"""simple docstring"""





import importlib.metadata
import operator
import re
import sys
from typing import Optional

from packaging import version


lowerCAmelCase_     =	{
    '''<''': operator.lt,
    '''<=''': operator.le,
    '''==''': operator.eq,
    '''!=''': operator.ne,
    '''>=''': operator.ge,
    '''>''': operator.gt,
}



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    Tuple:
							if got_ver is None or want_ver is None:
														raise ValueError(
														    F"""Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"""
														    F""" reinstalling {pkg}."""  )
							if not ops[op](version.parse(__SCREAMING_SNAKE_CASE  )     ,	version.parse(__SCREAMING_SNAKE_CASE  )  ):
														raise ImportError(
														    F"""{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE = None  )->    None:
							_SCREAMING_SNAKE_CASE		:						int			  =    F"""\n{hint}""" if hint is not None else """"""

							# non-versioned check
							if re.match(R"""^[\w_\-\d]+$"""     ,	__SCREAMING_SNAKE_CASE  ):
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Any			  =    requirement, None, None
							else:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    re.findall(R"""^([^!=<>\s]+)([\s!=<>]{1,2}.+)"""     ,	__SCREAMING_SNAKE_CASE  )
														if not match:
																					raise ValueError(
																					    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"""
																					    F""" got {requirement}"""  )
														_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    match[0]
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    want_full.split(""","""  )  # there could be multiple requirements
														_SCREAMING_SNAKE_CASE		:						Dict			  =    {}
														for w in want_range:
																					_SCREAMING_SNAKE_CASE		:						str			  =    re.findall(R"""^([\s!=<>]{1,2})(.+)"""     ,	__SCREAMING_SNAKE_CASE  )
																					if not match:
																												raise ValueError(
																												    """requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"""
																												    F""" but got {requirement}"""  )
																					_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    match[0]
																					_SCREAMING_SNAKE_CASE		:						Dict			  =    want_ver
																					if op not in ops:
																												raise ValueError(F"""{requirement}: need one of {list(ops.keys()  )}, but got {op}"""  )

    # special case
							if pkg == "python":
														_SCREAMING_SNAKE_CASE		:						Dict			  =    """.""".join([str(__SCREAMING_SNAKE_CASE  ) for x in sys.version_info[:3]]  )
														for op, want_ver in wanted.items():
																					_compare_versions(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
														return

							# check if any version is installed
							try:
														_SCREAMING_SNAKE_CASE		:						Tuple			  =    importlib.metadata.version(__SCREAMING_SNAKE_CASE  )
							except importlib.metadata.PackageNotFoundError:
														raise importlib.metadata.PackageNotFoundError(
														    F"""The '{requirement}' distribution was not found and is required by this application. {hint}"""  )

							# check that the right version is installed if version number or a range was provided
							if want_ver is not None:
														for op, want_ver in wanted.items():
																					_compare_versions(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    """Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"""
							return require_version(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )



"""simple docstring"""





# 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  _snake_case		(    __snake_case ):







				"""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 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											super().setup()
											_SCREAMING_SNAKE_CASE		:						Any			  =    self.model.config
											_SCREAMING_SNAKE_CASE		:						Any			  =    -1
											for idx, label in config.idalabel.items():
																		if label.lower().startswith("""entail"""):
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    _A							:							Tuple      ,    _A							:							List[str]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[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 		_lowerCAmelCase		(      self							:							Tuple      ,    _A							:							Optional[Any]):



											"""simple docstring"""







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


