


"""simple docstring"""







import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


lowerCamelCase_       :       List[Any]      						=  {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()
def _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase=None		):







    """simple docstring"""
    a					=XLNetConfig.from_json_file(lowercase		)

    a					=finetuning_task.lower() if finetuning_task is not None else ''''''
    if finetuning_task in GLUE_TASKS_NUM_LABELS:
        print(f'''Building PyTorch XLNetForSequenceClassification model from configuration: {config}'''		)
        a					=finetuning_task
        a					=GLUE_TASKS_NUM_LABELS[finetuning_task]
        a					=XLNetForSequenceClassification(lowercase		)
    elif "squad" in finetuning_task:
        a					=finetuning_task
        a					=XLNetForQuestionAnswering(lowercase		)
    else:
        a					=XLNetLMHeadModel(lowercase		)

    # Load weights from tf checkpoint
    load_tf_weights_in_xlnet(lowercase	,   lowercase	,   lowercase		)

    # Save pytorch-model
    a					=os.path.join(lowercase	,   lowercase		)
    a					=os.path.join(lowercase	,   lowercase		)
    print(f'''Save PyTorch model to {os.path.abspath(lowercase		)}'''		)
    torch.save(model.state_dict()	,   lowercase		)
    print(f'''Save configuration file to {os.path.abspath(lowercase		)}'''		)
    with open(lowercase	,   '''w'''	,   encoding='''utf-8'''		) as f:
        f.write(config.to_json_string()		)


if __name__ == "__main__":
      lowerCamelCase_       :       List[Any]      						=  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
      )
      parser.add_argument(
          """--xlnet_config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained XLNet model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
      )
      parser.add_argument(
          """--finetuning_task""",
          default=None,
          type=str,
          help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
      )
      lowerCamelCase_       :       Dict      						=  parser.parse_args()
      print(args)

      convert_xlnet_checkpoint_to_pytorch(
          args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
      )



"""simple docstring"""







from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase_       :       Dict      						=  logging.get_logger(__name__)






@add_end_docstrings(_SCREAMING_SNAKE_CASE      )
class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 def __init__(		self			,   **__A						) ->					Dict:
     super().__init__(**__A						)

     if self.framework == "tf":
         raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''						)

     requires_backends(self			,   '''vision'''						)
     self.check_model_type(__A						)


 def __call__(		self			,   __A			,   __A = None			,   **__A			,   ) ->					List[str]:
     if "text_queries" in kwargs:
         a					=kwargs.pop('''text_queries'''						)

     if isinstance(__A			,   (str, Image.Image)						):
         a					={'''image''': image, '''candidate_labels''': candidate_labels}
     else:
         a					=image
     a					=super().__call__(__A			,   **__A						)
     return results


 def 				SCREAMING_SNAKE_CASE							(		self			,   **__A						) ->					Optional[Any]:
     a					={}
     if "threshold" in kwargs:
         a					=kwargs['''threshold''']
     if "top_k" in kwargs:
         a					=kwargs['''top_k''']
     return {}, {}, postprocess_params


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					str:
     a					=load_image(inputs['''image''']						)
     a					=inputs['''candidate_labels''']
     if isinstance(__A			,   __A						):
         a					=candidate_labels.split(''','''						)

     a					=torch.tensor([[image.height, image.width]]			,   dtype=torch.intaa						)
     for i, candidate_label in enumerate(__A						):
         a					=self.tokenizer(__A			,   return_tensors=self.framework						)
         a					=self.image_processor(__A			,   return_tensors=self.framework						)
         yield {
             "is_last": i == len(__A						) - 1,
             "target_size": target_size,
             "candidate_label": candidate_label,
             **text_inputs,
             **image_features,
         }


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					List[Any]:
     a					=model_inputs.pop('''target_size'''						)
     a					=model_inputs.pop('''candidate_label'''						)
     a					=model_inputs.pop('''is_last'''						)

     a					=self.model(**__A						)

     a					={'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
     return model_outputs


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A=0.1			,   __A=None						) ->					List[str]:
     a					=[]
     for model_output in model_outputs:
         a					=model_output['''candidate_label''']
         a					=BaseModelOutput(__A						)
         a					=self.image_processor.post_process_object_detection(
             outputs=__A			,   threshold=__A			,   target_sizes=model_output['''target_size''']						)[0]

         for index in outputs["scores"].nonzero():
             a					=outputs['''scores'''][index].item()
             a					=self._get_bounding_box(outputs['''boxes'''][index][0]						)

             a					={'''score''': score, '''label''': label, '''box''': box}
             results.append(__A						)

     a					=sorted(__A			,   key=lambda __A						: x["score"]			,   reverse=__A						)
     if top_k:
         a					=results[:top_k]

     return results





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A						) ->					Dict[str, int]:
     if self.framework != "pt":
         raise ValueError('''The ZeroShotObjectDetectionPipeline is only available in PyTorch.'''						)
     a		,				a		,				a		,				a					=box.int().tolist()
     a					={
         '''xmin''': xmin,
         '''ymin''': ymin,
         '''xmax''': xmax,
         '''ymax''': ymax,
     }
     return bbox