



"""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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


snake_case_								=			logging.get_logger(__name__)


if is_vision_available():
				import PIL







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				["""pixel_values"""]






						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:Dict[str, int] = None  ,    lowercase_					:PILImageResampling = PILImageResampling.BILINEAR  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    lowercase_					:bool = True  ,    lowercase_					:bool = True  ,    lowercase_					:Union[int, float] = 1 / 2_55  ,    lowercase_					:bool = True  ,    lowercase_					:Optional[Union[float, List[float]]] = None  ,    lowercase_					:Optional[Union[float, List[float]]] = None  ,    **lowercase_					:Optional[int]  ,    )							->     None:
												super().__init__(**lowercase_   )

												UpperCAmelCase			 =					size if size is not None else {'height': 25_60, 'width': 19_20}
												if isinstance(lowercase_  ,    (tuple, list)   ):
																		# The previous feature extractor size parameter was in (width, height) format
																		UpperCAmelCase			 =					size[::-1]
												UpperCAmelCase			 =					get_size_dict(lowercase_   )

												UpperCAmelCase			 =					do_resize
												UpperCAmelCase			 =					size
												UpperCAmelCase			 =					resample
												UpperCAmelCase			 =					do_thumbnail
												UpperCAmelCase			 =					do_align_long_axis
												UpperCAmelCase			 =					do_pad
												UpperCAmelCase			 =					do_rescale
												UpperCAmelCase			 =					rescale_factor
												UpperCAmelCase			 =					do_normalize
												UpperCAmelCase			 =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												UpperCAmelCase			 =					image_std if image_std is not None else IMAGENET_STANDARD_STD






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None   )							->     np.ndarray:
												UpperCAmelCase ,       UpperCAmelCase			 =					get_image_size(lowercase_   )
												UpperCAmelCase ,       UpperCAmelCase			 =					size['height'], size['width']

												if (output_width < output_height and input_width > input_height) or (
												    output_width > output_height and input_width < input_height
												):
																		UpperCAmelCase			 =					np.rotaa(lowercase_  ,    3   )

												if data_format is not None:
																		UpperCAmelCase			 =					to_channel_dimension_format(lowercase_  ,    lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:bool = False  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    )							->     np.ndarray:
												UpperCAmelCase ,       UpperCAmelCase			 =					size['height'], size['width']
												UpperCAmelCase ,       UpperCAmelCase			 =					get_image_size(lowercase_   )

												UpperCAmelCase			 =					output_width - input_width
												UpperCAmelCase			 =					output_height - input_height

												if random_padding:
																		UpperCAmelCase			 =					np.random.randint(low=0  ,    high=delta_height + 1   )
																		UpperCAmelCase			 =					np.random.randint(low=0  ,    high=delta_width + 1   )
												else:
																		UpperCAmelCase			 =					delta_height // 2
																		UpperCAmelCase			 =					delta_width // 2

												UpperCAmelCase			 =					delta_height - pad_top
												UpperCAmelCase			 =					delta_width - pad_left

												UpperCAmelCase			 =					((pad_top, pad_bottom), (pad_left, pad_right))
												return pad(lowercase_  ,    lowercase_  ,    data_format=lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    *lowercase_					:Dict  ,    **lowercase_					:int   )							->     int:
												logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'   )
												return self.pad_image(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:PILImageResampling = PILImageResampling.BICUBIC  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    **lowercase_					:int  ,    )							->     np.ndarray:
												UpperCAmelCase ,       UpperCAmelCase			 =					get_image_size(lowercase_   )
												UpperCAmelCase ,       UpperCAmelCase			 =					size['height'], size['width']

												# We always resize to the smallest of either the input or output size.
												UpperCAmelCase			 =					min(lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					min(lowercase_  ,    lowercase_   )

												if height == input_height and width == input_width:
																		return image

												if input_height > input_width:
																		UpperCAmelCase			 =					int(input_width * height / input_height   )
												elif input_width > input_height:
																		UpperCAmelCase			 =					int(input_height * width / input_width   )

												return resize(
												    lowercase_  ,    size=(height, width)  ,    resample=lowercase_  ,    reducing_gap=2.0  ,    data_format=lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:PILImageResampling = PILImageResampling.BICUBIC  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    **lowercase_					:int  ,    )							->     np.ndarray:
												UpperCAmelCase			 =					get_size_dict(lowercase_   )
												UpperCAmelCase			 =					min(size['height']  ,    size['width']   )
												UpperCAmelCase			 =					get_resize_output_image_size(lowercase_  ,    size=lowercase_  ,    default_to_square=lowercase_   )
												UpperCAmelCase			 =					resize(lowercase_  ,    size=lowercase_  ,    resample=lowercase_  ,    data_format=lowercase_  ,    **lowercase_   )
												return resized_image






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Union[int, float]  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    **lowercase_					:List[Any]  ,    )							->     List[str]:
												return rescale(lowercase_  ,    scale=lowercase_  ,    data_format=lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Union[float, List[float]]  ,    lowercase_					:Union[float, List[float]]  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    **lowercase_					:Optional[int]  ,    )							->     np.ndarray:
												return normalize(lowercase_  ,    mean=lowercase_  ,    std=lowercase_  ,    data_format=lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:ImageInput  ,    lowercase_					:bool = None  ,    lowercase_					:Dict[str, int] = None  ,    lowercase_					:PILImageResampling = None  ,    lowercase_					:bool = None  ,    lowercase_					:bool = None  ,    lowercase_					:bool = None  ,    lowercase_					:bool = False  ,    lowercase_					:bool = None  ,    lowercase_					:float = None  ,    lowercase_					:bool = None  ,    lowercase_					:Optional[Union[float, List[float]]] = None  ,    lowercase_					:Optional[Union[float, List[float]]] = None  ,    lowercase_					:Optional[Union[str, TensorType]] = None  ,    lowercase_					:Optional[ChannelDimension] = ChannelDimension.FIRST  ,    **lowercase_					:List[str]  ,    )							->     PIL.Image.Image:
												UpperCAmelCase			 =					do_resize if do_resize is not None else self.do_resize
												UpperCAmelCase			 =					size if size is not None else self.size
												if isinstance(lowercase_  ,    (tuple, list)   ):
																		# Previous feature extractor had size in (width, height) format
																		UpperCAmelCase			 =					size[::-1]
												UpperCAmelCase			 =					get_size_dict(lowercase_   )
												UpperCAmelCase			 =					resample if resample is not None else self.resample
												UpperCAmelCase			 =					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
												UpperCAmelCase			 =					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
												UpperCAmelCase			 =					do_pad if do_pad is not None else self.do_pad
												UpperCAmelCase			 =					do_rescale if do_rescale is not None else self.do_rescale
												UpperCAmelCase			 =					rescale_factor if rescale_factor is not None else self.rescale_factor
												UpperCAmelCase			 =					do_normalize if do_normalize is not None else self.do_normalize
												UpperCAmelCase			 =					image_mean if image_mean is not None else self.image_mean
												UpperCAmelCase			 =					image_std if image_std is not None else self.image_std

												UpperCAmelCase			 =					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:
																		raise ValueError('Size must be specified if do_resize is True.'   )

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

												if do_pad and size is None:
																		raise ValueError('Size must be specified if do_pad 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			 =					[to_numpy_array(lowercase_   ) for image in images]

												if do_align_long_axis:
																		UpperCAmelCase			 =					[self.align_long_axis(lowercase_  ,    size=lowercase_   ) for image in images]

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

												if do_thumbnail:
																		UpperCAmelCase			 =					[self.thumbnail(image=lowercase_  ,    size=lowercase_   ) for image in images]

												if do_pad:
																		UpperCAmelCase			 =					[self.pad_image(image=lowercase_  ,    size=lowercase_  ,    random_padding=lowercase_   ) for image in images]

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

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

												UpperCAmelCase			 =					[to_channel_dimension_format(lowercase_  ,    lowercase_   ) for image in images]

												UpperCAmelCase			 =					{'pixel_values': images}
												return BatchFeature(data=lowercase_  ,    tensor_type=lowercase_   )






"""simple docstring"""







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


_SCREAMING_SNAKE_CASE	:		str   =   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	:		Any   =   {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Tuple    =    "opt"
   __UpperCAmelCase	:							Any    =    ["past_key_values"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE=50272 ,				_SCREAMING_SNAKE_CASE=768 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE=3072 ,				_SCREAMING_SNAKE_CASE=2048 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=False ,				_SCREAMING_SNAKE_CASE=None ,				_SCREAMING_SNAKE_CASE=0.1 ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=12 ,				_SCREAMING_SNAKE_CASE="relu" ,				_SCREAMING_SNAKE_CASE=0.0 ,				_SCREAMING_SNAKE_CASE=0.02 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=1 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=2 ,				_SCREAMING_SNAKE_CASE=True ,				_SCREAMING_SNAKE_CASE=True ,				**_SCREAMING_SNAKE_CASE ,				):


      '''simple docstring'''

      super().__init__(
          pad_token_id=_SCREAMING_SNAKE_CASE ,				bos_token_id=_SCREAMING_SNAKE_CASE ,				eos_token_id=_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					vocab_size
      __lowerCamelCase    		=					max_position_embeddings
      __lowerCamelCase    		=					num_attention_heads
      __lowerCamelCase    		=					word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
      __lowerCamelCase    		=					ffn_dim
      __lowerCamelCase    		=					hidden_size
      __lowerCamelCase    		=					num_hidden_layers
      __lowerCamelCase    		=					dropout
      __lowerCamelCase    		=					attention_dropout
      __lowerCamelCase    		=					activation_function
      __lowerCamelCase    		=					init_std
      __lowerCamelCase    		=					layerdrop
      __lowerCamelCase    		=					use_cache
      __lowerCamelCase    		=					do_layer_norm_before
      # We keep these variables at `True` for backward compatibility.
      __lowerCamelCase    		=					enable_bias
      __lowerCamelCase    		=					layer_norm_elementwise_affine

      # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
      # with checkpoints that have been fine-tuned before transformers v4.20.1
      # see https://github.com/facebookresearch/metaseq/pull/164
      __lowerCamelCase    		=					_remove_final_layer_norm






