


'''simple docstring'''



from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_tf_available():
	from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

_lowercase      :      Union[str, Any]            =					logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase	)
class 	UpperCamelCase__(       lowerCAmelCase	):




				def __init__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			Any				,   **lowerCAmelCase      :			List[str]				)->					Union[str, Any]:

										"""simple docstring"""




										super().__init__(*lowerCAmelCase				,   **lowerCAmelCase				)
										requires_backends(self				,   '''vision'''				)
										self.check_model_type(
										    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING				)




				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			int=None				,   lowerCAmelCase      :			Union[str, Any]=None				,   lowerCAmelCase      :			Dict=None				)->					Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     {}
										UpperCAmelCase					    =     {}

										if prompt is not None:
																UpperCAmelCase					    =     prompt

										if generate_kwargs is not None:
																UpperCAmelCase					    =     generate_kwargs
										if max_new_tokens is not None:
																if "generate_kwargs" not in forward_kwargs:
																						UpperCAmelCase					    =     {}
																if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
																						raise ValueError(
																						    '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
																						    ''' please use only one'''				)
																UpperCAmelCase					    =     max_new_tokens
										return preprocess_params, forward_kwargs, {}




				def __call__(		self      :			int				,   lowerCAmelCase      :			Union[str, List[str], "Image.Image", List["Image.Image"]]				,   **lowerCAmelCase      :			Dict				)->					List[str]:

										"""simple docstring"""




										return super().__call__(lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Any				,   lowerCAmelCase      :			Tuple				,   lowerCAmelCase      :			List[str]=None				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     load_image(lowerCAmelCase				)

										if prompt is not None:
																if not isinstance(lowerCAmelCase				,   lowerCAmelCase				):
																						raise ValueError(
																						    F"""Received an invalid text input, got - {type(lowerCAmelCase				)} - but expected a single string. """
																						    '''Note also that one single text can be provided for conditional image to text generation.'''				)

																UpperCAmelCase					    =     self.model.config.model_type

																if model_type == "git":
																						UpperCAmelCase					    =     self.image_processor(images=lowerCAmelCase				,   return_tensors=self.framework				)
																						UpperCAmelCase					    =     self.tokenizer(text=lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				).input_ids
																						UpperCAmelCase					    =     [self.tokenizer.cls_token_id] + input_ids
																						UpperCAmelCase					    =     torch.tensor(lowerCAmelCase				).unsqueeze(0				)
																						model_inputs.update({'''input_ids''': input_ids}				)

																elif model_type == "pix2struct":
																						UpperCAmelCase					    =     self.image_processor(images=lowerCAmelCase				,   header_text=lowerCAmelCase				,   return_tensors=self.framework				)

																elif model_type != "vision-encoder-decoder":
																						# vision-encoder-decoder does not support conditional generation
																						UpperCAmelCase					    =     self.image_processor(images=lowerCAmelCase				,   return_tensors=self.framework				)
																						UpperCAmelCase					    =     self.tokenizer(lowerCAmelCase				,   return_tensors=self.framework				)
																						model_inputs.update(lowerCAmelCase				)

																else:
																						raise ValueError(F"""Model type {model_type} does not support conditional text generation"""				)

										else:
																UpperCAmelCase					    =     self.image_processor(images=lowerCAmelCase				,   return_tensors=self.framework				)

										if self.model.config.model_type == "git" and prompt is None:
																UpperCAmelCase					    =     None

										return model_inputs




				def 					a__(		self      :			int				,   lowerCAmelCase      :			Any				,   lowerCAmelCase      :			str=None				)->					str:

										"""simple docstring"""




										if (
										    "input_ids" in model_inputs
										    and isinstance(model_inputs['''input_ids''']				,   lowerCAmelCase				)
										    and all(x is None for x in model_inputs['''input_ids''']				)
										):
																UpperCAmelCase					    =     None

										if generate_kwargs is None:
																UpperCAmelCase					    =     {}
										# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
										#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
										#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
										#  in the `_prepare_model_inputs` method.
										UpperCAmelCase					    =     model_inputs.pop(self.model.main_input_name				)
										UpperCAmelCase					    =     self.model.generate(lowerCAmelCase				,   **lowerCAmelCase				,   **lowerCAmelCase				)
										return model_outputs





				def 					a__(		self      :			Optional[Any]				,   lowerCAmelCase      :			Any				)->					Tuple:

										"""simple docstring"""




										UpperCAmelCase					    =     []
										for output_ids in model_outputs:
																UpperCAmelCase					    =     {
																    '''generated_text''': self.tokenizer.decode(
																        lowerCAmelCase				,   skip_special_tokens=lowerCAmelCase				,   )
																}
																records.append(lowerCAmelCase				)
										return records









'''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)





