






import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
       raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")

# TF training parameters
__lowerCamelCase	:		int										=   False
__lowerCamelCase	:		List[str]										=   False







def 			A__							(      _a  :					Namespace	):





 '''simple docstring'''







 return TrainCommand(_a	)







class  _lowercase   (   __SCREAMING_SNAKE_CASE						):
      @staticmethod
      def 						lowercase__					(     a   ):
       snake_case__  :    Union[str, Any]							       =parser.add_parser("""train"""				,			help="""CLI tool to train a model on a task."""   )

       train_parser.add_argument(
           """--train_data"""				,			type=_a				,			required=_a				,			help="""path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."""				,			)
       train_parser.add_argument(
           """--column_label"""				,			type=_a				,			default=0				,			help="""Column of the dataset csv file with example labels."""   )
       train_parser.add_argument(
           """--column_text"""				,			type=_a				,			default=1				,			help="""Column of the dataset csv file with example texts."""   )
       train_parser.add_argument(
           """--column_id"""				,			type=_a				,			default=2				,			help="""Column of the dataset csv file with example ids."""   )
       train_parser.add_argument(
           """--skip_first_row"""				,			action="""store_true"""				,			help="""Skip the first row of the csv file (headers)."""   )

       train_parser.add_argument("""--validation_data"""				,			type=_a				,			default=""""""				,			help="""path to validation dataset."""   )
       train_parser.add_argument(
           """--validation_split"""				,			type=_a				,			default=0.1				,			help="""if validation dataset is not provided, fraction of train dataset to use as validation dataset."""				,			)

       train_parser.add_argument("""--output"""				,			type=_a				,			default="""./"""				,			help="""path to saved the trained model."""   )

       train_parser.add_argument(
           """--task"""				,			type=_a				,			default="""text_classification"""				,			help="""Task to train the model on."""   )
       train_parser.add_argument(
           """--model"""				,			type=_a				,			default="""bert-base-uncased"""				,			help="""Model's name or path to stored model."""   )
       train_parser.add_argument("""--train_batch_size"""				,			type=_a				,			default=3_2				,			help="""Batch size for training."""   )
       train_parser.add_argument("""--valid_batch_size"""				,			type=_a				,			default=6_4				,			help="""Batch size for validation."""   )
       train_parser.add_argument("""--learning_rate"""				,			type=_a				,			default=3e-5				,			help="""Learning rate."""   )
       train_parser.add_argument("""--adam_epsilon"""				,			type=_a				,			default=1e-08				,			help="""Epsilon for Adam optimizer."""   )
       train_parser.set_defaults(func=_a   )
      def __init__(     self				,			a   ):
       snake_case__  :    int							       =logging.get_logger("""transformers-cli/training"""   )

       snake_case__  :    Optional[Any]							       ="""tf""" if is_tf_available() else """torch"""

       os.makedirs(args.output				,			exist_ok=_a   )
       snake_case__  :    Any							       =args.output

       snake_case__  :    str							       =args.column_label
       snake_case__  :    List[Any]							       =args.column_text
       snake_case__  :    Dict							       =args.column_id

       self.logger.info(F"Loading {args.task} pipeline for {args.model}"   )
       if args.task == "text_classification":
        snake_case__  :    List[Any]							       =TextClassificationPipeline.from_pretrained(args.model   )
       elif args.task == "token_classification":
        raise NotImplementedError
       elif args.task == "question_answering":
        raise NotImplementedError

       self.logger.info(F"Loading dataset from {args.train_data}"   )
       snake_case__  :    Tuple							       =Processor.create_from_csv(
           args.train_data				,			column_label=args.column_label				,			column_text=args.column_text				,			column_id=args.column_id				,			skip_first_row=args.skip_first_row				,			)
       snake_case__  :    List[Any]							       =None
       if args.validation_data:
        self.logger.info(F"Loading validation dataset from {args.validation_data}"   )
        snake_case__  :    Tuple							       =Processor.create_from_csv(
            args.validation_data				,			column_label=args.column_label				,			column_text=args.column_text				,			column_id=args.column_id				,			skip_first_row=args.skip_first_row				,			)

       snake_case__  :    int							       =args.validation_split
       snake_case__  :    Optional[Any]							       =args.train_batch_size
       snake_case__  :    List[Any]							       =args.valid_batch_size
       snake_case__  :    List[Any]							       =args.learning_rate
       snake_case__  :    Optional[int]							       =args.adam_epsilon
      def 						lowercase__					(     self   ):




       if self.framework == "tf":
        return self.run_tf()
       return self.run_torch()
      def 						lowercase__					(     self   ):
       raise NotImplementedError



      def 						lowercase__					(     self   ):
       self.pipeline.fit(
           self.train_dataset				,			validation_data=self.valid_dataset				,			validation_split=self.validation_split				,			learning_rate=self.learning_rate				,			adam_epsilon=self.adam_epsilon				,			train_batch_size=self.train_batch_size				,			valid_batch_size=self.valid_batch_size				,			)

       # Save trained pipeline
       self.pipeline.save_pretrained(self.output   )













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(_A						)
class  _lowercase   (   _A						):
      def __init__(     self				,			**a   ):
       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				,			):
       if "text_queries" in kwargs:
        snake_case__  :    Any							       =kwargs.pop("""text_queries"""   )

       if isinstance(a				,			(str, Image.Image)   ):
        snake_case__  :    Union[str, Any]							       ={"""image""": image, """candidate_labels""": candidate_labels}
       else:
        snake_case__  :    str							       =image
       snake_case__  :    Union[str, Any]							       =super().__call__(a				,			**a   )
       return results
      def 						lowercase__					(     self				,			**a   ):
       snake_case__  :    Union[str, Any]							       ={}
       if "threshold" in kwargs:
        snake_case__  :    Optional[Any]							       =kwargs["""threshold"""]
       if "top_k" in kwargs:
        snake_case__  :    Dict							       =kwargs["""top_k"""]
       return {}, {}, postprocess_params
      def 						lowercase__					(     self				,			a   ):
       snake_case__  :    List[Any]							       =load_image(inputs["""image"""]   )
       snake_case__  :    Dict							       =inputs["""candidate_labels"""]
       if isinstance(a				,			a   ):
        snake_case__  :    str							       =candidate_labels.split(""","""   )

       snake_case__  :    Optional[Any]							       =torch.tensor([[image.height, image.width]]				,			dtype=torch.intaa   )
       for i, candidate_label in enumerate(a   ):
        snake_case__  :    Tuple							       =self.tokenizer(a				,			return_tensors=self.framework   )
        snake_case__  :    int							       =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 						lowercase__					(     self				,			a   ):
       snake_case__  :    Tuple							       =model_inputs.pop("""target_size"""   )
       snake_case__  :    Tuple							       =model_inputs.pop("""candidate_label"""   )
       snake_case__  :    Dict							       =model_inputs.pop("""is_last"""   )

       snake_case__  :    Any							       =self.model(**a   )

       snake_case__  :    List[Any]							       ={"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
       return model_outputs
      def 						lowercase__					(     self				,			a				,			a=0.1				,			a=None   ):
       snake_case__  :    Optional[int]							       =[]
       for model_output in model_outputs:
        snake_case__  :    Tuple							       =model_output["""candidate_label"""]
        snake_case__  :    Optional[int]							       =BaseModelOutput(a   )
        snake_case__  :    int							       =self.image_processor.post_process_object_detection(
            outputs=a				,			threshold=a				,			target_sizes=model_output["""target_size"""]   )[0]

        for index in outputs["scores"].nonzero():
         snake_case__  :    int							       =outputs["""scores"""][index].item()
         snake_case__  :    Tuple							       =self._get_bounding_box(outputs["""boxes"""][index][0]   )

         snake_case__  :    Tuple							       ={"""score""": score, """label""": label, """box""": box}
         results.append(a   )

       snake_case__  :    Tuple							       =sorted(a				,			key=lambda a   : x["score"]				,			reverse=a   )
       if top_k:
        snake_case__  :    Union[str, Any]							       =results[:top_k]

       return results



      def 						lowercase__					(     self				,			a   ):
       if self.framework != "pt":
        raise ValueError("""The ZeroShotObjectDetectionPipeline is only available in PyTorch."""   )
       snake_case__       ,   snake_case__       ,   snake_case__       ,   snake_case__  :    Dict							       =box.int().tolist()
       snake_case__  :    int							       ={
           """xmin""": xmin,
           """ymin""": ymin,
           """xmax""": xmax,
           """ymax""": ymax,
       }
       return bbox





