






from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
						import tensorflow as tf

						from ..tf_utils import stable_softmax


if is_torch_available():
						import torch


__UpperCamelCase      :      List[Any]     						=						logging.get_logger(__name__)


@add_end_docstrings(
    _lowerCAmelCase   ,			R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '   ,			)
class        __UpperCamelCase    (     _lowerCAmelCase   ):







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							GenericTensor			)		-> np.ndarray:
		"""simple docstring"""







		if self.framework == "tf":
			__lowercase             =  tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()
		elif self.framework == "pt":
			__lowercase             =  torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,				as_tuple=_lowerCAmelCase			)
		else:
			raise ValueError("""Unsupported framework"""			)
		return masked_index







	def  _a   (  self      :							List[str]       ,				_lowerCAmelCase      :							GenericTensor			)		-> np.ndarray:
		"""simple docstring"""







		__lowercase             =  self.get_masked_index(_lowerCAmelCase			)
		__lowercase             =  np.prod(masked_index.shape			)
		if numel < 1:
			raise PipelineException(
			    """fill-mask"""       ,				self.model.base_model_prefix       ,				F'No mask_token ({self.tokenizer.mask_token}) found on the input'       ,				)







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							GenericTensor			)		-> str:
		"""simple docstring"""







		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			for model_input in model_inputs:
				self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0]			)
		else:
			for input_ids in model_inputs["input_ids"]:
				self._ensure_exactly_one_mask_token(_lowerCAmelCase			)







	def  _a   (  self      :							Optional[int]       ,				_lowerCAmelCase      :							Union[str, Any]       ,				_lowerCAmelCase      :							Union[str, Any]=None       ,				**_lowerCAmelCase      :							List[str]			)		-> Dict[str, GenericTensor]:
		"""simple docstring"""







		if return_tensors is None:
			__lowercase             =  self.framework
		__lowercase             =  self.tokenizer(_lowerCAmelCase       ,				return_tensors=_lowerCAmelCase			)
		self.ensure_exactly_one_mask_token(_lowerCAmelCase			)
		return model_inputs







	def  _a   (  self      :							List[Any]       ,				_lowerCAmelCase      :							List[str]			)		-> Any:
		"""simple docstring"""







		__lowercase             =  self.model(**_lowerCAmelCase			)
		__lowercase             =  model_inputs["""input_ids"""]
		return model_outputs







	def  _a   (  self      :							int       ,				_lowerCAmelCase      :							Optional[Any]       ,				_lowerCAmelCase      :							str=5       ,				_lowerCAmelCase      :							str=None			)		-> Tuple:
		"""simple docstring"""







		if target_ids is not None and target_ids.shape[0] < top_k:
			__lowercase             =  target_ids.shape[0]
		__lowercase             =  model_outputs["""input_ids"""][0]
		__lowercase             =  model_outputs["""logits"""]

		if self.framework == "tf":
			__lowercase             =  tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()[:, 0]

			__lowercase             =  outputs.numpy()

			__lowercase             =  outputs[0, masked_index, :]
			__lowercase             =  stable_softmax(_lowerCAmelCase       ,				axis=-1			)
			if target_ids is not None:
				__lowercase             =  tf.gather_nd(tf.squeeze(_lowerCAmelCase       ,				0			)       ,				target_ids.reshape(-1       ,				1			)			)
				__lowercase             =  tf.expand_dims(_lowerCAmelCase       ,				0			)

			__lowercase             =  tf.math.top_k(_lowerCAmelCase       ,				k=_lowerCAmelCase			)
			__lowercase       ,						__lowercase             =  topk.values.numpy(), topk.indices.numpy()
		else:
			__lowercase             =  torch.nonzero(input_ids == self.tokenizer.mask_token_id       ,				as_tuple=_lowerCAmelCase			).squeeze(-1			)
			# Fill mask pipeline supports only one ${mask_token} per sample

			__lowercase             =  outputs[0, masked_index, :]
			__lowercase             =  logits.softmax(dim=-1			)
			if target_ids is not None:
				__lowercase             =  probs[..., target_ids]

			__lowercase       ,						__lowercase             =  probs.topk(_lowerCAmelCase			)

		__lowercase             =  []
		__lowercase             =  values.shape[0] == 1
		for i, (_values, _predictions) in enumerate(zip(values.tolist()       ,				predictions.tolist()			)			):
			__lowercase             =  []
			for v, p in zip(_values       ,				_predictions			):
				# Copy is important since we're going to modify this array in place
				__lowercase             =  input_ids.numpy().copy()
				if target_ids is not None:
					__lowercase             =  target_ids[p].tolist()

				__lowercase             =  p
				# Filter padding out:
				__lowercase             =  tokens[np.where(tokens != self.tokenizer.pad_token_id			)]
				# Originally we skip special tokens to give readable output.
				# For multi masks though, the other [MASK] would be removed otherwise
				# making the output look odd, so we add them back
				__lowercase             =  self.tokenizer.decode(_lowerCAmelCase       ,				skip_special_tokens=_lowerCAmelCase			)
				__lowercase             =  {"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]			), """sequence""": sequence}
				row.append(_lowerCAmelCase			)
			result.append(_lowerCAmelCase			)
		if single_mask:
			return result[0]
		return result







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							Tuple       ,				_lowerCAmelCase      :							int=None			)		-> Union[str, Any]:
		"""simple docstring"""







		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			):
			__lowercase             =  [targets]
		try:
			__lowercase             =  self.tokenizer.get_vocab()
		except Exception:
			__lowercase             =  {}
		__lowercase             =  []
		for target in targets:
			__lowercase             =  vocab.get(_lowerCAmelCase       ,				_lowerCAmelCase			)
			if id_ is None:
				__lowercase             =  self.tokenizer(
				    _lowerCAmelCase       ,				add_special_tokens=_lowerCAmelCase       ,				return_attention_mask=_lowerCAmelCase       ,				return_token_type_ids=_lowerCAmelCase       ,				max_length=1       ,				truncation=_lowerCAmelCase       ,				)["""input_ids"""]
				if len(_lowerCAmelCase			) == 0:
					logger.warning(
					    F'The specified target token `{target}` does not exist in the model vocabulary. '
					    """We cannot replace it with anything meaningful, ignoring it"""			)
					continue
				__lowercase             =  input_ids[0]
				# XXX: If users encounter this pass
				# it becomes pretty slow, so let's make sure
				# The warning enables them to fix the input to
				# get faster performance.
				logger.warning(
				    F'The specified target token `{target}` does not exist in the model vocabulary. '
				    F'Replacing with `{self.tokenizer.convert_ids_to_tokens(id_			)}`.'			)
			target_ids.append(id_			)
		__lowercase             =  list(set(_lowerCAmelCase			)			)
		if len(_lowerCAmelCase			) == 0:
			raise ValueError("""At least one target must be provided when passed."""			)
		__lowercase             =  np.array(_lowerCAmelCase			)
		return target_ids







	def  _a   (  self      :							Any       ,				_lowerCAmelCase      :							List[Any]=None       ,				_lowerCAmelCase      :							str=None			)		-> Tuple:
		"""simple docstring"""







		__lowercase             =  {}

		if targets is not None:
			__lowercase             =  self.get_target_ids(_lowerCAmelCase       ,				_lowerCAmelCase			)
			__lowercase             =  target_ids

		if top_k is not None:
			__lowercase             =  top_k

		if self.tokenizer.mask_token_id is None:
			raise PipelineException(
			    """fill-mask"""       ,				self.model.base_model_prefix       ,				"""The tokenizer does not define a `mask_token`."""			)
		return {}, {}, postprocess_params







	def __call__(  self      :							str       ,				_lowerCAmelCase      :							Optional[int]       ,				*_lowerCAmelCase      :							Optional[int]       ,				**_lowerCAmelCase      :							str			)		-> Any:
		"""simple docstring"""







		__lowercase             =  super().__call__(_lowerCAmelCase       ,				**_lowerCAmelCase			)
		if isinstance(_lowerCAmelCase       ,				_lowerCAmelCase			) and len(_lowerCAmelCase			) == 1:
			return outputs[0]
		return outputs









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





def 				snake_case  (     lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase				,  lowerCamelCase					):




	'''simple docstring'''







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

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

			# degree to radiant
			__lowercase             =  theta / 180 * np.pi
			__lowercase             =  np.cos(_theta					)
			__lowercase             =  np.sin(_theta					)

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

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

			# fill kernel
			__lowercase             =  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
						__UpperCamelCase      :      List[Any]     						=						imread("""../image_data/lena.jpg""")
						# turn image in gray scale value
						__UpperCamelCase      :      Union[str, Any]     						=						cvtColor(img, COLOR_BGR2GRAY)

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

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

						waitKey(0)

