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#
# 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					(		UpperCAmelCase			):
						_lowercase        =     "facebook/bart-large-mnli"
						_lowercase        =     (
						    "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."
						)
						_lowercase        =     "text_classifier"
						_lowercase        =     AutoTokenizer
						_lowercase        =     AutoModelForSequenceClassification

						_lowercase        =     ["text", ["text"]]
						_lowercase        =     ["text"]


						def     _UpperCAmelCase			(    self		):







									'''simple docstring'''


									super().setup()
									_UpperCAmelCase    :						List[str]							=      self.model.config
									_UpperCAmelCase    :						Any							=      -1
									for idx, label in config.idalabel.items():
												if label.lower().startswith("entail"		):
															_UpperCAmelCase    :						str							=      int(A_		)
									if self.entailment_id == -1:
												raise ValueError("Could not determine the entailment ID from the model config, please pass it at init."		)


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







									'''simple docstring'''


									_UpperCAmelCase    :						Union[str, Any]							=      labels
									return self.pre_processor(
									    [text] * len(A_		)  ,   [f'This example is {label}' for label in labels]  ,   return_tensors="pt"  ,   padding="max_length"  ,   )





						def     _UpperCAmelCase			(    self  ,   A_		):







									'''simple docstring'''


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





from __future__ import annotations

class 			a					:


						def __init__(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Union[str, Any]							=      order

										# a_{0} ... a_{k}
										_UpperCAmelCase    :						Tuple							=      [1.0] + [0.0] * order
										# b_{0} ... b_{k}
										_UpperCAmelCase    :						int							=      [1.0] + [0.0] * order

										# x[n-1] ... x[n-k]
										_UpperCAmelCase    :						Optional[Any]							=      [0.0] * self.order
										# y[n-1] ... y[n-k]
										_UpperCAmelCase    :						Dict							=      [0.0] * self.order


						def     _UpperCAmelCase			(    self  ,   A_  ,   A_		):







										'''simple docstring'''


										if len(A_		) < self.order:
														_UpperCAmelCase    :						List[str]							=      [1.0, *a_coeffs]

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						List[Any]							=      (
														    f'Expected a_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										if len(A_		) != self.order + 1:
														_UpperCAmelCase    :						int							=      (
														    f'Expected b_coeffs to have {self.order + 1} elements '
														    f'for {self.order}-order filter, got {len(A_		)}'
														)
														raise ValueError(A_		)

										_UpperCAmelCase    :						Optional[Any]							=      a_coeffs
										_UpperCAmelCase    :						Union[str, Any]							=      b_coeffs





						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      0.0

										# Start at index 1 and do index 0 at the end.
										for i in range(1  ,   self.order + 1		):
														result += (
														    self.b_coeffs[i] * self.input_history[i - 1]
														    - self.a_coeffs[i] * self.output_history[i - 1]
														)

										_UpperCAmelCase    :						Dict							=      (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

										_UpperCAmelCase    :						Optional[Any]							=      self.input_history[:-1]
										_UpperCAmelCase    :						Optional[int]							=      self.output_history[:-1]

										_UpperCAmelCase    :						Optional[Any]							=      sample
										_UpperCAmelCase    :						str							=      result

										return result


