"""simple docstring"""




import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def        lowerCAmelCase_			(					__A,  __A							)					->    List[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  torch.load(lowerCamelCase__,  map_location="cpu"							)

      UpperCAmelCase__								=  chkpt["model"]

      # We have the base model one level deeper than the original XLM repository
      UpperCAmelCase__								=  {}
      for k, v in state_dict.items():
            if "pred_layer" in k:
                  UpperCAmelCase__								=  v
            else:
                  UpperCAmelCase__								=  v

      UpperCAmelCase__								=  chkpt["params"]
      UpperCAmelCase__								=  {n: v for n, v in config.items() if not isinstance(lowerCamelCase__,  (torch.FloatTensor, numpy.ndarray)							)}

      UpperCAmelCase__								=  chkpt["dico_word2id"]
      UpperCAmelCase__								=  {s + "</w>" if s.find("@@"							) == -1 and i > 13 else s.replace("@@",  ""							): i for s, i in vocab.items()}

      # Save pytorch-model
      UpperCAmelCase__								=  pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      UpperCAmelCase__								=  pytorch_dump_folder_path + "/" + CONFIG_NAME
      UpperCAmelCase__								=  pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["vocab_file"]

      print(f"""Save PyTorch model to {pytorch_weights_dump_path}"""							)
      torch.save(lowerCamelCase__,  lowerCamelCase__							)

      print(f"""Save configuration file to {pytorch_config_dump_path}"""							)
      with open(lowerCamelCase__,  "w",  encoding="utf-8"							) as f:
            f.write(json.dumps(lowerCamelCase__,  indent=2							) + "\n"							)

      print(f"""Save vocab file to {pytorch_config_dump_path}"""							)
      with open(lowerCamelCase__,  "w",  encoding="utf-8"							) as f:
            f.write(json.dumps(lowerCamelCase__,  indent=2							) + "\n"							)


if __name__ == "__main__":
      UpperCamelCase__    							= argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
      )
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
      )
      UpperCamelCase__    							= parser.parse_args()
      convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)




# 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							(    UpperCAmelCase_					):
   __UpperCAmelCase			:			List[Any]              =      'facebook/bart-large-mnli'
   __UpperCAmelCase			:			Optional[Any]              =      (
       '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.'
   )
   __UpperCAmelCase			:			Optional[int]              =      'text_classifier'
   __UpperCAmelCase			:			int              =      AutoTokenizer
   __UpperCAmelCase			:			Dict              =      AutoModelForSequenceClassification

   __UpperCAmelCase			:			int              =      ['text', ['text']]
   __UpperCAmelCase			:			Optional[int]              =      ['text']


   def       lowercase_				(self					:						List[Any]		)						->    List[str]:
         """simple docstring"""



         super().setup()
         UpperCAmelCase__								=  self.model.config
         UpperCAmelCase__								=  -1
         for idx, label in config.idalabel.items():
               if label.lower().startswith("entail"		):
                     UpperCAmelCase__								=  int(__UpperCAmelCase		)
         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					:						Union[str, Any]	,  __UpperCAmelCase					:						Dict	,  __UpperCAmelCase					:						int		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  labels
         return self.pre_processor(
             [text] * len(__UpperCAmelCase		)	,  [f"""This example is {label}""" for label in labels]	,  return_tensors="pt"	,  padding="max_length"	,  )





   def       lowercase_				(self					:						Dict	,  __UpperCAmelCase					:						Tuple		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  outputs.logits
         UpperCAmelCase__								=  torch.argmax(logits[:, 2]		).item()
         return self._labels[label_id]



