






'''simple docstring'''







from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__A      :       Any      =	logging.get_logger(__name__)

__A      :       Optional[Any]      =	{
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}






class 					a__    (			a__						):
     '''simple docstring'''







     _snake_case             =  "gpt2"
     _snake_case             =  ["past_key_values"]
     _snake_case             =  {
         "hidden_size": "n_embd",
         "max_position_embeddings": "n_positions",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }






     def __init__(self    :	Any				,				lowercase__    :	Optional[int]=50257				,				lowercase__    :	Dict=1024				,				lowercase__    :	Optional[Any]=768				,				lowercase__    :	Optional[int]=12				,				lowercase__    :	Any=12				,				lowercase__    :	Optional[Any]=None				,				lowercase__    :	Union[str, Any]="gelu_new"				,				lowercase__    :	Any=0.1				,				lowercase__    :	Dict=0.1				,				lowercase__    :	Optional[Any]=0.1				,				lowercase__    :	Union[str, Any]=1e-5				,				lowercase__    :	Optional[Any]=0.02				,				lowercase__    :	str="cls_index"				,				lowercase__    :	List[str]=True				,				lowercase__    :	Union[str, Any]=None				,				lowercase__    :	Union[str, Any]=True				,				lowercase__    :	Optional[Any]=0.1				,				lowercase__    :	Dict=True				,				lowercase__    :	Dict=True				,				lowercase__    :	List[str]=50256				,				lowercase__    :	int=50256				,				lowercase__    :	Union[str, Any]=False				,				lowercase__    :	Optional[int]=False				,				**lowercase__    :	Dict				,				)  ->       List[Any]:
           __A		   =      vocab_size
           __A		   =      n_positions
           __A		   =      n_embd
           __A		   =      n_layer
           __A		   =      n_head
           __A		   =      n_inner
           __A		   =      activation_function
           __A		   =      resid_pdrop
           __A		   =      embd_pdrop
           __A		   =      attn_pdrop
           __A		   =      layer_norm_epsilon
           __A		   =      initializer_range
           __A		   =      summary_type
           __A		   =      summary_use_proj
           __A		   =      summary_activation
           __A		   =      summary_first_dropout
           __A		   =      summary_proj_to_labels
           __A		   =      scale_attn_weights
           __A		   =      use_cache
           __A		   =      scale_attn_by_inverse_layer_idx
           __A		   =      reorder_and_upcast_attn

           __A		   =      bos_token_id
           __A		   =      eos_token_id

           super().__init__(bos_token_id=SCREAMING_SNAKE_CASE_				,				eos_token_id=SCREAMING_SNAKE_CASE_				,				**SCREAMING_SNAKE_CASE_					)







class 					a__    (			a__						):
     '''simple docstring'''







     def __init__(self    :	Optional[int]				,				lowercase__    :	Optional[Any]				,				lowercase__    :	Optional[Any] = "default"				,				lowercase__    :	List[str] = None				,				lowercase__    :	Optional[int] = False				,				)  ->       Tuple:
           super().__init__(SCREAMING_SNAKE_CASE_				,				task=SCREAMING_SNAKE_CASE_				,				patching_specs=SCREAMING_SNAKE_CASE_				,				use_past=SCREAMING_SNAKE_CASE_					)
           if not getattr(self._config				,				"pad_token_id"				,				SCREAMING_SNAKE_CASE_					):
                 # TODO: how to do that better?
                 __A		   =      0






     @property
     def     _UpperCamelCase   (self    :	Any					)  ->       Mapping[str, Mapping[int, str]]:
           __A		   =      OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}					)
           if self.use_past:
                 self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_				,				direction="inputs"					)
                 __A		   =      {0: 'batch', 1: 'past_sequence + sequence'}
           else:
                 __A		   =      {0: 'batch', 1: 'sequence'}

           return common_inputs






     @property
     def     _UpperCamelCase   (self    :	int					)  ->       int:
           return self._config.n_layer






     @property
     def     _UpperCamelCase   (self    :	str					)  ->       int:
           return self._config.n_head






     def     _UpperCamelCase   (self    :	str				,				lowercase__    :	int				,				lowercase__    :	Optional[Any] = -1				,				lowercase__    :	Dict = -1				,				lowercase__    :	Dict = False				,				lowercase__    :	Optional[Any] = None				,				)  ->       Mapping[str, Any]:
           __A		   =      super(SCREAMING_SNAKE_CASE_				,				self					).generate_dummy_inputs(
               SCREAMING_SNAKE_CASE_				,				batch_size=SCREAMING_SNAKE_CASE_				,				seq_length=SCREAMING_SNAKE_CASE_				,				is_pair=SCREAMING_SNAKE_CASE_				,				framework=SCREAMING_SNAKE_CASE_					)

           # We need to order the input in the way they appears in the forward()
           __A		   =      OrderedDict({"input_ids": common_inputs["input_ids"]}					)

           # Need to add the past_keys
           if self.use_past:
                 if not is_torch_available():
                       raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."					)
                 else:
                       import torch

                       __A		   =      common_inputs['input_ids'].shape
                       # Not using the same length for past_key_values
                       __A		   =      seqlen + 2
                       __A		   =      (
                           batch,
                           self.num_attention_heads,
                           past_key_values_length,
                           self._config.hidden_size // self.num_attention_heads,
                       )
                       __A		   =      [
                           (torch.zeros(SCREAMING_SNAKE_CASE_					), torch.zeros(SCREAMING_SNAKE_CASE_					)) for _ in range(self.num_layers					)
                       ]

           __A		   =      common_inputs['attention_mask']
           if self.use_past:
                 __A		   =      ordered_inputs['attention_mask'].dtype
                 __A		   =      torch.cat(
                     [ordered_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				dtype=SCREAMING_SNAKE_CASE_					)]				,				dim=1					)

           return ordered_inputs






     @property
     def     _UpperCamelCase   (self    :	List[Any]					)  ->       int:
           return 13










'''simple docstring'''







from cva import destroyAllWindows, imread, imshow, waitKey


def 						_UpperCamelCase							(     snake_case__							,			snake_case__							):
      __A   ,				__A		   =      img.shape[0], img.shape[1]

      def to_grayscale(snake_case__							,			snake_case__							,			snake_case__							):
            return 0.2126 * red + 0.587 * green + 0.114 * blue

      def normalize(snake_case__							):
            return min(snake_case__							,			255							)

      for i in range(snake_case__							):
            for j in range(snake_case__							):
                  __A		   =      int(to_grayscale(*img[i][j]							)							)
                  __A		   =      [
                      normalize(snake_case__							),
                      normalize(greyscale + factor							),
                      normalize(greyscale + 2 * factor							),
                  ]

      return img


if __name__ == "__main__":
       # read original image
       __A      :       Dict      =	{
           percentage: imread('image_data/lena.jpg', 1) for percentage in (1_0, 2_0, 3_0, 4_0)
       }

       for percentage, img in images.items():
              make_sepia(img, percentage)

       for percentage, img in images.items():
              imshow(F"""Original image with sepia (factor: {percentage})""", img)

       waitKey(0)
       destroyAllWindows()


