





# 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        _a(    __A    ):
		lowerCamelCase__     :Tuple   				=		'facebook/bart-large-mnli'
		lowerCamelCase__     :List[str]   				=		(
		    '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.'
		)
		lowerCamelCase__     :Tuple   				=		'text_classifier'
		lowerCamelCase__     :Tuple   				=		AutoTokenizer
		lowerCamelCase__     :int   				=		AutoModelForSequenceClassification

		lowerCamelCase__     :Optional[int]   				=		['text', ['text']]
		lowerCamelCase__     :str   				=		['text']







		def        lowercase       (    self				)      ->      Optional[Any]:


							'''simple docstring'''

							super().setup()
							_snake_case       :					int				       =   self.model.config
							_snake_case       :					List[str]				       =   -1
							for idx, label in config.idalabel.items():
												if label.lower().startswith("entail"				):
																	_snake_case       :					str				       =   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        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      Tuple:


							'''simple docstring'''

							_snake_case       :					int				       =   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        lowercase       (    self ,						__snake_case				)      ->      Any:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   outputs.logits
							_snake_case       :					List[Any]				       =   torch.argmax(logits[:, 2]				).item()
							return self._labels[label_id]






import numpy as np

__lowerCAmelCase :Dict     =    [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class        _a:







		def __init__(    self				)      ->      None:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   np.array(__snake_case				)







		def        lowercase       (    self ,						__snake_case				)      ->      np.ndarray:


							'''simple docstring'''

							_snake_case       , _snake_case       :					List[Any]				       =   np.where(letter == self.SQUARE				)
							_snake_case       :					Any				       =   np.concatenate([indexa + 1, indexa + 1]				)
							return indexes







		def        lowercase       (    self ,						__snake_case ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					int				       =   self.SQUARE[indexa - 1, indexa - 1]
							return letter







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Optional[Any]				       =   message.lower()
							_snake_case       :					List[Any]				       =   message.replace(" " ,						""				)
							_snake_case       :					Any				       =   message.replace("j" ,						"i"				)

							_snake_case       :					Tuple				       =   np.empty((2, len(__snake_case				))				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   self.letter_to_numbers(message[letter_index]				)

												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					List[Any]				       =   numbers[1]

							_snake_case       :					Any				       =   first_step.reshape(2 * len(__snake_case				)				)
							_snake_case       :					Optional[Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Optional[int]				       =   int(second_step[numbers_index * 2]				)
												_snake_case       :					List[str]				       =   int(second_step[(numbers_index * 2) + 1]				)
												_snake_case       :					Any				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					Any				       =   encoded_message + letter

							return encoded_message







		def        lowercase       (    self ,						__snake_case				)      ->      str:


							'''simple docstring'''

							_snake_case       :					Union[str, Any]				       =   message.lower()
							message.replace(" " ,						""				)
							_snake_case       :					Tuple				       =   np.empty(2 * len(__snake_case				)				)
							for letter_index in range(len(__snake_case				)				):
												_snake_case       :					Union[str, Any]				       =   self.letter_to_numbers(message[letter_index]				)
												_snake_case       :					Tuple				       =   numbers[0]
												_snake_case       :					Any				       =   numbers[1]

							_snake_case       :					Optional[Any]				       =   first_step.reshape((2, len(__snake_case				))				)
							_snake_case       :					Union[str, Any]				       =   ""
							for numbers_index in range(len(__snake_case				)				):
												_snake_case       :					Dict				       =   int(second_step[0, numbers_index]				)
												_snake_case       :					Optional[Any]				       =   int(second_step[1, numbers_index]				)
												_snake_case       :					Union[str, Any]				       =   self.numbers_to_letter(__snake_case ,						__snake_case				)
												_snake_case       :					str				       =   decoded_message + letter

							return decoded_message