
"""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


lowerCAmelCase__							=				logging.get_logger(__name__)


lowerCAmelCase__							=				{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}





class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''codegen'''
							UpperCAmelCase__						     =      {
							    '''max_position_embeddings''': '''n_positions''',
							    '''hidden_size''': '''n_embd''',
							    '''num_attention_heads''': '''n_head''',
							    '''num_hidden_layers''': '''n_layer''',
							}

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=5_04_00       ,       SCREAMING_SNAKE_CASE_=20_48       ,       SCREAMING_SNAKE_CASE_=20_48       ,       SCREAMING_SNAKE_CASE_=40_96       ,       SCREAMING_SNAKE_CASE_=28       ,       SCREAMING_SNAKE_CASE_=16       ,       SCREAMING_SNAKE_CASE_=64       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_="gelu_new"       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=0.0       ,       SCREAMING_SNAKE_CASE_=1e-5       ,       SCREAMING_SNAKE_CASE_=0.02       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=5_02_56       ,       SCREAMING_SNAKE_CASE_=5_02_56       ,       SCREAMING_SNAKE_CASE_=False       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = vocab_size
									SCREAMING_SNAKE_CASE_						 = n_ctx
									SCREAMING_SNAKE_CASE_						 = n_positions
									SCREAMING_SNAKE_CASE_						 = n_embd
									SCREAMING_SNAKE_CASE_						 = n_layer
									SCREAMING_SNAKE_CASE_						 = n_head
									SCREAMING_SNAKE_CASE_						 = n_inner
									SCREAMING_SNAKE_CASE_						 = rotary_dim
									SCREAMING_SNAKE_CASE_						 = activation_function
									SCREAMING_SNAKE_CASE_						 = resid_pdrop
									SCREAMING_SNAKE_CASE_						 = embd_pdrop
									SCREAMING_SNAKE_CASE_						 = attn_pdrop
									SCREAMING_SNAKE_CASE_						 = layer_norm_epsilon
									SCREAMING_SNAKE_CASE_						 = initializer_range
									SCREAMING_SNAKE_CASE_						 = use_cache

									SCREAMING_SNAKE_CASE_						 = bos_token_id
									SCREAMING_SNAKE_CASE_						 = eos_token_id

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







class  snake_case     (							__lowercase   ):

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

									"""simple docstring"""







									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?
											SCREAMING_SNAKE_CASE_						 = 0

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}    )
									if self.use_past:
											self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_       ,       direction='''inputs'''    )
											SCREAMING_SNAKE_CASE_						 = {0: '''batch''', 1: '''past_sequence + sequence'''}
									else:
											SCREAMING_SNAKE_CASE_						 = {0: '''batch''', 1: '''sequence'''}

									return common_inputs

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self._config.n_layer

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self._config.n_head

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = -1       ,       SCREAMING_SNAKE_CASE_ = -1       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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()
									SCREAMING_SNAKE_CASE_						 = 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

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

									SCREAMING_SNAKE_CASE_						 = common_inputs['''attention_mask''']
									if self.use_past:
											SCREAMING_SNAKE_CASE_						 = ordered_inputs['''attention_mask'''].dtype
											SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									return 13

"""simple docstring"""




import cva
import numpy as np





class  snake_case     :

							def __init__(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									if k in (0.04, 0.06):
											SCREAMING_SNAKE_CASE_						 = k
											SCREAMING_SNAKE_CASE_						 = window_size
									else:
											raise ValueError('''invalid k value'''    )

							def __str__(self    ):

									"""simple docstring"""







									return str(self.k    )







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = cva.imread(SCREAMING_SNAKE_CASE_       ,       0    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = img.shape
									SCREAMING_SNAKE_CASE_						 = []
									SCREAMING_SNAKE_CASE_						 = img.copy()
									SCREAMING_SNAKE_CASE_						 = cva.cvtColor(SCREAMING_SNAKE_CASE_       ,       cva.COLOR_GRAY2RGB    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = np.gradient(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = dx**2
									SCREAMING_SNAKE_CASE_						 = dy**2
									SCREAMING_SNAKE_CASE_						 = dx * dy
									SCREAMING_SNAKE_CASE_						 = 0.04
									SCREAMING_SNAKE_CASE_						 = self.window_size // 2
									for y in range(SCREAMING_SNAKE_CASE_       ,       h - offset    ):
											for x in range(SCREAMING_SNAKE_CASE_       ,       w - offset    ):
													SCREAMING_SNAKE_CASE_						 = ixx[
													    y - offset : y + offset + 1, x - offset : x + offset + 1
													].sum()
													SCREAMING_SNAKE_CASE_						 = iyy[
													    y - offset : y + offset + 1, x - offset : x + offset + 1
													].sum()
													SCREAMING_SNAKE_CASE_						 = ixy[
													    y - offset : y + offset + 1, x - offset : x + offset + 1
													].sum()

													SCREAMING_SNAKE_CASE_						 = (wxx * wyy) - (wxy**2)
													SCREAMING_SNAKE_CASE_						 = wxx + wyy
													SCREAMING_SNAKE_CASE_						 = 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)       ,       2_55    )
									return color_img, corner_list


if __name__ == "__main__":
						lowerCAmelCase__							=				HarrisCorner(0.04, 3)
						lowerCAmelCase__,  lowerCAmelCase__							=				edge_detect.detect('path_to_image')
						cva.imwrite('detect.png', color_img)