



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()

def        a						(      _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Union[str, Any] ,   _UpperCAmelCase							:					List[Any] ,   _UpperCAmelCase							:					Optional[Any] ,   _UpperCAmelCase							:					int						):







		'''simple docstring'''





		__UpperCAmelCase    :     Optional[int]													=	TapasConfig.from_json_file(_UpperCAmelCase						)
		# set absolute/relative position embeddings parameter
		__UpperCAmelCase    :     Tuple													=	reset_position_index_per_cell

		# set remaining parameters of TapasConfig as well as the model based on the task
		if task == "SQA":
				__UpperCAmelCase    :     Optional[Any]													=	TapasForQuestionAnswering(config=_UpperCAmelCase						)
		elif task == "WTQ":
				# run_task_main.py hparams
				__UpperCAmelCase    :     Union[str, Any]													=	4
				__UpperCAmelCase    :     int													=	True
				# hparam_utils.py hparams
				__UpperCAmelCase    :     List[str]													=	0.66_46_94
				__UpperCAmelCase    :     List[str]													=	0.20_79_51
				__UpperCAmelCase    :     List[str]													=	0.12_11_94
				__UpperCAmelCase    :     str													=	True
				__UpperCAmelCase    :     Dict													=	True
				__UpperCAmelCase    :     Tuple													=	False
				__UpperCAmelCase    :     List[Any]													=	0.0_35_25_13

				__UpperCAmelCase    :     Tuple													=	TapasForQuestionAnswering(config=_UpperCAmelCase						)
		elif task == "WIKISQL_SUPERVISED":
				# run_task_main.py hparams
				__UpperCAmelCase    :     List[Any]													=	4
				__UpperCAmelCase    :     Tuple													=	False
				# hparam_utils.py hparams
				__UpperCAmelCase    :     Optional[int]													=	36.45_19
				__UpperCAmelCase    :     Dict													=	0.90_34_21
				__UpperCAmelCase    :     List[str]													=	2_22.0_88
				__UpperCAmelCase    :     Optional[int]													=	True
				__UpperCAmelCase    :     List[str]													=	True
				__UpperCAmelCase    :     Any													=	True
				__UpperCAmelCase    :     List[Any]													=	0.76_31_41

				__UpperCAmelCase    :     Union[str, Any]													=	TapasForQuestionAnswering(config=_UpperCAmelCase						)
		elif task == "TABFACT":
				__UpperCAmelCase    :     Optional[Any]													=	TapasForSequenceClassification(config=_UpperCAmelCase						)
		elif task == "MLM":
				__UpperCAmelCase    :     Optional[int]													=	TapasForMaskedLM(config=_UpperCAmelCase						)
		elif task == "INTERMEDIATE_PRETRAINING":
				__UpperCAmelCase    :     Dict													=	TapasModel(config=_UpperCAmelCase						)
		else:
				raise ValueError(f'Task {task} not supported.'						)

		print(f'Building PyTorch model from configuration: {config}'						)
		# Load weights from tf checkpoint
		load_tf_weights_in_tapas(_UpperCAmelCase ,   _UpperCAmelCase ,   _UpperCAmelCase						)

		# Save pytorch-model (weights and configuration)
		print(f'Save PyTorch model to {pytorch_dump_path}'						)
		model.save_pretrained(_UpperCAmelCase						)

		# Save tokenizer files
		print(f'Save tokenizer files to {pytorch_dump_path}'						)
		__UpperCAmelCase    :     List[str]													=	TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + '''vocab.txt''' ,   model_max_length=5_12						)
		tokenizer.save_pretrained(_UpperCAmelCase						)

		print('''Used relative position embeddings:''' ,   model.config.reset_position_index_per_cell						)


if __name__ == "__main__":
				__A   			=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--task", default="SQA", type=str, help="Model task for which to convert a checkpoint. Defaults to SQA."
				)
				parser.add_argument(
				    "--reset_position_index_per_cell",
				    default=False,
				    action="store_true",
				    help="Whether to use relative position embeddings or not. Defaults to True.",
				)
				parser.add_argument(
				    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
				)
				parser.add_argument(
				    "--tapas_config_file",
				    default=None,
				    type=str,
				    required=True,
				    help=(
				        "The config json file corresponding to the pre-trained TAPAS model. \n"
				        "This specifies the model architecture."
				    ),
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				__A   			=parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.task,
				    args.reset_position_index_per_cell,
				    args.tf_checkpoint_path,
				    args.tapas_config_file,
				    args.pytorch_dump_path,
				)










from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros

def        a						(      _UpperCAmelCase							:					Tuple ,   _UpperCAmelCase							:					Optional[int]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Union[str, Any]													=	k_size // 2
		__UpperCAmelCase							,					__UpperCAmelCase    :     Optional[Any]													=	mgrid[0 - center : k_size - center, 0 - center : k_size - center]
		__UpperCAmelCase    :     Any													=	1 / (2 * pi * sigma) * exp(-(square(_UpperCAmelCase						) + square(_UpperCAmelCase						)) / (2 * square(_UpperCAmelCase						))						)
		return g

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any ,   _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     Union[str, Any]													=	image.shape[0], image.shape[1]
		# dst image height and width
		__UpperCAmelCase    :     str													=	height - k_size + 1
		__UpperCAmelCase    :     Optional[int]													=	width - k_size + 1

		# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
		__UpperCAmelCase    :     str													=	zeros((dst_height * dst_width, k_size * k_size)						)
		__UpperCAmelCase    :     Optional[Any]													=	0
		for i, j in product(range(_UpperCAmelCase						) ,   range(_UpperCAmelCase						)						):
				__UpperCAmelCase    :     int													=	ravel(image[i : i + k_size, j : j + k_size]						)
				__UpperCAmelCase    :     Union[str, Any]													=	window
				row += 1

		#  turn the kernel into shape(k*k, 1)
		__UpperCAmelCase    :     Tuple													=	gen_gaussian_kernel(_UpperCAmelCase ,   _UpperCAmelCase						)
		__UpperCAmelCase    :     List[Any]													=	ravel(_UpperCAmelCase						)

		# reshape and get the dst image
		__UpperCAmelCase    :     Optional[Any]													=	dot(_UpperCAmelCase ,   _UpperCAmelCase						).reshape(_UpperCAmelCase ,   _UpperCAmelCase						).astype(_UpperCAmelCase						)

		return dst


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

				# get values with two different mask size
				__A   			=gaussian_filter(gray, 3, sigma=1)
				__A   			=gaussian_filter(gray, 5, sigma=0.8)

				# show result images
				imshow("gaussian filter with 3x3 mask", gaussianaxa)
				imshow("gaussian filter with 5x5 mask", gaussianaxa)
				waitKey()





