


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

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


A   :					Optional[Any]	=      logging.get_logger(__name__)

A   :					Any	=      {
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}







class     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "gpt_neo"
       A__      		=    ["past_key_values"]
       A__      		=    {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}





       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    List[Any]=50257      ,   __lowerCamelCase							:    Any=2048      ,   __lowerCamelCase							:    int=2048      ,   __lowerCamelCase							:    Optional[Any]=24      ,   __lowerCamelCase							:    Optional[int]=[[["global", "local"], 12]]      ,   __lowerCamelCase							:    Tuple=16      ,   __lowerCamelCase							:    Optional[Any]=None      ,   __lowerCamelCase							:    List[Any]=256      ,   __lowerCamelCase							:    Tuple="gelu_new"      ,   __lowerCamelCase							:    int=0.0      ,   __lowerCamelCase							:    Union[str, Any]=0.0      ,   __lowerCamelCase							:    str=0.0      ,   __lowerCamelCase							:    Tuple=0.1      ,   __lowerCamelCase							:    List[str]=1E-5      ,   __lowerCamelCase							:    str=0.0_2      ,   __lowerCamelCase							:    int=True      ,   __lowerCamelCase							:    int=50256      ,   __lowerCamelCase							:    int=50256      ,   **__lowerCamelCase							:    Dict      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[int]					=						vocab_size
             lowerCamelCase__	:       int					=						max_position_embeddings
             lowerCamelCase__	:       Union[str, Any]					=						hidden_size
             lowerCamelCase__	:       Optional[Any]					=						num_layers
             lowerCamelCase__	:       Dict					=						num_heads
             lowerCamelCase__	:       Tuple					=						intermediate_size
             lowerCamelCase__	:       Dict					=						window_size
             lowerCamelCase__	:       List[str]					=						activation_function
             lowerCamelCase__	:       Tuple					=						resid_dropout
             lowerCamelCase__	:       Tuple					=						embed_dropout
             lowerCamelCase__	:       Optional[int]					=						attention_dropout
             lowerCamelCase__	:       Any					=						classifier_dropout
             lowerCamelCase__	:       str					=						layer_norm_epsilon
             lowerCamelCase__	:       Any					=						initializer_range
             lowerCamelCase__	:       List[str]					=						use_cache

             lowerCamelCase__	:       int					=						bos_token_id
             lowerCamelCase__	:       Optional[Any]					=						eos_token_id

             lowerCamelCase__	:       Tuple					=						attention_types
             lowerCamelCase__	:       Union[str, Any]					=						self.expand_attention_types_params(__lowerCamelCase )

             if len(self.attention_layers ) != self.num_layers:
                   raise ValueError(
                       "Configuration for convolutional module is incorrect. "
                       "It is required that `len(config.attention_layers)` == `config.num_layers` "
                       f"but is `len(config.attention_layers) = {len(self.attention_layers )}`, "
                       f"`config.num_layers = {self.num_layers}`. "
                       "`config.attention_layers` is prepared using `config.attention_types`. "
                       "Please verify the value of `config.attention_types` argument." )

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





       @staticmethod
       def 		lowerCAmelCase  (     __lowerCamelCase							:    Optional[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Tuple					=						[]
             for item in attention_types:
                   for _ in range(item[1] ):
                         attentions.extend(item[0] )
             return attentions



def    lowercase_	(	_A    :					str					,      _A    :					List[Any]					,      _A    :					Optional[Any]					,      _A    :					Tuple		):

      """simple docstring"""




      import torch

      lowerCamelCase__	:       Union[str, Any]					=						input.size()
      lowerCamelCase__	:       Tuple					=						len(_A		)
      lowerCamelCase__	:       Optional[Any]					=						shape[dimension]

      lowerCamelCase__	:       int					=						torch.arange(0					,      _A					,      _A		)
      lowerCamelCase__	:       Optional[Any]					=						torch.div(sizedim - size					,      _A					,      rounding_mode="floor"		) + 1
      lowerCamelCase__	:       Union[str, Any]					=						torch.arange(_A		) + low_indices[:min_length][:, None]

      lowerCamelCase__	:       Tuple					=						[slice(_A		)] * rank
      lowerCamelCase__	:       Tuple					=						indices
      lowerCamelCase__	:       Optional[int]					=						input[s]

      lowerCamelCase__	:       int					=						list(range(0					,      rank + 1		)		)
      perm.append(perm.pop(dimension + 1		)		)

      return sliced.permute(_A		)

def    lowercase_	(	_A    :					int					,      _A    :					Optional[Any]		):

      """simple docstring"""




      import torch

      lowerCamelCase__	:       Optional[int]					=						torch.arange(1					,      _A		)
      lowerCamelCase__	:       Any					=						torch.remainder(_A					,      _A		)
      lowerCamelCase__	:       int					=						remainders == 0
      lowerCamelCase__	:       Tuple					=						candidates[divisor_indices]
      lowerCamelCase__	:       Optional[Any]					=						torch.max(_A		)
      return largest_divisor, torch.div(_A					,      _A					,      rounding_mode="floor"		)







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    List[Any] ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
             if self.use_past:
                   self.fill_with_past_key_values_(__lowerCamelCase      ,   direction="inputs" )
                   lowerCamelCase__	:       Tuple					=						{0: "batch", 1: "past_sequence + sequence"}
             else:
                   lowerCamelCase__	:       Tuple					=						{0: "batch", 1: "sequence"}

             return common_inputs





       @property
       def 		lowerCAmelCase  (     self							:    Optional[Any] ):


             '''simple docstring'''



             return self._config.num_heads





       def 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    PreTrainedTokenizer      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    int = -1      ,   __lowerCamelCase							:    bool = False      ,   __lowerCamelCase							:    Optional[TensorType] = None      ,   ):


             '''simple docstring'''



             lowerCamelCase__	:       Optional[Any]					=						super(__lowerCamelCase      ,   self ).generate_dummy_inputs(
                 __lowerCamelCase      ,   batch_size=__lowerCamelCase      ,   seq_length=__lowerCamelCase      ,   is_pair=__lowerCamelCase      ,   framework=__lowerCamelCase )

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

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

             lowerCamelCase__	:       int					=						common_inputs["attention_mask"]
             if self.use_past:
                   lowerCamelCase__	:       Optional[int]					=						ordered_inputs["attention_mask"].dtype
                   lowerCamelCase__	:       List[Any]					=						torch.cat(
                       [ordered_inputs["attention_mask"], torch.ones(__lowerCamelCase      ,   __lowerCamelCase      ,   dtype=__lowerCamelCase )]      ,   dim=1 )

             return ordered_inputs





       @property
       def 		lowerCAmelCase  (     self							:    Tuple ):


             '''simple docstring'''



             return 13






import cva
import numpy as np







class     _lowercase				:


       """simple docstring"""




       def __init__(     self							:    Union[str, Any]      ,   __lowerCamelCase							:    float      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             if k in (0.0_4, 0.0_6):
                   lowerCamelCase__	:       int					=						k
                   lowerCamelCase__	:       List[str]					=						window_size
             else:
                   raise ValueError("invalid k value" )





       def __str__(     self							:    str ):


             '''simple docstring'''



             return str(self.k )





       def 		lowerCAmelCase  (     self							:    Tuple      ,   __lowerCamelCase							:    str ):


             '''simple docstring'''



             lowerCamelCase__	:       Union[str, Any]					=						cva.imread(__lowerCamelCase      ,   0 )
             lowerCamelCase__					,     lowerCamelCase__	:       Any					=						img.shape
             lowerCamelCase__	:       list[list[int]]					=						[]
             lowerCamelCase__	:       List[Any]					=						img.copy()
             lowerCamelCase__	:       int					=						cva.cvtColor(__lowerCamelCase      ,   cva.COLOR_GRAY2RGB )
             lowerCamelCase__					,     lowerCamelCase__	:       int					=						np.gradient(__lowerCamelCase )
             lowerCamelCase__	:       Dict					=						dx**2
             lowerCamelCase__	:       Optional[Any]					=						dy**2
             lowerCamelCase__	:       int					=						dx * dy
             lowerCamelCase__	:       Union[str, Any]					=						0.0_4
             lowerCamelCase__	:       Any					=						self.window_size // 2
             for y in range(__lowerCamelCase      ,   h - offset ):
                   for x in range(__lowerCamelCase      ,   w - offset ):
                         lowerCamelCase__	:       Optional[Any]					=						ixx[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       Optional[Any]					=						iyy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()
                         lowerCamelCase__	:       str					=						ixy[
                             y - offset : y + offset + 1, x - offset : x + offset + 1
                         ].sum()

                         lowerCamelCase__	:       Optional[Any]					=						(wxx * wyy) - (wxy**2)
                         lowerCamelCase__	:       List[str]					=						wxx + wyy
                         lowerCamelCase__	:       List[Any]					=						det - k * (trace**2)
                         # Can change the value
                         if r > 0.5:
                               corner_list.append([x, y, r] )
                               color_img.itemset((y, x, 0)      ,   0 )
                               color_img.itemset((y, x, 1)      ,   0 )
                               color_img.itemset((y, x, 2)      ,   255 )
             return color_img, corner_list


if __name__ == "__main__":
    A   :					Tuple	=      HarrisCorner(0.0_4, 3)
    A,						A   :					Optional[int]	=      edge_detect.detect("path_to_image")
    cva.imwrite("detect.png", color_img)


