

"""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():
						import tensorflow as tf

						from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
						from ..tf_utils import stable_softmax

if is_torch_available():
						from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCAmelCase							:  str            =						logging.get_logger(__name__)



@add_end_docstrings(UpperCAmelCase_     )
class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def __init__(			self  :					Optional[Any]       ,						*lowerCAmelCase__  :					List[str]       ,						**lowerCAmelCase__  :					Optional[Any]):
									super().__init__(*lowerCAmelCase__       ,						**lowerCAmelCase__)
									requires_backends(self       ,						"vision")
									self.check_model_type(
									    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
									    if self.framework == "tf"
									    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]       ,						lowerCAmelCase__  :					Dict=None):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	{}
									if top_k is not None:
															SCREAMING_SNAKE_CASE_:			List[str]  		=	top_k
									return {}, {}, postprocess_params



			def __call__(			self  :					List[Any]       ,						lowerCAmelCase__  :					Union[str, List[str], "Image.Image", List["Image.Image"]]       ,						**lowerCAmelCase__  :					List[Any]):
									return super().__call__(lowerCAmelCase__       ,						**lowerCAmelCase__)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict       ,						lowerCAmelCase__  :					Any):
									SCREAMING_SNAKE_CASE_:			int  		=	load_image(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			Any  		=	self.image_processor(images=lowerCAmelCase__       ,						return_tensors=self.framework)
									return model_inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.model(**lowerCAmelCase__)
									return model_outputs




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					Tuple       ,						lowerCAmelCase__  :					Dict=5):
									if top_k > self.model.config.num_labels:
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.model.config.num_labels

									if self.framework == "pt":
															SCREAMING_SNAKE_CASE_:			int  		=	model_outputs.logits.softmax(-1)[0]
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	probs.topk(lowerCAmelCase__)
									elif self.framework == "tf":
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	stable_softmax(model_outputs.logits       ,						axis=-1)[0]
															SCREAMING_SNAKE_CASE_:			List[Any]  		=	tf.math.top_k(lowerCAmelCase__       ,						k=lowerCAmelCase__)
															SCREAMING_SNAKE_CASE_:			Dict  		=	topk.values.numpy(), topk.indices.numpy()
									else:
															raise ValueError(F"Unsupported framework: {self.framework}")

									SCREAMING_SNAKE_CASE_:			Tuple  		=	scores.tolist()
									SCREAMING_SNAKE_CASE_:			Tuple  		=	ids.tolist()
									return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCAmelCase__       ,						lowerCAmelCase__)]









import doctest
from collections import deque

import numpy as np



class   __lowercase    :




			"""simple docstring"""







			def __init__(			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			int  		=	[2, 1, 2, -1]
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	[1, 2, 3, 4]




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Any  		=	len(self.first_signal)
									SCREAMING_SNAKE_CASE_:			Dict  		=	len(self.second_signal)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	max(lowerCAmelCase__       ,						lowerCAmelCase__)

									# create a zero matrix of max_length x max_length
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	[[0] * max_length for i in range(lowerCAmelCase__)]

									# fills the smaller signal with zeros to make both signals of same length
									if length_first_signal < length_second_signal:
															self.first_signal += [0] * (max_length - length_first_signal)
									elif length_first_signal > length_second_signal:
															self.second_signal += [0] * (max_length - length_second_signal)
									for i in range(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_:			Tuple  		=	deque(self.second_signal)
															rotated_signal.rotate(lowerCAmelCase__)
															for j, item in enumerate(lowerCAmelCase__):
																					matrix[i][j] += item

        # multiply the matrix with the first signal
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	np.matmul(np.transpose(lowerCAmelCase__)       ,						np.transpose(self.first_signal))

									# rounding-off to two decimal places
									return [round(lowerCAmelCase__       ,						2) for i in final_signal]


if __name__ == "__main__":
						doctest.testmod()






