



from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
	import torch

	from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

UpperCAmelCase							 =					logging.get_logger(__name__)

@add_end_docstrings(SCREAMING_SNAKE_CASE)
class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							def __init__(							self  ,  **a_		):
											super().__init__(**a_		)
											requires_backends(self  ,  """vision"""		)
											requires_backends(self  ,  """torch"""		)

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

											self.check_model_type(a_		)


							def _a      (							self  ,  **a_		):
											a__				      =		{}
											a__				      =		{}
											a__				      =		{}
											# preprocess args
											if "points_per_batch" in kwargs:
															a__				      =		kwargs["""points_per_batch"""]
											if "points_per_crop" in kwargs:
															a__				      =		kwargs["""points_per_crop"""]
											if "crops_n_layers" in kwargs:
															a__				      =		kwargs["""crops_n_layers"""]
											if "crop_overlap_ratio" in kwargs:
															a__				      =		kwargs["""crop_overlap_ratio"""]
											if "crop_n_points_downscale_factor" in kwargs:
															a__				      =		kwargs["""crop_n_points_downscale_factor"""]
											# postprocess args
											if "pred_iou_thresh" in kwargs:
															a__				      =		kwargs["""pred_iou_thresh"""]
											if "stability_score_offset" in kwargs:
															a__				      =		kwargs["""stability_score_offset"""]
											if "mask_threshold" in kwargs:
															a__				      =		kwargs["""mask_threshold"""]
											if "stability_score_thresh" in kwargs:
															a__				      =		kwargs["""stability_score_thresh"""]
											if "crops_nms_thresh" in kwargs:
															a__				      =		kwargs["""crops_nms_thresh"""]
											if "output_rle_mask" in kwargs:
															a__				      =		kwargs["""output_rle_mask"""]
											if "output_bboxes_mask" in kwargs:
															a__				      =		kwargs["""output_bboxes_mask"""]
											return preprocess_kwargs, forward_params, postprocess_kwargs


							def __call__(							self  ,  a_  ,  *a_  ,  a_=None  ,  a_=None  ,  **a_		):
											return super().__call__(a_  ,  *a_  ,  num_workers=a_  ,  batch_size=a_  ,  **a_		)


							def _a      (							self  ,  a_  ,  a_=64  ,  a_ = 0  ,  a_ = 512 / 1_500  ,  a_ = 32  ,  a_ = 1  ,  ):
											a__				      =		load_image(a_		)
											a__				      =		self.image_processor.size["""longest_edge"""]
											a__      ,			a__      ,			a__      ,			a__				      =		self.image_processor.generate_crop_boxes(
											    a_  ,  a_  ,  a_  ,  a_  ,  a_  ,  a_		)
											a__				      =		self.image_processor(images=a_  ,  return_tensors="""pt"""		)

											with self.device_placement():
															if self.framework == "pt":
																			a__				      =		self.get_inference_context()
																			with inference_context():
																							a__				      =		self._ensure_tensor_on_device(a_  ,  device=self.device		)
																							a__				      =		self.model.get_image_embeddings(model_inputs.pop("""pixel_values"""		)		)
																							a__				      =		image_embeddings

											a__				      =		grid_points.shape[1]
											a__				      =		points_per_batch if points_per_batch is not None else n_points

											if points_per_batch <= 0:
															raise ValueError(
															    """Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. """
															    """To return all points at once, set points_per_batch to None"""		)

											for i in range(0  ,  a_  ,  a_		):
															a__				      =		grid_points[:, i : i + points_per_batch, :, :]
															a__				      =		input_labels[:, i : i + points_per_batch]
															a__				      =		i == n_points - points_per_batch
															yield {
															    "input_points": batched_points,
															    "input_labels": labels,
															    "input_boxes": crop_boxes,
															    "is_last": is_last,
															    **model_inputs,
															}


							def _a      (							self  ,  a_  ,  a_=0.88  ,  a_=0.95  ,  a_=0  ,  a_=1  ,  ):
											a__				      =		model_inputs.pop("""input_boxes"""		)
											a__				      =		model_inputs.pop("""is_last"""		)
											a__				      =		model_inputs.pop("""original_sizes"""		).tolist()
											a__				      =		model_inputs.pop("""reshaped_input_sizes"""		).tolist()

											a__				      =		self.model(**a_		)

											# post processing happens here in order to avoid CPU GPU copies of ALL the masks
											a__				      =		model_outputs["""pred_masks"""]
											a__				      =		self.image_processor.post_process_masks(
											    a_  ,  a_  ,  a_  ,  a_  ,  binarize=a_		)
											a__				      =		model_outputs["""iou_scores"""]
											a__      ,			a__      ,			a__				      =		self.image_processor.filter_masks(
											    masks[0]  ,  iou_scores[0]  ,  original_sizes[0]  ,  input_boxes[0]  ,  a_  ,  a_  ,  a_  ,  a_  ,  )
											return {
											    "masks": masks,
											    "is_last": is_last,
											    "boxes": boxes,
											    "iou_scores": iou_scores,
											}







							def _a      (							self  ,  a_  ,  a_=False  ,  a_=False  ,  a_=0.7  ,  ):
											a__				      =		[]
											a__				      =		[]
											a__				      =		[]
											for model_output in model_outputs:
															all_scores.append(model_output.pop("""iou_scores"""		)		)
															all_masks.extend(model_output.pop("""masks"""		)		)
															all_boxes.append(model_output.pop("""boxes"""		)		)

											a__				      =		torch.cat(a_		)
											a__				      =		torch.cat(a_		)
											a__      ,			a__      ,			a__      ,			a__				      =		self.image_processor.post_process_for_mask_generation(
											    a_  ,  a_  ,  a_  ,  a_		)

											a__				      =		defaultdict(a_		)
											for output in model_outputs:
															for k, v in output.items():
																			extra[k].append(a_		)

											a__				      =		{}
											if output_rle_mask:
															a__				      =		rle_mask

											if output_bboxes_mask:
															a__				      =		bounding_boxes

											return {"masks": output_masks, "scores": iou_scores, **optional, **extra}






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
UpperCAmelCase							 =					False
UpperCAmelCase							 =					False

def 			A_      (	__a     :				Namespace		):

				"""simple docstring"""
				return TrainCommand(__a		)




class  __snake_case      (   SCREAMING_SNAKE_CASE):







							'''simple docstring'''






							@staticmethod
							def _a      (							a_		):
											a__				      =		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=32  ,  help="""Batch size for training."""		)
											train_parser.add_argument("""--valid_batch_size"""  ,  type=a_  ,  default=64  ,  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-0_8  ,  help="""Epsilon for Adam optimizer."""		)
											train_parser.set_defaults(func=a_		)


							def __init__(							self  ,  a_		):
											a__				      =		logging.get_logger("""transformers-cli/training"""		)

											a__				      =		"""tf""" if is_tf_available() else """torch"""

											os.makedirs(args.output  ,  exist_ok=a_		)
											a__				      =		args.output

											a__				      =		args.column_label
											a__				      =		args.column_text
											a__				      =		args.column_id

											self.logger.info(F'''Loading {args.task} pipeline for {args.model}'''		)
											if args.task == "text_classification":
															a__				      =		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}'''		)
											a__				      =		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  ,  )
											a__				      =		None
											if args.validation_data:
															self.logger.info(F'''Loading validation dataset from {args.validation_data}'''		)
															a__				      =		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  ,  )

											a__				      =		args.validation_split
											a__				      =		args.train_batch_size
											a__				      =		args.valid_batch_size
											a__				      =		args.learning_rate
											a__				      =		args.adam_epsilon


							def _a      (							self		):
											if self.framework == "tf":
															return self.run_tf()
											return self.run_torch()


							def _a      (							self		):
											raise NotImplementedError







							def _a      (							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		)

