





from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray







def  _SCREAMING_SNAKE_CASE				(   a					,						a					,						a					,						a					,						)      ->      list[float]:
					__A	:						Union[str, Any]       =				coefficient_matrix.shape
					__A	:						Any       =				constant_matrix.shape

					if rowsa != colsa:
										__A	:						Any       =				F"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
										raise ValueError(lowerCamelCase_  )

					if colsa != 1:
										__A	:						List[str]       =				F"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
										raise ValueError(lowerCamelCase_  )

					if rowsa != rowsa:
										__A	:						Any       =				(
										    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
										    F"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
										)
										raise ValueError(lowerCamelCase_  )

					if len(lowerCamelCase_  ) != rowsa:
										__A	:						Optional[Any]       =				(
										    'Number of initial values must be equal to number of rows in coefficient '
										    F"""matrix but received {len(lowerCamelCase_  )} and {rowsa}"""
										)
										raise ValueError(lowerCamelCase_  )

					if iterations <= 0:
										raise ValueError('Iterations must be at least 1'  )

					__A	:						NDArray[floataa]       =				np.concatenate(
					    (coefficient_matrix, constant_matrix)					,						axis=1  )

					__A	:						Optional[int]       =				table.shape

					strictly_diagonally_dominant(lowerCamelCase_  )

					# Iterates the whole matrix for given number of times
					for _ in range(lowerCamelCase_  ):
										__A	:						List[str]       =				[]
										for row in range(lowerCamelCase_  ):
															__A	:						List[str]       =				0
															for col in range(lowerCamelCase_  ):
																				if col == row:
																									__A	:						Union[str, Any]       =				table[row][col]
																				elif col == cols - 1:
																									__A	:						Optional[int]       =				table[row][col]
																				else:
																									temp += (-1) * table[row][col] * init_val[col]
															__A	:						Union[str, Any]       =				(temp + val) / denom
															new_val.append(lowerCamelCase_  )
										__A	:						Any       =				new_val

					return [float(lowerCamelCase_  ) for i in new_val]







def  _SCREAMING_SNAKE_CASE				(   a  )      ->      bool:
					__A	:						Any       =				table.shape

					__A	:						List[Any]       =				True

					for i in range(0					,						lowerCamelCase_  ):
										__A	:						Optional[Any]       =				0
										for j in range(0					,						cols - 1  ):
															if i == j:
																				continue
															else:
																				total += table[i][j]

										if table[i][i] <= total:
															raise ValueError('Coefficient matrix is not strictly diagonally dominant'  )

					return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
				import doctest

				doctest.testmod()








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

SCREAMING_SNAKE_CASE       :      List[str]												= logging.get_logger(__name__)






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a   ):
       def __init__(					self,      *lowerCamelCase,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           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 			UpperCamelCase     (					self,      lowerCamelCase=None)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		{}
           if top_k is not None:
               _lowercase       :							List[str]      		=		top_k
           return {}, {}, postprocess_params
       def __call__(					self,      lowerCamelCase,      **lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           return super().__call__(lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		load_image(lowerCamelCase)
           _lowercase       :							List[str]      		=		self.image_processor(images=lowerCamelCase,      return_tensors=self.framework)
           return model_inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.model(**lowerCamelCase)
           return model_outputs




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=5)       ->   Dict:




           """simple docstring"""

           if top_k > self.model.config.num_labels:
               _lowercase       :							List[Any]      		=		self.model.config.num_labels

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

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


