
'''simple docstring'''


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

from digital_image_processing.filters.convolve import img_convolve
def 					__magic_name__(       UpperCAmelCase__ :       Union[str, Any]	):
						UpperCamelCase_     :	Tuple					     =     np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]	)
						UpperCamelCase_     :	List[Any]					     =     np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]]	)

						UpperCamelCase_     :	Any					     =     np.abs(img_convolve(UpperCAmelCase__							,				UpperCAmelCase__	)	)
						UpperCamelCase_     :	Tuple					     =     np.abs(img_convolve(UpperCAmelCase__							,				UpperCAmelCase__	)	)
						# modify the pix within [0, 255]
						UpperCamelCase_     :	List[Any]					     =     dst_x * 255 / np.max(UpperCAmelCase__	)
						UpperCamelCase_     :	str					     =     dst_y * 255 / np.max(UpperCAmelCase__	)

						UpperCamelCase_     :	Dict					     =     np.sqrt((np.square(UpperCAmelCase__	)) + (np.square(UpperCAmelCase__	))	)
						UpperCamelCase_     :	List[Any]					     =     dst_xy * 255 / np.max(UpperCAmelCase__	)
						UpperCamelCase_     :	List[str]					     =     dst_xy.astype(np.uinta	)

						UpperCamelCase_     :	Optional[int]					     =     np.arctana(UpperCAmelCase__							,				UpperCAmelCase__	)
						return dst, theta


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

	__SCREAMING_SNAKE_CASE			,      __SCREAMING_SNAKE_CASE						=      sobel_filter(gray)

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






'''simple docstring'''


import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import (
	    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)

if is_tf_available() and is_tensorflow_probability_available():
	import tensorflow as tf
	import tensorflow_probability as tfp

	from ..models.auto.modeling_tf_auto import (
	    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
	    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
	)




class     __snake_case			(    _a	):




		def __call__(  self					:Dict			,    _SCREAMING_SNAKE_CASE					:List[Any]=None			,    _SCREAMING_SNAKE_CASE					:Tuple=None			,    **_SCREAMING_SNAKE_CASE					:int)					->			str:






								'''simple docstring'''






								requires_backends(self			,    """pandas""")
								import pandas as pd

								if table is None:
														raise ValueError("""Keyword argument `table` cannot be None.""")
								elif query is None:
														if isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) and table.get("""query""") is not None and table.get("""table""") is not None:
																				UpperCamelCase_     :	Union[str, Any]					     =     [table]
														elif isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) and len(_SCREAMING_SNAKE_CASE) > 0:
																				if not all(isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) for d in table):
																										raise ValueError(
																										    F"""Keyword argument `table` should be a list of dict, but is {(type(_SCREAMING_SNAKE_CASE) for d in table)}""")

																				if table[0].get("""query""") is not None and table[0].get("""table""") is not None:
																										UpperCamelCase_     :	Any					     =     table
																				else:
																										raise ValueError(
																										    """If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"""
																										    F""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.""")
														elif Dataset is not None and isinstance(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE) or isinstance(_SCREAMING_SNAKE_CASE			,    types.GeneratorType):
																				return table
														else:
																				raise ValueError(
																				    """Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but """
																				    F"""is {type(_SCREAMING_SNAKE_CASE)})""")
								else:
														UpperCamelCase_     :	int					     =     [{"""table""": table, """query""": query}]

								for tqa_pipeline_input in tqa_pipeline_inputs:
														if not isinstance(tqa_pipeline_input["""table"""]			,    pd.DataFrame):
																				if tqa_pipeline_input["table"] is None:
																										raise ValueError("""Table cannot be None.""")

																				UpperCamelCase_     :	Any					     =     pd.DataFrame(tqa_pipeline_input["""table"""])

								return tqa_pipeline_inputs







@add_end_docstrings(_a	)
class     __snake_case			(    _a	):
		a_          =					"table,query"




		def __init__(  self					:List[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=TableQuestionAnsweringArgumentHandler()			,    *_SCREAMING_SNAKE_CASE					:Any			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[Any]:






								'''simple docstring'''






								super().__init__(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Optional[Any]					     =     args_parser

								self.check_model_type(
								    dict(
								        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
								        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items())
								    if self.framework == """tf"""
								    else dict(
								        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()))

								UpperCamelCase_     :	List[str]					     =     bool(getattr(self.model.config			,    """aggregation_labels"""			,    _SCREAMING_SNAKE_CASE)) and bool(
								    getattr(self.model.config			,    """num_aggregation_labels"""			,    _SCREAMING_SNAKE_CASE))
								UpperCamelCase_     :	List[Any]					     =     """tapas""" if hasattr(self.model.config			,    """aggregation_labels""") else None




		def 	_snake_case	(  self					:List[Any]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								return self.model(**_SCREAMING_SNAKE_CASE)




		def 	_snake_case	(  self					:Optional[int]			,    **_SCREAMING_SNAKE_CASE					:Tuple)					->			Tuple:






								'''simple docstring'''






								if self.framework == "pt":
														UpperCamelCase_     :	Optional[int]					     =     []
														UpperCamelCase_     :	Union[str, Any]					     =     []
														UpperCamelCase_     :	int					     =     None
														UpperCamelCase_     :	Dict					     =     inputs["""input_ids"""].shape[0]

														UpperCamelCase_     :	List[str]					     =     inputs["""input_ids"""].to(self.device)
														UpperCamelCase_     :	int					     =     inputs["""attention_mask"""].to(self.device)
														UpperCamelCase_     :	List[Any]					     =     inputs["""token_type_ids"""].to(self.device)
														UpperCamelCase_     :	List[str]					     =     None

														for index in range(_SCREAMING_SNAKE_CASE):
																				# If sequences have already been processed, the token type IDs will be created according to the previous
																				# answer.
																				if prev_answers is not None:
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 3]  # shape (seq_len,)
																										UpperCamelCase_     :	int					     =     np.zeros_like(prev_labels_example.cpu().numpy())  # shape (seq_len,)

																										UpperCamelCase_     :	int					     =     token_type_ids[index]  # shape (seq_len, 7)
																										for i in range(model_labels.shape[0]):
																																UpperCamelCase_     :	Optional[Any]					     =     token_type_ids_example[:, 0].tolist()[i]
																																UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																																UpperCamelCase_     :	Union[str, Any]					     =     token_type_ids_example[:, 2].tolist()[i] - 1

																																if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																						UpperCamelCase_     :	Tuple					     =     int(prev_answers[(col_id, row_id)])

																										UpperCamelCase_     :	Union[str, Any]					     =     torch.from_numpy(_SCREAMING_SNAKE_CASE).type(torch.long).to(self.device)

																				UpperCamelCase_     :	Optional[int]					     =     input_ids[index]
																				UpperCamelCase_     :	int					     =     attention_mask[index]  # shape (seq_len,)
																				UpperCamelCase_     :	Any					     =     token_type_ids[index]  # shape (seq_len, 7)
																				UpperCamelCase_     :	Union[str, Any]					     =     self.model(
																				    input_ids=input_ids_example.unsqueeze(0)			,    attention_mask=attention_mask_example.unsqueeze(0)			,    token_type_ids=token_type_ids_example.unsqueeze(0)			,    )
																				UpperCamelCase_     :	int					     =     outputs.logits

																				if self.aggregate:
																										all_aggregations.append(outputs.logits_aggregation)

																				all_logits.append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	Any					     =     torch.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	List[Any]					     =     dist_per_token.probs * attention_mask_example.type(torch.floataa).to(
																				    dist_per_token.probs.device)

																				UpperCamelCase_     :	Tuple					     =     collections.defaultdict(_SCREAMING_SNAKE_CASE)
																				for i, p in enumerate(probabilities.squeeze().tolist()):
																										UpperCamelCase_     :	str					     =     token_type_ids_example[:, 0].tolist()[i]
																										UpperCamelCase_     :	Optional[Any]					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																										UpperCamelCase_     :	Tuple					     =     token_type_ids_example[:, 2].tolist()[i] - 1
																										if col >= 0 and row >= 0 and segment_id == 1:
																																coords_to_probs[(col, row)].append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	str					     =     {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}

														UpperCamelCase_     :	str					     =     torch.cat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

														return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(_SCREAMING_SNAKE_CASE)			,    0))
								else:
														UpperCamelCase_     :	Any					     =     []
														UpperCamelCase_     :	Dict					     =     []
														UpperCamelCase_     :	Union[str, Any]					     =     None
														UpperCamelCase_     :	List[Any]					     =     inputs["""input_ids"""].shape[0]

														UpperCamelCase_     :	List[str]					     =     inputs["""input_ids"""]
														UpperCamelCase_     :	int					     =     inputs["""attention_mask"""]
														UpperCamelCase_     :	Optional[Any]					     =     inputs["""token_type_ids"""].numpy()
														UpperCamelCase_     :	Dict					     =     None

														for index in range(_SCREAMING_SNAKE_CASE):
																				# If sequences have already been processed, the token type IDs will be created according to the previous
																				# answer.
																				if prev_answers is not None:
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 3]  # shape (seq_len,)
																										UpperCamelCase_     :	Optional[Any]					     =     np.zeros_like(_SCREAMING_SNAKE_CASE			,    dtype=np.intaa)  # shape (seq_len,)

																										UpperCamelCase_     :	Union[str, Any]					     =     token_type_ids[index]  # shape (seq_len, 7)
																										for i in range(model_labels.shape[0]):
																																UpperCamelCase_     :	Any					     =     token_type_ids_example[:, 0].tolist()[i]
																																UpperCamelCase_     :	str					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																																UpperCamelCase_     :	Union[str, Any]					     =     token_type_ids_example[:, 2].tolist()[i] - 1

																																if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																						UpperCamelCase_     :	int					     =     int(prev_answers[(col_id, row_id)])

																										UpperCamelCase_     :	Optional[int]					     =     model_labels

																				UpperCamelCase_     :	Any					     =     input_ids[index]
																				UpperCamelCase_     :	Optional[int]					     =     attention_mask[index]  # shape (seq_len,)
																				UpperCamelCase_     :	List[Any]					     =     token_type_ids[index]  # shape (seq_len, 7)
																				UpperCamelCase_     :	Union[str, Any]					     =     self.model(
																				    input_ids=np.expand_dims(_SCREAMING_SNAKE_CASE			,    axis=0)			,    attention_mask=np.expand_dims(_SCREAMING_SNAKE_CASE			,    axis=0)			,    token_type_ids=np.expand_dims(_SCREAMING_SNAKE_CASE			,    axis=0)			,    )
																				UpperCamelCase_     :	List[Any]					     =     outputs.logits

																				if self.aggregate:
																										all_aggregations.append(outputs.logits_aggregation)

																				all_logits.append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	List[Any]					     =     tfp.distributions.Bernoulli(logits=_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	str					     =     dist_per_token.probs_parameter() * tf.cast(_SCREAMING_SNAKE_CASE			,    tf.floataa)

																				UpperCamelCase_     :	str					     =     collections.defaultdict(_SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Any					     =     token_type_ids_example
																				for i, p in enumerate(tf.squeeze(_SCREAMING_SNAKE_CASE).numpy().tolist()):
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 0].tolist()[i]
																										UpperCamelCase_     :	List[str]					     =     token_type_ids_example[:, 1].tolist()[i] - 1
																										UpperCamelCase_     :	Optional[int]					     =     token_type_ids_example[:, 2].tolist()[i] - 1
																										if col >= 0 and row >= 0 and segment_id == 1:
																																coords_to_probs[(col, row)].append(_SCREAMING_SNAKE_CASE)

																				UpperCamelCase_     :	str					     =     {key: np.array(coords_to_probs[key]).mean() > 0.5 for key in coords_to_probs}

														UpperCamelCase_     :	Optional[Any]					     =     tf.concat(tuple(_SCREAMING_SNAKE_CASE)			,    0)

														return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(_SCREAMING_SNAKE_CASE)			,    0))




		def __call__(  self					:Union[str, Any]			,    *_SCREAMING_SNAKE_CASE					:str			,    **_SCREAMING_SNAKE_CASE					:Any)					->			List[str]:






								'''simple docstring'''






								UpperCamelCase_     :	Dict					     =     self._args_parser(*_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)

								UpperCamelCase_     :	List[str]					     =     super().__call__(_SCREAMING_SNAKE_CASE			,    **_SCREAMING_SNAKE_CASE)
								if len(_SCREAMING_SNAKE_CASE) == 1:
														return results[0]
								return results




		def 	_snake_case	(  self					:Any			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]=None			,    _SCREAMING_SNAKE_CASE					:Dict=None			,    _SCREAMING_SNAKE_CASE					:Optional[int]=None			,    **_SCREAMING_SNAKE_CASE					:List[str])					->			Tuple:






								'''simple docstring'''






								UpperCamelCase_     :	Any					     =     {}
								if padding is not None:
														UpperCamelCase_     :	Tuple					     =     padding
								if truncation is not None:
														UpperCamelCase_     :	Tuple					     =     truncation

								UpperCamelCase_     :	Optional[int]					     =     {}
								if sequential is not None:
														UpperCamelCase_     :	Tuple					     =     sequential
								return preprocess_params, forward_params, {}




		def 	_snake_case	(  self					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:str=None			,    _SCREAMING_SNAKE_CASE					:Tuple=True			,    _SCREAMING_SNAKE_CASE					:str=None)					->			int:






								'''simple docstring'''






								if truncation is None:
														if self.type == "tapas":
																				UpperCamelCase_     :	List[Any]					     =     """drop_rows_to_fit"""
														else:
																				UpperCamelCase_     :	Union[str, Any]					     =     """do_not_truncate"""

								UpperCamelCase_     ,UpperCamelCase_     :	Any					     =     pipeline_input["""table"""], pipeline_input["""query"""]
								if table.empty:
														raise ValueError("""table is empty""")
								if query is None or query == "":
														raise ValueError("""query is empty""")
								UpperCamelCase_     :	Optional[Any]					     =     self.tokenizer(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    return_tensors=self.framework			,    truncation=_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	List[Any]					     =     table
								return inputs




		def 	_snake_case	(  self					:Union[str, Any]			,    _SCREAMING_SNAKE_CASE					:Optional[Any]			,    _SCREAMING_SNAKE_CASE					:List[str]=False)					->			List[Any]:






								'''simple docstring'''






								UpperCamelCase_     :	List[Any]					     =     model_inputs.pop("""table""")

								if self.type == "tapas":
														if sequential:
																				UpperCamelCase_     :	List[Any]					     =     self.sequential_inference(**_SCREAMING_SNAKE_CASE)
														else:
																				UpperCamelCase_     :	str					     =     self.batch_inference(**_SCREAMING_SNAKE_CASE)
								else:
														UpperCamelCase_     :	List[Any]					     =     self.model.generate(**_SCREAMING_SNAKE_CASE)
								UpperCamelCase_     :	Any					     =     {"""model_inputs""": model_inputs, """table""": table, """outputs""": outputs}
								return model_outputs





		def 	_snake_case	(  self					:Tuple			,    _SCREAMING_SNAKE_CASE					:Optional[int])					->			Any:






								'''simple docstring'''






								UpperCamelCase_     :	str					     =     model_outputs["""model_inputs"""]
								UpperCamelCase_     :	int					     =     model_outputs["""table"""]
								UpperCamelCase_     :	Tuple					     =     model_outputs["""outputs"""]
								if self.type == "tapas":
														if self.aggregate:
																				UpperCamelCase_     ,UpperCamelCase_     :	int					     =     outputs[:2]
																				UpperCamelCase_     :	Any					     =     self.tokenizer.convert_logits_to_predictions(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     ,UpperCamelCase_     :	Dict					     =     predictions
																				UpperCamelCase_     :	str					     =     {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(_SCREAMING_SNAKE_CASE)}

																				UpperCamelCase_     :	Tuple					     =     self.model.config.no_aggregation_label_index
																				UpperCamelCase_     :	List[Any]					     =     {
																				    i: aggregators[i] + """ > """ for i, pred in enumerate(_SCREAMING_SNAKE_CASE) if pred != no_agg_label_index
																				}
														else:
																				UpperCamelCase_     :	Dict					     =     outputs[0]
																				UpperCamelCase_     :	List[Any]					     =     self.tokenizer.convert_logits_to_predictions(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE)
																				UpperCamelCase_     :	Optional[int]					     =     predictions[0]
																				UpperCamelCase_     :	Tuple					     =     {}
																				UpperCamelCase_     :	str					     =     {}
														UpperCamelCase_     :	int					     =     []
														for index, coordinates in enumerate(_SCREAMING_SNAKE_CASE):
																				UpperCamelCase_     :	int					     =     [table.iat[coordinate] for coordinate in coordinates]
																				UpperCamelCase_     :	Optional[int]					     =     aggregators.get(_SCREAMING_SNAKE_CASE			,    """""")
																				UpperCamelCase_     :	int					     =     aggregators_prefix.get(_SCREAMING_SNAKE_CASE			,    """""")
																				UpperCamelCase_     :	Optional[Any]					     =     {
																				    """answer""": aggregator_prefix + """, """.join(_SCREAMING_SNAKE_CASE),
																				    """coordinates""": coordinates,
																				    """cells""": [table.iat[coordinate] for coordinate in coordinates],
																				}
																				if aggregator:
																										UpperCamelCase_     :	Union[str, Any]					     =     aggregator

																				answers.append(_SCREAMING_SNAKE_CASE)
														if len(_SCREAMING_SNAKE_CASE) == 0:
																				raise PipelineException("""Empty answer""")
								else:
														UpperCamelCase_     :	Optional[int]					     =     [{"""answer""": answer} for answer in self.tokenizer.batch_decode(_SCREAMING_SNAKE_CASE			,    skip_special_tokens=_SCREAMING_SNAKE_CASE)]

								return answers if len(_SCREAMING_SNAKE_CASE) > 1 else answers[0]




