




"""simple docstring"""

from cva import destroyAllWindows, imread, imshow, waitKey

def 		lowercase_      (		_UpperCAmelCase	):




	"""simple docstring"""






	A_   , A_  :				Optional[Any]	   =		img.shape[0], img.shape[1]

	# converting each pixel's color to its negative
	for i in range(_UpperCAmelCase	):
		for j in range(_UpperCAmelCase	):
			A_  :				Optional[int]	   =		[255, 255, 255] - img[i][j]

	return img


if __name__ == "__main__":
		# read original image
		_lowerCamelCase  :			int        =       imread('image_data/lena.jpg', 1)

		# convert to its negative
		_lowerCamelCase  :			Union[str, Any]        =       convert_to_negative(img)

		# show result image
		imshow('negative of original image', img)
		waitKey(0)
		destroyAllWindows()











"""simple docstring"""

from typing import Mapping

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


_lowerCamelCase  :			List[str]        =       logging.get_logger(__name__)

_lowerCamelCase  :			str        =       {
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class        lowercase      (    __UpperCAmelCase):
				__lowerCAmelCase    :    int         =   """umt5"""
				__lowerCAmelCase    :    List[str]         =   ["""past_key_values"""]







				def __init__(							self   :					Any      ,     _lowerCamelCase   :					Union[str, Any]=25_01_12      ,     _lowerCamelCase   :					Any=5_12      ,     _lowerCamelCase   :					Optional[int]=64      ,     _lowerCamelCase   :					str=10_24      ,     _lowerCamelCase   :					List[str]=8      ,     _lowerCamelCase   :					Union[str, Any]=None      ,     _lowerCamelCase   :					Any=6      ,     _lowerCamelCase   :					List[Any]=32      ,     _lowerCamelCase   :					Optional[Any]=1_28      ,     _lowerCamelCase   :					int=0.1      ,     _lowerCamelCase   :					Union[str, Any]=1E-6      ,     _lowerCamelCase   :					Tuple=1.0      ,     _lowerCamelCase   :					Optional[int]="gated-gelu"      ,     _lowerCamelCase   :					List[str]=True      ,     _lowerCamelCase   :					str=True      ,     _lowerCamelCase   :					Tuple="T5Tokenizer"      ,     _lowerCamelCase   :					Optional[int]=True      ,     _lowerCamelCase   :					Optional[int]=0      ,     _lowerCamelCase   :					Tuple=1      ,     _lowerCamelCase   :					Tuple=0      ,     **_lowerCamelCase   :					List[str]      ,     ):



					"""simple docstring"""

					super().__init__(
					    is_encoder_decoder=_lowerCamelCase      ,     tokenizer_class=_lowerCamelCase      ,     tie_word_embeddings=_lowerCamelCase      ,     pad_token_id=_lowerCamelCase      ,     eos_token_id=_lowerCamelCase      ,     decoder_start_token_id=_lowerCamelCase      ,     **_lowerCamelCase      ,     )
					A_  :				str	   =		vocab_size
					A_  :				List[Any]	   =		d_model
					A_  :				Optional[int]	   =		d_kv
					A_  :				int	   =		d_ff
					A_  :				Union[str, Any]	   =		num_layers
					A_  :				Optional[Any]	   =		(
					    num_decoder_layers if num_decoder_layers is not None else self.num_layers
					)  # default = symmetry
					A_  :				Dict	   =		num_heads
					A_  :				Optional[int]	   =		relative_attention_num_buckets
					A_  :				Union[str, Any]	   =		relative_attention_max_distance
					A_  :				Any	   =		dropout_rate
					A_  :				Optional[int]	   =		layer_norm_epsilon
					A_  :				Tuple	   =		initializer_factor
					A_  :				Optional[int]	   =		feed_forward_proj
					A_  :				Dict	   =		use_cache

					A_  :				Any	   =		self.feed_forward_proj.split('''-'''       )
					A_  :				Tuple	   =		act_info[-1]
					A_  :				Any	   =		act_info[0] == '''gated'''

					if len(_lowerCamelCase       ) > 1 and act_info[0] != "gated" or len(_lowerCamelCase       ) > 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\''''       )

					if feed_forward_proj == "gated-gelu":
						A_  :				Dict	   =		'''gelu_new'''







				@property
				def 					a_  (							self   :					Tuple       ):



					"""simple docstring"""

					return self.d_model







				@property
				def 					a_  (							self   :					Optional[int]       ):



					"""simple docstring"""

					return self.num_heads







				@property
				def 					a_  (							self   :					int       ):



					"""simple docstring"""

					return self.num_layers

class        lowercase      (    __UpperCAmelCase):







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					A_  :				str	   =		{
					    '''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
					    '''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
					}
					if self.use_past:
						A_  :				Optional[Any]	   =		'''past_encoder_sequence + sequence'''
						A_  :				Optional[Any]	   =		{0: '''batch'''}
						A_  :				List[Any]	   =		{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
					else:
						A_  :				Optional[int]	   =		{0: '''batch''', 1: '''decoder_sequence'''}
						A_  :				List[str]	   =		{0: '''batch''', 1: '''decoder_sequence'''}

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

					return common_inputs







				@property
				# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
				def 					a_  (							self   :					Union[str, Any]       ):



					"""simple docstring"""

					return 13







				@property
				def 					a_  (							self   :					Any       ):



					"""simple docstring"""

					return 5E-4





