

from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


_lowerCamelCase								=logging.get_logger(__name__)



class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      __UpperCAmelCase      		=				'mt5'
      __UpperCAmelCase      		=				['past_key_values']
      def __init__(				self					:							List[str]      ,snake_case					:							Optional[Any]=250112      ,snake_case					:							Union[str, Any]=512      ,snake_case					:							List[Any]=64      ,snake_case					:							str=1024      ,snake_case					:							List[Any]=8      ,snake_case					:							Union[str, Any]=None      ,snake_case					:							Optional[Any]=6      ,snake_case					:							Any=32      ,snake_case					:							List[Any]=128      ,snake_case					:							Optional[int]=0.1      ,snake_case					:							Tuple=1e-6      ,snake_case					:							int=1.0      ,snake_case					:							Optional[Any]="gated-gelu"      ,snake_case					:							Dict=True      ,snake_case					:							Union[str, Any]=True      ,snake_case					:							int="T5Tokenizer"      ,snake_case					:							List[str]=False      ,snake_case					:							int=0      ,snake_case					:							List[Any]=1      ,snake_case					:							List[Any]=0      ,**snake_case					:							int      ,):
           super().__init__(
               is_encoder_decoder=snake_case      ,tokenizer_class=snake_case      ,tie_word_embeddings=snake_case      ,pad_token_id=snake_case      ,eos_token_id=snake_case      ,decoder_start_token_id=snake_case      ,**snake_case      ,)
           SCREAMING_SNAKE_CASE										=vocab_size
           SCREAMING_SNAKE_CASE										=d_model
           SCREAMING_SNAKE_CASE										=d_kv
           SCREAMING_SNAKE_CASE										=d_ff
           SCREAMING_SNAKE_CASE										=num_layers
           SCREAMING_SNAKE_CASE										=(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           SCREAMING_SNAKE_CASE										=num_heads
           SCREAMING_SNAKE_CASE										=relative_attention_num_buckets
           SCREAMING_SNAKE_CASE										=relative_attention_max_distance
           SCREAMING_SNAKE_CASE										=dropout_rate
           SCREAMING_SNAKE_CASE										=layer_norm_epsilon
           SCREAMING_SNAKE_CASE										=initializer_factor
           SCREAMING_SNAKE_CASE										=feed_forward_proj
           SCREAMING_SNAKE_CASE										=use_cache

           SCREAMING_SNAKE_CASE										=self.feed_forward_proj.split('-'    )
           SCREAMING_SNAKE_CASE										=act_info[-1]
           SCREAMING_SNAKE_CASE										=act_info[0] == 'gated'

           if len(snake_case    ) > 1 and act_info[0] != "gated" or len(snake_case    ) > 2:
                raise ValueError(
                    f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                    'Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '
                    '\'gated-gelu\' or \'relu\''    )

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
                SCREAMING_SNAKE_CASE										='gelu_new'
      @property
      def 	_lowerCAmelCase					(				self					:							Tuple    ):
           return self.d_model
      @property
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           return self.num_heads


      @property
      def 	_lowerCAmelCase					(				self					:							Union[str, Any]    ):
           return self.num_layers







class 		a_	(							lowerCamelCase_					):



      """simple docstring"""







      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
      def 	_lowerCAmelCase					(				self					:							int    ):
           SCREAMING_SNAKE_CASE										={
               'input_ids': {0: 'batch', 1: 'encoder_sequence'},
               'attention_mask': {0: 'batch', 1: 'encoder_sequence'},
           }
           if self.use_past:
                SCREAMING_SNAKE_CASE										='past_encoder_sequence + sequence'
                SCREAMING_SNAKE_CASE										={0: 'batch'}
                SCREAMING_SNAKE_CASE										={0: 'batch', 1: 'past_decoder_sequence + sequence'}
           else:
                SCREAMING_SNAKE_CASE										={0: 'batch', 1: 'decoder_sequence'}
                SCREAMING_SNAKE_CASE										={0: 'batch', 1: 'decoder_sequence'}

           if self.use_past:
                self.fill_with_past_key_values_(snake_case      ,direction='inputs'    )

           return common_inputs
      @property
      # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
      def 	_lowerCAmelCase					(				self					:							List[str]    ):
           return 13


      @property
      def 	_lowerCAmelCase					(				self					:							Optional[Any]    ):
           return 5e-4




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey

from digital_image_processing.filters.convolve import img_convolve







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]      )
     SCREAMING_SNAKE_CASE										=np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]      )

     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     SCREAMING_SNAKE_CASE										=np.abs(img_convolve(lowerCAmelCase_,   lowerCAmelCase_      )      )
     # modify the pix within [0, 255]
     SCREAMING_SNAKE_CASE										=dst_x * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_y * 255 / np.max(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=np.sqrt((np.square(lowerCAmelCase_      )) + (np.square(lowerCAmelCase_      ))      )
     SCREAMING_SNAKE_CASE										=dst_xy * 255 / np.max(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=dst_xy.astype(np.uinta      )

     SCREAMING_SNAKE_CASE										=np.arctana(lowerCAmelCase_,   lowerCAmelCase_      )
     return dst, theta


if __name__ == "__main__":
 # read original image
 _lowerCamelCase								=imread("../image_data/lena.jpg")
 # turn image in gray scale value
 _lowerCamelCase								=cvtColor(img, COLOR_BGR2GRAY)

 _lowerCamelCase	,			_lowerCamelCase								=sobel_filter(gray)

 # show result images
 imshow("sobel filter", sobel_grad)
 imshow("sobel theta", sobel_theta)
 waitKey(0)

