

from __future__ import annotations

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






def 					lowerCamelCase_     (							UpperCAmelCase_							:     NDArray[floataa]							,       UpperCAmelCase_							:     NDArray[floataa]							,       UpperCAmelCase_							:     list[int]							,       UpperCAmelCase_							:     int							,       )						->				list[float]:




	'''simple docstring'''




	_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						coefficient_matrix.shape
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						constant_matrix.shape

	if rowsa != colsa:
		_UpperCamelCase   :		Any			=						F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
		raise ValueError(UpperCAmelCase_	)

	if colsa != 1:
		_UpperCamelCase   :		str			=						F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
		raise ValueError(UpperCAmelCase_	)

	if rowsa != rowsa:
		_UpperCamelCase   :		int			=						(
		    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
		    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
		)
		raise ValueError(UpperCAmelCase_	)

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

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

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

	_UpperCamelCase	,		_UpperCamelCase   :		int			=						table.shape

	strictly_diagonally_dominant(UpperCAmelCase_	)

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

	return [float(UpperCAmelCase_	) for i in new_val]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     NDArray[floataa]	)						->				bool:




	'''simple docstring'''




	_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						table.shape

	_UpperCamelCase   :		Any			=						True

	for i in range(0							,       UpperCAmelCase_	):
		_UpperCamelCase   :		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()







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool







class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		a__					       =  "facebook/bart-large-mnli"
		a__					       =  (
		    "This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which "
		    "should be the text to classify, and `labels`, which should be the list of labels to use for classification. "
		    "It returns the most likely label in the list of provided `labels` for the input text."
		)
		a__					       =  "text_classifier"
		a__					       =  AutoTokenizer
		a__					       =  AutoModelForSequenceClassification

		a__					       =  ["text", ["text"]]
		a__					       =  ["text"]


		def 		A__					(				self):
			super().setup()
			_UpperCamelCase   :		List[Any]			=						self.model.config
			_UpperCamelCase   :		Optional[int]			=						-1
			for idx, label in config.idalabel.items():
				if label.lower().startswith('entail'):
					_UpperCamelCase   :		Tuple			=						int(__snake_case)
			if self.entailment_id == -1:
				raise ValueError('Could not determine the entailment ID from the model config, please pass it at init.')


		def 		A__					(				self	,       __snake_case	,       __snake_case):
			_UpperCamelCase   :		List[Any]			=						labels
			return self.pre_processor(
			    [text] * len(__snake_case)	,       [f'''This example is {label}''' for label in labels]	,       return_tensors='pt'	,       padding='max_length'	,       )






		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		str			=						outputs.logits
			_UpperCamelCase   :		Optional[Any]			=						torch.argmax(logits[:, 2]).item()
			return self._labels[label_id]




