:							str	    =	image_std if image_std is not None else IMAGENET_STANDARD_STD


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = PIL.Image.BICUBIC				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							List[str]	    =	get_size_dict(lowercase__)
							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(
							    lowercase__				,     size=(size['''height'''], size['''width'''])				,     resample=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Optional[int]	    =	get_size_dict(lowercase__)
							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(lowercase__				,     size=(size['''height'''], size['''width'''])				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							return rescale(lowercase__				,     scale=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__				,     lowercase__				,     lowercase__ = None				,     **lowercase__				,     ):
							return normalize(lowercase__				,     mean=lowercase__				,     std=lowercase__				,     data_format=lowercase__				,     **lowercase__)


	def  A(    self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__=None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = None				,     lowercase__ = ChannelDimension.FIRST				,     **lowercase__				,     ):
							__UpperCAmelCase			:							Union[str, Any]	    =	do_resize if do_resize is not None else self.do_resize
							__UpperCAmelCase			:							Dict	    =	resample if resample is not None else self.resample
							__UpperCAmelCase			:							str	    =	do_center_crop if do_center_crop is not None else self.do_center_crop
							__UpperCAmelCase			:							List[str]	    =	do_rescale if do_rescale is not None else self.do_rescale
							__UpperCAmelCase			:							List[str]	    =	rescale_factor if rescale_factor is not None else self.rescale_factor
							__UpperCAmelCase			:							Dict	    =	do_normalize if do_normalize is not None else self.do_normalize
							__UpperCAmelCase			:							Tuple	    =	image_mean if image_mean is not None else self.image_mean
							__UpperCAmelCase			:							Dict	    =	image_std if image_std is not None else self.image_std

							__UpperCAmelCase			:							str	    =	size if size is not None else self.size
							__UpperCAmelCase			:							Union[str, Any]	    =	get_size_dict(lowercase__)
							__UpperCAmelCase			:							int	    =	crop_size if crop_size is not None else self.crop_size
							__UpperCAmelCase			:							List[str]	    =	get_size_dict(lowercase__				,     param_name='''crop_size''')

							__UpperCAmelCase			:							Optional[Any]	    =	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_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.
							__UpperCAmelCase			:							List[Any]	    =	[to_numpy_array(lowercase__) for image in images]

							if do_resize:
													__UpperCAmelCase			:							Any	    =	[self.resize(image=lowercase__				,     size=lowercase__				,     resample=lowercase__) for image in images]

							if do_center_crop:
													__UpperCAmelCase			:							str	    =	[self.center_crop(image=lowercase__				,     size=lowercase__) for image in images]

							if do_rescale:
													__UpperCAmelCase			:							Union[str, Any]	    =	[self.rescale(image=lowercase__				,     scale=lowercase__) for image in images]

							if do_normalize:
													__UpperCAmelCase			:							Union[str, Any]	    =	[self.normalize(image=lowercase__				,     mean=lowercase__				,     std=lowercase__) for image in images]

							__UpperCAmelCase			:							List[str]	    =	[to_channel_dimension_format(lowercase__				,     lowercase__) for image in images]

							__UpperCAmelCase			:							List[Any]	    =	{'''pixel_values''': images}
							return BatchFeature(data=lowercase__				,     tensor_type=lowercase__)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase       		=    logging.get_logger(__name__)

lowerCAmelCase       		=    {
    """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json""",
}




class 							lowerCamelCase   (  _UpperCamelCase ):
	_lowerCAmelCase     :		Tuple										=						'''gpt_neox_japanese'''


	def __init__(    self				,     lowercase__=3_2_0_0_0				,     lowercase__=2_5_6_0				,     lowercase__=3_2				,     lowercase__=3_2				,     lowercase__=4				,     lowercase__="gelu"				,     lowercase__=1.0_0				,     lowercase__=1_0_0_0_0				,     lowercase__=2_0_4_8				,     lowercase__=0.0_2				,     lowercase__=1e-5				,     lowercase__=True				,     lowercase__=3_1_9_9_6				,     lowercase__=3_1_9_9_9				,     lowercase__=0.1				,     lowercase__=0.0				,     **lowercase__				,     ):
							super().__init__(bos_token_id=lowercase__				,     eos_token_id=lowercase__				,     **lowercase__)
							__UpperCAmelCase			:							Optional[Any]	    =	vocab_size
							__UpperCAmelCase			:							Dict	    =	max_position_embeddings
							__UpperCAmelCase			:							int	    =	hidden_size
							__UpperCAmelCase			:							List[Any]	    =	num_hidden_layers
							__UpperCAmelCase			:							Union[str, Any]	    =	num_attention_heads
							__UpperCAmelCase			:							Optional[int]	    =	intermediate_multiple_size
							__UpperCAmelCase			:							Any	    =	hidden_act
							__UpperCAmelCase			:							str	    =	rotary_pct
							__UpperCAmelCase			:							Any	    =	rotary_emb_base
							__UpperCAmelCase			:							Dict	    =	initializer_range
							__UpperCAmelCase			:							Tuple	    =	layer_norm_eps
							__UpperCAmelCase			:							Optional[int]	    =	use_cache
							__UpperCAmelCase			:							str	    =	attention_dropout
							__UpperCAmelCase			:							Any	    =	hidden_dropout

