
"""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									=				logging.get_logger(__name__)

_a									=				{
    '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_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		List[Any]    	=						"""gpt2"""
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						["""past_key_values"""]
  SCREAMING_SNAKE_CASE__     :		str    	=						{
      """hidden_size""": """n_embd""",
      """max_position_embeddings""": """n_positions""",
      """num_attention_heads""": """n_head""",
      """num_hidden_layers""": """n_layer""",
  }







  def __init__(						self							,  lowercase_=5_0257							,  lowercase_=1024							,  lowercase_=768							,  lowercase_=12							,  lowercase_=12							,  lowercase_=None							,  lowercase_="gelu_new"							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=0.1							,  lowercase_=1E-5							,  lowercase_=0.02							,  lowercase_="cls_index"							,  lowercase_=True							,  lowercase_=None							,  lowercase_=True							,  lowercase_=0.1							,  lowercase_=True							,  lowercase_=True							,  lowercase_=5_0256							,  lowercase_=5_0256							,  lowercase_=False							,  lowercase_=False							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       vocab_size
     UpperCAmelCase_   :   List[Any]     	=       n_positions
     UpperCAmelCase_   :   str     	=       n_embd
     UpperCAmelCase_   :   str     	=       n_layer
     UpperCAmelCase_   :   Tuple     	=       n_head
     UpperCAmelCase_   :   int     	=       n_inner
     UpperCAmelCase_   :   Any     	=       activation_function
     UpperCAmelCase_   :   Optional[int]     	=       resid_pdrop
     UpperCAmelCase_   :   List[str]     	=       embd_pdrop
     UpperCAmelCase_   :   str     	=       attn_pdrop
     UpperCAmelCase_   :   Optional[Any]     	=       layer_norm_epsilon
     UpperCAmelCase_   :   Optional[int]     	=       initializer_range
     UpperCAmelCase_   :   Union[str, Any]     	=       summary_type
     UpperCAmelCase_   :   Tuple     	=       summary_use_proj
     UpperCAmelCase_   :   Dict     	=       summary_activation
     UpperCAmelCase_   :   List[Any]     	=       summary_first_dropout
     UpperCAmelCase_   :   str     	=       summary_proj_to_labels
     UpperCAmelCase_   :   str     	=       scale_attn_weights
     UpperCAmelCase_   :   List[str]     	=       use_cache
     UpperCAmelCase_   :   Union[str, Any]     	=       scale_attn_by_inverse_layer_idx
     UpperCAmelCase_   :   Tuple     	=       reorder_and_upcast_attn

     UpperCAmelCase_   :   int     	=       bos_token_id
     UpperCAmelCase_   :   Any     	=       eos_token_id

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




class A_      (lowercase__ ):




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_ = "default"							,  lowercase_ = None							,  lowercase_ = False							,  ):

     """simple docstring"""
     super().__init__(lowercase_							,  task=lowercase_							,  patching_specs=lowercase_							,  use_past=lowercase_    )
     if not getattr(self._config							,  "pad_token_id"							,  lowercase_    ):
        # TODO: how to do that better?
        UpperCAmelCase_   :   int     	=       0







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Tuple     	=       OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}    )
     if self.use_past:
        self.fill_with_past_key_values_(lowercase_							,  direction="inputs"    )
        UpperCAmelCase_   :   Dict     	=       {0: "batch", 1: "past_sequence + sequence"}
     else:
        UpperCAmelCase_   :   List[Any]     	=       {0: "batch", 1: "sequence"}

     return common_inputs







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self._config.n_layer







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return self._config.n_head







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = -1							,  lowercase_ = -1							,  lowercase_ = False							,  lowercase_ = None							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   List[Any]     	=       super(lowercase_							,  self    ).generate_dummy_inputs(
         lowercase_							,  batch_size=lowercase_							,  seq_length=lowercase_							,  is_pair=lowercase_							,  framework=lowercase_    )

     # We need to order the input in the way they appears in the forward()
     UpperCAmelCase_   :   Any     	=       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

           UpperCAmelCase_	,  UpperCAmelCase_   :   Tuple     	=       common_inputs["input_ids"].shape
           # Not using the same length for past_key_values
           UpperCAmelCase_   :   Any     	=       seqlen + 2
           UpperCAmelCase_   :   str     	=       (
               batch,
               self.num_attention_heads,
               past_key_values_length,
               self._config.hidden_size // self.num_attention_heads,
           )
           UpperCAmelCase_   :   Dict     	=       [
               (torch.zeros(lowercase_    ), torch.zeros(lowercase_    )) for _ in range(self.num_layers    )
           ]

     UpperCAmelCase_   :   int     	=       common_inputs["attention_mask"]
     if self.use_past:
        UpperCAmelCase_   :   List[Any]     	=       ordered_inputs["attention_mask"].dtype
        UpperCAmelCase_   :   Union[str, Any]     	=       torch.cat(
            [ordered_inputs["attention_mask"], torch.ones(lowercase_							,  lowercase_							,  dtype=lowercase_    )]							,  dim=1    )

     return ordered_inputs







  @property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return 13




"""simple docstring"""





import cva
import numpy as np







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):

   try:
      return int(image[x_coordinate][y_coordinate] >= center    )
   except (IndexError, TypeError):
      return 0







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[int]     	=       image[x_coordinate][y_coordinate]
   UpperCAmelCase_   :   int     	=       [1, 2, 4, 8, 16, 32, 64, 128]

   # skip get_neighbors_pixel if center is null
   if center is None:
      return 0

   # Starting from the top right, assigning value to pixels clockwise
   UpperCAmelCase_   :   List[Any]     	=       [
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate + 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						__lowerCamelCase,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate + 1,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						__lowerCamelCase,						y_coordinate - 1,						__lowerCamelCase    ),
       get_neighbors_pixel(__lowerCamelCase,						x_coordinate - 1,						y_coordinate - 1,						__lowerCamelCase    ),
   ]

   # Converting the binary value to decimal.
   return sum(
       binary_value * power for binary_value, power in zip(__lowerCamelCase,						__lowerCamelCase    )    )


if __name__ == "__main__":
    # Reading the image and converting it to grayscale.
    _a									=				cva.imread(
        'digital_image_processing/image_data/lena.jpg', cva.IMREAD_GRAYSCALE
    )

    # Create a numpy array as the same height and width of read image
    _a									=				np.zeros((image.shape[0], image.shape[1]))

    # Iterating through the image and calculating the
    # local binary pattern value for each pixel.
    for i in range(0, image.shape[0]):
        for j in range(0, image.shape[1]):
            _a									=				local_binary_value(image, i, j)

    cva.imshow('local binary pattern', lbp_image)
    cva.waitKey(0)
    cva.destroyAllWindows()


