
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,
    convert_to_rgb,
    get_resize_output_image_size,
    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


lowerCamelCase_  :	List[str] 				=					logging.get_logger(__name__)


if is_vision_available():
		import PIL

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	["""pixel_values"""]

			def __init__(    self	:    Any  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    PILImageResampling = PILImageResampling.BICUBIC  ,		snake_case_	:    bool = True  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    bool = True  ,		snake_case_	:    Union[int, float] = 1 / 255  ,		snake_case_	:    bool = True  ,		snake_case_	:    Optional[Union[float, List[float]]] = None  ,		snake_case_	:    Optional[Union[float, List[float]]] = None  ,		snake_case_	:    bool = True  ,		**snake_case_	:    Optional[Any]  ,		):
							super().__init__(**snake_case_ )
							UpperCamelCase_:							Union[str, Any]		=  size if size is not None else {"""shortest_edge""": 224}
							UpperCamelCase_:							Dict		=  get_size_dict(snake_case_  ,		default_to_square=snake_case_ )
							UpperCamelCase_:							Any		=  crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
							UpperCamelCase_:							List[str]		=  get_size_dict(snake_case_  ,		default_to_square=snake_case_  ,		param_name="""crop_size""" )

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

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Dict[str, int]  ,		snake_case_	:    PILImageResampling = PILImageResampling.BICUBIC  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Union[str, Any]		=  get_size_dict(snake_case_  ,		default_to_square=snake_case_ )
							if "shortest_edge" not in size:
											raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
							UpperCamelCase_:							Dict		=  get_resize_output_image_size(snake_case_  ,		size=size["""shortest_edge"""]  ,		default_to_square=snake_case_ )
							return resize(snake_case_  ,		size=snake_case_  ,		resample=snake_case_  ,		data_format=snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Dict[str, int]  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Dict  ,		):
							UpperCamelCase_:							str		=  get_size_dict(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(snake_case_  ,		size=(size["""height"""], size["""width"""])  ,		data_format=snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    Dict  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Union[int, float]  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    Union[str, Any]  ,		):
							return rescale(snake_case_  ,		scale=snake_case_  ,		data_format=snake_case_  ,		**snake_case_ )

			def  lowerCAmelCase__		(    self	:    int  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    Union[float, List[float]]  ,		snake_case_	:    Union[float, List[float]]  ,		snake_case_	:    Optional[Union[str, ChannelDimension]] = None  ,		**snake_case_	:    List[str]  ,		):
							return normalize(snake_case_  ,		mean=snake_case_  ,		std=snake_case_  ,		data_format=snake_case_  ,		**snake_case_ )




			def  lowerCAmelCase__		(    self	:    Union[str, Any]  ,		snake_case_	:    ImageInput  ,		snake_case_	:    bool = None  ,		snake_case_	:    Dict[str, int] = None  ,		snake_case_	:    PILImageResampling = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    int = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    float = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[Union[float, List[float]]] = None  ,		snake_case_	:    Optional[Union[float, List[float]]] = None  ,		snake_case_	:    bool = None  ,		snake_case_	:    Optional[Union[str, TensorType]] = None  ,		snake_case_	:    Optional[ChannelDimension] = ChannelDimension.FIRST  ,		**snake_case_	:    List[Any]  ,		):
							UpperCamelCase_:							Any		=  do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_:							Tuple		=  size if size is not None else self.size
							UpperCamelCase_:							int		=  get_size_dict(snake_case_  ,		param_name="""size"""  ,		default_to_square=snake_case_ )
							UpperCamelCase_:							List[Any]		=  resample if resample is not None else self.resample
							UpperCamelCase_:							Tuple		=  do_center_crop if do_center_crop is not None else self.do_center_crop
							UpperCamelCase_:							int		=  crop_size if crop_size is not None else self.crop_size
							UpperCamelCase_:							Any		=  get_size_dict(snake_case_  ,		param_name="""crop_size"""  ,		default_to_square=snake_case_ )
							UpperCamelCase_:							Tuple		=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase_:							List[Any]		=  rescale_factor if rescale_factor is not None else self.rescale_factor
							UpperCamelCase_:							Any		=  do_normalize if do_normalize is not None else self.do_normalize
							UpperCamelCase_:							Union[str, Any]		=  image_mean if image_mean is not None else self.image_mean
							UpperCamelCase_:							List[Any]		=  image_std if image_std is not None else self.image_std
							UpperCamelCase_:							List[Any]		=  do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							UpperCamelCase_:							str		=  make_list_of_images(snake_case_ )

							if not valid_images(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.""" )

							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:
											UpperCamelCase_:							Dict		=  [convert_to_rgb(snake_case_ ) for image in images]

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Optional[int]		=  [to_numpy_array(snake_case_ ) for image in images]

							if do_resize:
											UpperCamelCase_:							Union[str, Any]		=  [self.resize(image=snake_case_  ,		size=snake_case_  ,		resample=snake_case_ ) for image in images]

							if do_center_crop:
											UpperCamelCase_:							Any		=  [self.center_crop(image=snake_case_  ,		size=snake_case_ ) for image in images]

							if do_rescale:
											UpperCamelCase_:							Tuple		=  [self.rescale(image=snake_case_  ,		scale=snake_case_ ) for image in images]

							if do_normalize:
											UpperCamelCase_:							Optional[int]		=  [self.normalize(image=snake_case_  ,		mean=snake_case_  ,		std=snake_case_ ) for image in images]

							UpperCamelCase_:							Tuple		=  [to_channel_dimension_format(snake_case_  ,		snake_case_ ) for image in images]

							UpperCamelCase_:							List[Any]		=  {"""pixel_values""": images}
							return BatchFeature(data=snake_case_  ,		tensor_type=snake_case_ )








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


lowerCamelCase_  :	Optional[int] 				=					logging.get_logger(__name__)

lowerCamelCase_  :	Optional[int] 				=					{"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  int    =	"""ctrl"""
			__UpperCamelCase :  Dict    =	["""past_key_values"""]
			__UpperCamelCase :  List[str]    =	{
			    """max_position_embeddings""": """n_positions""",
			    """hidden_size""": """n_embd""",
			    """num_attention_heads""": """n_head""",
			    """num_hidden_layers""": """n_layer""",
			}

			def __init__(    self	:    Dict  ,		snake_case_	:    Any=24_6534  ,		snake_case_	:    Dict=256  ,		snake_case_	:    str=1280  ,		snake_case_	:    Optional[int]=8192  ,		snake_case_	:    Union[str, Any]=48  ,		snake_case_	:    Any=16  ,		snake_case_	:    Optional[int]=0.1  ,		snake_case_	:    Any=0.1  ,		snake_case_	:    Any=1e-6  ,		snake_case_	:    Optional[Any]=0.02  ,		snake_case_	:    Optional[int]=True  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							Union[str, Any]		=  vocab_size
							UpperCamelCase_:							Union[str, Any]		=  n_positions
							UpperCamelCase_:							Optional[int]		=  n_embd
							UpperCamelCase_:							int		=  n_layer
							UpperCamelCase_:							str		=  n_head
							UpperCamelCase_:							Optional[int]		=  dff
							UpperCamelCase_:							Optional[Any]		=  resid_pdrop
							UpperCamelCase_:							Union[str, Any]		=  embd_pdrop
							UpperCamelCase_:							List[str]		=  layer_norm_epsilon
							UpperCamelCase_:							Optional[Any]		=  initializer_range

							UpperCamelCase_:							Optional[Any]		=  use_cache

							super().__init__(**snake_case_ )






