

from math import factorial

lowerCAmelCase__								=		{str(digit): factorial(digit) for digit in range(1_0)}






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int	)						->				int:




	'''simple docstring'''




	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
		raise TypeError('Parameter number must be int'	)

	if number < 0:
		raise ValueError('Parameter number must be greater than or equal to 0'	)

	# Converts number in string to iterate on its digits and adds its factorial.
	return sum(DIGIT_FACTORIAL[digit] for digit in str(UpperCAmelCase_	)	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     int = 6_0							,       UpperCAmelCase_							:     int = 1_0_0_0_0_0_0	)						->				int:




	'''simple docstring'''




	if not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	) or not isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
		raise TypeError('Parameters chain_length and number_limit must be int'	)

	if chain_length <= 0 or number_limit <= 0:
		raise ValueError(
		    'Parameters chain_length and number_limit must be greater than 0'	)

	# the counter for the chains with the exact desired length
	_UpperCamelCase   :		str			=						0
	# the cached sizes of the previous chains
	_UpperCamelCase   :		dict[int, int]			=						{}

	for start_chain_element in range(1							,       UpperCAmelCase_	):
		# The temporary set will contain the elements of the chain
		_UpperCamelCase   :		int			=						set()
		_UpperCamelCase   :		Optional[Any]			=						0

		# Stop computing the chain when you find a cached size, a repeating item or the
		# length is greater then the desired one.
		_UpperCamelCase   :		int			=						start_chain_element
		while (
		    chain_element not in chain_sets_lengths
		    and chain_element not in chain_set
		    and chain_set_length <= chain_length
		):
			chain_set.add(UpperCAmelCase_	)
			chain_set_length += 1
			_UpperCamelCase   :		Optional[int]			=						digit_factorial_sum(UpperCAmelCase_	)

		if chain_element in chain_sets_lengths:
			chain_set_length += chain_sets_lengths[chain_element]

		_UpperCamelCase   :		List[str]			=						chain_set_length

		# If chain contains the exact amount of elements increase the counter
		if chain_set_length == chain_length:
			chains_counter += 1

	return chains_counter


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f'{solution()}')







# 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]




