



"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__snake_case				:  List[Any] =       logging.get_logger(__name__)



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      ['pixel_values']

	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						Optional[Dict[str, int]] = None			,     _SCREAMING_SNAKE_CASE:						PILImageResampling = PILImageResampling.BICUBIC			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						Union[int, float] = 1 / 255			,     _SCREAMING_SNAKE_CASE:						Dict[str, int] = None			,     _SCREAMING_SNAKE_CASE:						bool = True			,     _SCREAMING_SNAKE_CASE:						Optional[Union[float, List[float]]] = None			,     _SCREAMING_SNAKE_CASE:						Optional[Union[float, List[float]]] = None			,     **_SCREAMING_SNAKE_CASE:						str			,     )							->			None:

				"""simple docstring"""

				super().__init__(**_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			size if size is not None else {"height": 224, "width": 224}
				__lowerCAmelCase :    str				=			get_size_dict(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    int				=			crop_size if crop_size is not None else {"height": 224, "width": 224}
				__lowerCAmelCase :    str				=			get_size_dict(_SCREAMING_SNAKE_CASE			,     default_to_square=_SCREAMING_SNAKE_CASE			,     param_name="crop_size")

				__lowerCAmelCase :    Tuple				=			do_resize
				__lowerCAmelCase :    Optional[int]				=			do_rescale
				__lowerCAmelCase :    str				=			do_normalize
				__lowerCAmelCase :    str				=			do_center_crop
				__lowerCAmelCase :    Optional[Any]				=			crop_size
				__lowerCAmelCase :    str				=			size
				__lowerCAmelCase :    Union[str, Any]				=			resample
				__lowerCAmelCase :    Optional[int]				=			rescale_factor
				__lowerCAmelCase :    Any				=			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
				__lowerCAmelCase :    List[Any]				=			image_std if image_std is not None else IMAGENET_DEFAULT_STD

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						np.ndarray			,     _SCREAMING_SNAKE_CASE:						Dict[str, int]			,     _SCREAMING_SNAKE_CASE:						PILImageResampling = PILImageResampling.BILINEAR			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, ChannelDimension]] = None			,     **_SCREAMING_SNAKE_CASE:						Dict			,     )							->			np.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			get_size_dict(_SCREAMING_SNAKE_CASE)

				if "shortest_edge" in size:
							__lowerCAmelCase :    Tuple				=			get_resize_output_image_size(_SCREAMING_SNAKE_CASE			,     size=size["shortest_edge"]			,     default_to_square=_SCREAMING_SNAKE_CASE)
							# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
				elif "height" in size and "width" in size:
							__lowerCAmelCase :    Dict				=			(size["height"], size["width"])
				else:
							raise ValueError(F"""Size must contain 'height' and 'width' keys or 'shortest_edge' key. Got {size.keys()}""")
				return resize(_SCREAMING_SNAKE_CASE			,     size=_SCREAMING_SNAKE_CASE			,     resample=_SCREAMING_SNAKE_CASE			,     data_format=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						int			,     _SCREAMING_SNAKE_CASE:						np.ndarray			,     _SCREAMING_SNAKE_CASE:						Dict[str, int]			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, ChannelDimension]] = None			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			np.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			get_size_dict(_SCREAMING_SNAKE_CASE)
				if "height" not in size or "width" not in size:
							raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""")
				return center_crop(_SCREAMING_SNAKE_CASE			,     size=(size["height"], size["width"])			,     data_format=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						np.ndarray			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, ChannelDimension]] = None			,     **_SCREAMING_SNAKE_CASE:						Union[str, Any])							->			np.ndarray:

				"""simple docstring"""

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

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						np.ndarray			,     _SCREAMING_SNAKE_CASE:						Union[float, List[float]]			,     _SCREAMING_SNAKE_CASE:						Union[float, List[float]]			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, ChannelDimension]] = None			,     **_SCREAMING_SNAKE_CASE:						Optional[int]			,     )							->			np.ndarray:

				"""simple docstring"""

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







	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						ImageInput			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     _SCREAMING_SNAKE_CASE:						Dict[str, int] = None			,     _SCREAMING_SNAKE_CASE:						PILImageResampling = None			,     _SCREAMING_SNAKE_CASE:						bool = None			,     _SCREAMING_SNAKE_CASE:						int = None			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     _SCREAMING_SNAKE_CASE:						Optional[float] = None			,     _SCREAMING_SNAKE_CASE:						Optional[bool] = None			,     _SCREAMING_SNAKE_CASE:						Optional[Union[float, List[float]]] = None			,     _SCREAMING_SNAKE_CASE:						Optional[Union[float, List[float]]] = None			,     _SCREAMING_SNAKE_CASE:						Optional[Union[str, TensorType]] = None			,     _SCREAMING_SNAKE_CASE:						Union[str, ChannelDimension] = ChannelDimension.FIRST			,     **_SCREAMING_SNAKE_CASE:						int			,     )							->			BatchFeature:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			do_resize if do_resize is not None else self.do_resize
				__lowerCAmelCase :    Tuple				=			do_rescale if do_rescale is not None else self.do_rescale
				__lowerCAmelCase :    Union[str, Any]				=			do_normalize if do_normalize is not None else self.do_normalize
				__lowerCAmelCase :    Dict				=			do_center_crop if do_center_crop is not None else self.do_center_crop
				__lowerCAmelCase :    int				=			crop_size if crop_size is not None else self.crop_size
				__lowerCAmelCase :    Union[str, Any]				=			get_size_dict(_SCREAMING_SNAKE_CASE			,     param_name="crop_size"			,     default_to_square=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    int				=			resample if resample is not None else self.resample
				__lowerCAmelCase :    Any				=			rescale_factor if rescale_factor is not None else self.rescale_factor
				__lowerCAmelCase :    Dict				=			image_mean if image_mean is not None else self.image_mean
				__lowerCAmelCase :    Dict				=			image_std if image_std is not None else self.image_std

				__lowerCAmelCase :    Tuple				=			size if size is not None else self.size
				__lowerCAmelCase :    Tuple				=			get_size_dict(_SCREAMING_SNAKE_CASE)

				if not is_batched(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    Optional[Any]				=			[images]

				if not valid_images(_SCREAMING_SNAKE_CASE):
							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:
							raise ValueError("Size 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.")

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

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

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

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

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

				__lowerCAmelCase :    int				=			[to_channel_dimension_format(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) for image in images]

				__lowerCAmelCase :    List[str]				=			{"pixel_values": images}
				return BatchFeature(data=_SCREAMING_SNAKE_CASE			,     tensor_type=_SCREAMING_SNAKE_CASE)




"""simple docstring"""






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


__snake_case				:  Tuple =       logging.get_logger(__name__)

__snake_case				:  Any =       {
    'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/config.json',
    # See all XGLM models at https://huggingface.co/models?filter=xglm
}



class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      'xglm'
	SCREAMING_SNAKE_CASE        =      ['past_key_values']

	SCREAMING_SNAKE_CASE        =      {
	    'num_attention_heads': 'attention_heads',
	    'hidden_size': 'd_model',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[Any]=25_6008			,     _SCREAMING_SNAKE_CASE:						Dict=2048			,     _SCREAMING_SNAKE_CASE:						int=1024			,     _SCREAMING_SNAKE_CASE:						Dict=4096			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=24			,     _SCREAMING_SNAKE_CASE:						int=16			,     _SCREAMING_SNAKE_CASE:						List[str]="gelu"			,     _SCREAMING_SNAKE_CASE:						Optional[Any]=0.1			,     _SCREAMING_SNAKE_CASE:						int=0.1			,     _SCREAMING_SNAKE_CASE:						List[Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=0.0			,     _SCREAMING_SNAKE_CASE:						Any=0.02			,     _SCREAMING_SNAKE_CASE:						Any=True			,     _SCREAMING_SNAKE_CASE:						Tuple=True			,     _SCREAMING_SNAKE_CASE:						Any=2			,     _SCREAMING_SNAKE_CASE:						str=1			,     _SCREAMING_SNAKE_CASE:						Dict=0			,     _SCREAMING_SNAKE_CASE:						Dict=2			,     **_SCREAMING_SNAKE_CASE:						List[Any]			,     )							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			vocab_size
				__lowerCAmelCase :    int				=			max_position_embeddings
				__lowerCAmelCase :    Optional[Any]				=			d_model
				__lowerCAmelCase :    List[Any]				=			ffn_dim
				__lowerCAmelCase :    int				=			num_layers
				__lowerCAmelCase :    Any				=			attention_heads
				__lowerCAmelCase :    int				=			activation_function
				__lowerCAmelCase :    List[Any]				=			dropout
				__lowerCAmelCase :    Optional[int]				=			attention_dropout
				__lowerCAmelCase :    Optional[int]				=			activation_dropout
				__lowerCAmelCase :    Optional[int]				=			layerdrop
				__lowerCAmelCase :    Optional[int]				=			init_std
				__lowerCAmelCase :    Tuple				=			scale_embedding  # scale factor will be sqrt(d_model) if True
				__lowerCAmelCase :    Dict				=			use_cache

				super().__init__(
				    pad_token_id=_SCREAMING_SNAKE_CASE			,     bos_token_id=_SCREAMING_SNAKE_CASE			,     eos_token_id=_SCREAMING_SNAKE_CASE			,     decoder_start_token_id=_SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE			,     )