


'''simple docstring'''



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

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


_lowercase      :      str            =					logging.get_logger(__name__)

_lowercase      :      Optional[int]            =					{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Tuple  		=	"bart"
				__magic_name__    :				Any  		=	["past_key_values"]
				__magic_name__    :				List[Any]  		=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




				def __init__(		self      :			str				,   lowerCAmelCase      :			Dict=50265				,   lowerCAmelCase      :			Any=1024				,   lowerCAmelCase      :			str=12				,   lowerCAmelCase      :			Union[str, Any]=4096				,   lowerCAmelCase      :			Optional[int]=16				,   lowerCAmelCase      :			str=12				,   lowerCAmelCase      :			Optional[int]=4096				,   lowerCAmelCase      :			int=16				,   lowerCAmelCase      :			Dict=0.0				,   lowerCAmelCase      :			str=0.0				,   lowerCAmelCase      :			str="gelu"				,   lowerCAmelCase      :			int=1024				,   lowerCAmelCase      :			int=0.1				,   lowerCAmelCase      :			Tuple=0.0				,   lowerCAmelCase      :			Optional[int]=0.0				,   lowerCAmelCase      :			List[Any]=0.02				,   lowerCAmelCase      :			Dict=0.0				,   lowerCAmelCase      :			List[Any]=False				,   lowerCAmelCase      :			Optional[int]=True				,   lowerCAmelCase      :			List[str]=3				,   lowerCAmelCase      :			List[Any]=1				,   lowerCAmelCase      :			Optional[Any]=0				,   lowerCAmelCase      :			List[Any]=2				,   lowerCAmelCase      :			Optional[int]=True				,   lowerCAmelCase      :			List[str]=2				,   lowerCAmelCase      :			Dict=2				,   **lowerCAmelCase      :			Any				,   )->					List[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     vocab_size
										UpperCAmelCase					    =     max_position_embeddings
										UpperCAmelCase					    =     d_model
										UpperCAmelCase					    =     encoder_ffn_dim
										UpperCAmelCase					    =     encoder_layers
										UpperCAmelCase					    =     encoder_attention_heads
										UpperCAmelCase					    =     decoder_ffn_dim
										UpperCAmelCase					    =     decoder_layers
										UpperCAmelCase					    =     decoder_attention_heads
										UpperCAmelCase					    =     dropout
										UpperCAmelCase					    =     attention_dropout
										UpperCAmelCase					    =     activation_dropout
										UpperCAmelCase					    =     activation_function
										UpperCAmelCase					    =     init_std
										UpperCAmelCase					    =     encoder_layerdrop
										UpperCAmelCase					    =     decoder_layerdrop
										UpperCAmelCase					    =     classifier_dropout
										UpperCAmelCase					    =     use_cache
										UpperCAmelCase					    =     encoder_layers
										UpperCAmelCase					    =     scale_embedding  # scale factor will be sqrt(d_model) if True

										super().__init__(
										    num_labels=lowerCAmelCase				,   pad_token_id=lowerCAmelCase				,   bos_token_id=lowerCAmelCase				,   eos_token_id=lowerCAmelCase				,   is_encoder_decoder=lowerCAmelCase				,   decoder_start_token_id=lowerCAmelCase				,   forced_eos_token_id=lowerCAmelCase				,   **lowerCAmelCase				,   )

										# ensure backward compatibility for BART CNN models
										if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				,   lowerCAmelCase				):
																UpperCAmelCase					    =     self.bos_token_id
																warnings.warn(
																    F"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
																    '''The config can simply be saved and uploaded again to be fixed.'''				)







class 	UpperCamelCase__(       lowerCAmelCase	):




				@property
				def 					a__(		self      :			Optional[int]				)->					Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase					    =     OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]				)

																if self.use_past:
																						UpperCAmelCase					    =     {0: '''batch'''}
																						UpperCAmelCase					    =     {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
																else:
																						UpperCAmelCase					    =     {0: '''batch''', 1: '''decoder_sequence'''}
																						UpperCAmelCase					    =     {0: '''batch''', 1: '''decoder_sequence'''}

																if self.use_past:
																						self.fill_with_past_key_values_(lowerCAmelCase				,   direction='''inputs'''				)
										elif self.task == "causal-lm":
																# TODO: figure this case out.
																UpperCAmelCase					    =     OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																    ]				)
																if self.use_past:
																						UpperCAmelCase    ,    UpperCAmelCase					    =     self.num_layers
																						for i in range(lowerCAmelCase				):
																												UpperCAmelCase					    =     {0: '''batch''', 2: '''past_sequence + sequence'''}
																												UpperCAmelCase					    =     {0: '''batch''', 2: '''past_sequence + sequence'''}
										else:
																UpperCAmelCase					    =     OrderedDict(
																    [
																        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
																        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
																        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
																    ]				)

										return common_inputs




				@property
				def 					a__(		self      :			List[Any]				)->					Mapping[str, Mapping[int, str]]:

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase					    =     super().outputs
										else:
																UpperCAmelCase					    =     super(lowerCAmelCase				,   self				).outputs
																if self.use_past:
																						UpperCAmelCase    ,    UpperCAmelCase					    =     self.num_layers
																						for i in range(lowerCAmelCase				):
																												UpperCAmelCase					    =     {0: '''batch''', 2: '''past_sequence + sequence'''}
																												UpperCAmelCase					    =     {0: '''batch''', 2: '''past_sequence + sequence'''}
										return common_outputs




				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			PreTrainedTokenizer				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			Optional[TensorType] = None				,   )->					Mapping[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)

										# Generate decoder inputs
										UpperCAmelCase					    =     seq_length if not self.use_past else 1
										UpperCAmelCase					    =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)
										UpperCAmelCase					    =     {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
										UpperCAmelCase					    =     dict(**lowerCAmelCase				,   **lowerCAmelCase				)

										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					    =     common_inputs['''input_ids'''].shape
																UpperCAmelCase					    =     common_inputs['''decoder_input_ids'''].shape[1]
																UpperCAmelCase    ,    UpperCAmelCase					    =     self.num_attention_heads
																UpperCAmelCase					    =     (
																    batch,
																    num_encoder_attention_heads,
																    encoder_seq_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)
																UpperCAmelCase					    =     decoder_seq_length + 3
																UpperCAmelCase					    =     (
																    batch,
																    num_decoder_attention_heads,
																    decoder_past_length,
																    self._config.hidden_size // num_decoder_attention_heads,
																)

																UpperCAmelCase					    =     torch.cat(
																    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCAmelCase				,   lowerCAmelCase				)]				,   dim=1				)

																UpperCAmelCase					    =     []
																# If the number of encoder and decoder layers are present in the model configuration, both are considered
																UpperCAmelCase    ,    UpperCAmelCase					    =     self.num_layers
																UpperCAmelCase					    =     min(lowerCAmelCase				,   lowerCAmelCase				)
																UpperCAmelCase					    =     max(lowerCAmelCase				,   lowerCAmelCase				) - min_num_layers
																UpperCAmelCase					    =     '''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

																for _ in range(lowerCAmelCase				):
																						common_inputs["past_key_values"].append(
																						    (
																						        torch.zeros(lowerCAmelCase				),
																						        torch.zeros(lowerCAmelCase				),
																						        torch.zeros(lowerCAmelCase				),
																						        torch.zeros(lowerCAmelCase				),
																						    )				)
																# TODO: test this.
																UpperCAmelCase					    =     encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
																for _ in range(lowerCAmelCase				,   lowerCAmelCase				):
																						common_inputs["past_key_values"].append((torch.zeros(lowerCAmelCase				), torch.zeros(lowerCAmelCase				))				)
										return common_inputs




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

										"""simple docstring"""




										UpperCAmelCase					    =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
										    lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)

										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					    =     common_inputs['''input_ids'''].shape
																# Not using the same length for past_key_values
																UpperCAmelCase					    =     seqlen + 2
																UpperCAmelCase    ,    UpperCAmelCase					    =     self.num_layers
																UpperCAmelCase    ,    UpperCAmelCase					    =     self.num_attention_heads
																UpperCAmelCase					    =     (
																    batch,
																    num_encoder_attention_heads,
																    past_key_values_length,
																    self._config.hidden_size // num_encoder_attention_heads,
																)

																UpperCAmelCase					    =     common_inputs['''attention_mask'''].dtype
																UpperCAmelCase					    =     torch.cat(
																    [common_inputs['''attention_mask'''], torch.ones(lowerCAmelCase				,   lowerCAmelCase				,   dtype=lowerCAmelCase				)]				,   dim=1				)
																UpperCAmelCase					    =     [
																    (torch.zeros(lowerCAmelCase				), torch.zeros(lowerCAmelCase				)) for _ in range(lowerCAmelCase				)
																]
										return common_inputs




				def 					a__(		self      :			Tuple				,   lowerCAmelCase      :			PreTrainedTokenizer				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			int = -1				,   lowerCAmelCase      :			bool = False				,   lowerCAmelCase      :			Optional[TensorType] = None				,   )->					Mapping[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     compute_effective_axis_dimension(
										    lowerCAmelCase				,   fixed_dimension=OnnxConfig.default_fixed_batch				,   num_token_to_add=0				)

										# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
										UpperCAmelCase					    =     tokenizer.num_special_tokens_to_add(lowerCAmelCase				)
										UpperCAmelCase					    =     compute_effective_axis_dimension(
										    lowerCAmelCase				,   fixed_dimension=OnnxConfig.default_fixed_sequence				,   num_token_to_add=lowerCAmelCase				)

										# Generate dummy inputs according to compute batch and sequence
										UpperCAmelCase					    =     [''' '''.join([tokenizer.unk_token]				) * seq_length] * batch_size
										UpperCAmelCase					    =     dict(tokenizer(lowerCAmelCase				,   return_tensors=lowerCAmelCase				)				)
										return common_inputs




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

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase					    =     self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																    lowerCAmelCase				,   batch_size=lowerCAmelCase				,   seq_length=lowerCAmelCase				,   is_pair=lowerCAmelCase				,   framework=lowerCAmelCase				)

										elif self.task == "causal-lm":
																UpperCAmelCase					    =     self._generate_dummy_inputs_for_causal_lm(
																    lowerCAmelCase				,   batch_size=lowerCAmelCase				,   seq_length=lowerCAmelCase				,   is_pair=lowerCAmelCase				,   framework=lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																    lowerCAmelCase				,   batch_size=lowerCAmelCase				,   seq_length=lowerCAmelCase				,   is_pair=lowerCAmelCase				,   framework=lowerCAmelCase				)

										return common_inputs





				def 					a__(		self      :			Any				,   lowerCAmelCase      :			List[Any]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			List[str]				,   lowerCAmelCase      :			str				)->					int:

										"""simple docstring"""




										if self.task in ["default", "seq2seq-lm"]:
																UpperCAmelCase					    =     super()._flatten_past_key_values_(lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)
										else:
																UpperCAmelCase					    =     super(lowerCAmelCase				,   self				)._flatten_past_key_values_(
																    lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				,   lowerCAmelCase				)









'''simple docstring'''



import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey


def 				lowerCamelCase__				(	A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int				,       A       :      int					):




						'''simple docstring'''






						if (ksize % 2) == 0:
												UpperCAmelCase					    =     ksize + 1
						UpperCAmelCase					    =     np.zeros((ksize, ksize)				,       dtype=np.floataa					)

						# each value
						for y in range(A					):
												for x in range(A					):
																		# distance from center
																		UpperCAmelCase					    =     x - ksize // 2
																		UpperCAmelCase					    =     y - ksize // 2

																		# degree to radiant
																		UpperCAmelCase					    =     theta / 1_80 * np.pi
																		UpperCAmelCase					    =     np.cos(_theta					)
																		UpperCAmelCase					    =     np.sin(_theta					)

																		# get kernel x
																		UpperCAmelCase					    =     cos_theta * px + sin_theta * py

																		# get kernel y
																		UpperCAmelCase					    =     -sin_theta * px + cos_theta * py

																		# fill kernel
																		UpperCAmelCase					    =     np.exp(
																		    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)					) * np.cos(2 * np.pi * _x / lambd + psi					)

						return gabor


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	# read original image
	_lowercase      :      Tuple            =					imread("""../image_data/lena.jpg""")
	# turn image in gray scale value
	_lowercase      :      int            =					cvtColor(img, COLOR_BGR2GRAY)

	# Apply multiple Kernel to detect edges
	_lowercase      :      List[str]            =					np.zeros(gray.shape[:2])
	for theta in [0, 30, 60, 90, 120, 150]:
		_lowercase      :      List[Any]            =					gabor_filter_kernel(10, 8, theta, 10, 0, 0)
		out += filteraD(gray, CV_8UC3, kernel_aa)
	_lowercase      :      Optional[int]            =					out / out.max() * 255
	_lowercase      :      Optional[int]            =					out.astype(np.uinta)

	imshow("""Original""", gray)
	imshow("""Gabor filter with 20x20 mask and 6 directions""", out)

	waitKey(0)





