


from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
				import torch

if is_tf_available():
				import tensorflow as tf







class   _UpperCamelCase  (       A_  ):







					'''simple docstring'''

					lowerCamelCase     :     Optional[int]			    =       ['image_processor']
					lowerCamelCase     :     Any			    =       'SamImageProcessor'





					def __init__(						self    :  Any ,					__lowercase    :  Optional[int]		):






						'''simple docstring'''







						super().__init__(__A		)
						UpperCAmelCase_								=     self.image_processor
						UpperCAmelCase_								=     -10
						UpperCAmelCase_								=     self.image_processor.size["longest_edge"]





					def __call__(						self    :  Any ,					__lowercase    :  Tuple=None ,					__lowercase    :  Any=None ,					__lowercase    :  Optional[Any]=None ,					__lowercase    :  Dict=None ,					__lowercase    :  Optional[Union[str, TensorType]] = None ,					**__lowercase    :  str ,					):






						'''simple docstring'''







						UpperCAmelCase_								=     self.image_processor(
						    __A ,					return_tensors=__A ,					**__A ,					)

						# pop arguments that are not used in the foward but used nevertheless
						UpperCAmelCase_								=     encoding_image_processor["original_sizes"]

						if hasattr(__A ,					"""numpy"""		):  # Checks if Torch or TF tensor
							UpperCAmelCase_								=     original_sizes.numpy()

						UpperCAmelCase_								=     self._check_and_preprocess_points(
						    input_points=__A ,					input_labels=__A ,					input_boxes=__A ,					)

						UpperCAmelCase_								=     self._normalize_and_convert(
						    __A ,					__A ,					input_points=__A ,					input_labels=__A ,					input_boxes=__A ,					return_tensors=__A ,					)

						return encoding_image_processor





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  List[str] ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  List[str]=None ,					__lowercase    :  int=None ,					__lowercase    :  Any=None ,					__lowercase    :  int="pt" ,					):






						'''simple docstring'''







						if input_points is not None:
							if len(__A		) != len(__A		):
								UpperCAmelCase_								=     [
								    self._normalize_coordinates(self.target_size ,					__A ,					original_sizes[0]		) for point in input_points
								]
							else:
								UpperCAmelCase_								=     [
								    self._normalize_coordinates(self.target_size ,					__A ,					__A		)
								    for point, original_size in zip(__A ,					__A		)
								]
							# check that all arrays have the same shape
							if not all(point.shape == input_points[0].shape for point in input_points		):
								if input_labels is not None:
									UpperCAmelCase_								=     self._pad_points_and_labels(__A ,					__A		)

							UpperCAmelCase_								=     np.array(__A		)

						if input_labels is not None:
							UpperCAmelCase_								=     np.array(__A		)

						if input_boxes is not None:
							if len(__A		) != len(__A		):
								UpperCAmelCase_								=     [
								    self._normalize_coordinates(self.target_size ,					__A ,					original_sizes[0] ,					is_bounding_box=__A		)
								    for box in input_boxes
								]
							else:
								UpperCAmelCase_								=     [
								    self._normalize_coordinates(self.target_size ,					__A ,					__A ,					is_bounding_box=__A		)
								    for box, original_size in zip(__A ,					__A		)
								]
							UpperCAmelCase_								=     np.array(__A		)

						if input_boxes is not None:
							if return_tensors == "pt":
								UpperCAmelCase_								=     torch.from_numpy(__A		)
								# boxes batch size of 1 by default
								UpperCAmelCase_								=     input_boxes.unsqueeze(1		) if len(input_boxes.shape		) != 3 else input_boxes
							elif return_tensors == "tf":
								UpperCAmelCase_								=     tf.convert_to_tensor(__A		)
								# boxes batch size of 1 by default
								UpperCAmelCase_								=     tf.expand_dims(__A ,					1		) if len(input_boxes.shape		) != 3 else input_boxes
							encoding_image_processor.update({"""input_boxes""": input_boxes}		)
						if input_points is not None:
							if return_tensors == "pt":
								UpperCAmelCase_								=     torch.from_numpy(__A		)
								# point batch size of 1 by default
								UpperCAmelCase_								=     input_points.unsqueeze(1		) if len(input_points.shape		) != 4 else input_points
							elif return_tensors == "tf":
								UpperCAmelCase_								=     tf.convert_to_tensor(__A		)
								# point batch size of 1 by default
								UpperCAmelCase_								=     tf.expand_dims(__A ,					1		) if len(input_points.shape		) != 4 else input_points
							encoding_image_processor.update({"""input_points""": input_points}		)
						if input_labels is not None:
							if return_tensors == "pt":
								UpperCAmelCase_								=     torch.from_numpy(__A		)
								# point batch size of 1 by default
								UpperCAmelCase_								=     input_labels.unsqueeze(1		) if len(input_labels.shape		) != 3 else input_labels
							elif return_tensors == "tf":
								UpperCAmelCase_								=     tf.convert_to_tensor(__A		)
								# point batch size of 1 by default
								UpperCAmelCase_								=     tf.expand_dims(__A ,					1		) if len(input_labels.shape		) != 3 else input_labels
							encoding_image_processor.update({"""input_labels""": input_labels}		)

						return encoding_image_processor





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  Dict ,					__lowercase    :  str		):






						'''simple docstring'''







						UpperCAmelCase_								=     max([point.shape[0] for point in input_points]		)
						UpperCAmelCase_								=     []
						for i, point in enumerate(__A		):
							if point.shape[0] != expected_nb_points:
								UpperCAmelCase_								=     np.concatenate(
								    [point, np.zeros((expected_nb_points - point.shape[0], 2)		) + self.point_pad_value] ,					axis=0		)
								UpperCAmelCase_								=     np.append(input_labels[i] ,					[self.point_pad_value]		)
							processed_input_points.append(__A		)
						UpperCAmelCase_								=     processed_input_points
						return input_points, input_labels





					def        SCREAMING_SNAKE_CASE							(						self    :  int ,					__lowercase    :  int ,					__lowercase    :  np.ndarray ,					__lowercase    :  Union[str, Any] ,					__lowercase    :  Optional[int]=False		):






						'''simple docstring'''







						UpperCAmelCase_								=     original_size
						UpperCAmelCase_								=     self.image_processor._get_preprocess_shape(__A ,					longest_edge=__A		)
						UpperCAmelCase_								=     deepcopy(__A		).astype(__A		)

						if is_bounding_box:
							UpperCAmelCase_								=     coords.reshape(-1 ,					2 ,					2		)

						UpperCAmelCase_								=     coords[..., 0] * (new_w / old_w)
						UpperCAmelCase_								=     coords[..., 1] * (new_h / old_h)

						if is_bounding_box:
							UpperCAmelCase_								=     coords.reshape(-1 ,					4		)

						return coords





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					__lowercase    :  str=None ,					__lowercase    :  Tuple=None ,					__lowercase    :  List[Any]=None ,					):






						'''simple docstring'''







						if input_points is not None:
							if hasattr(__A ,					"""numpy"""		):  # Checks for TF or Torch tensor
								UpperCAmelCase_								=     input_points.numpy().tolist()

							if not isinstance(__A ,					__A		) or not isinstance(input_points[0] ,					__A		):
								raise ValueError("""Input points must be a list of list of floating points."""		)
							UpperCAmelCase_								=     [np.array(__A		) for input_point in input_points]
						else:
							UpperCAmelCase_								=     None

						if input_labels is not None:
							if hasattr(__A ,					"""numpy"""		):
								UpperCAmelCase_								=     input_labels.numpy().tolist()

							if not isinstance(__A ,					__A		) or not isinstance(input_labels[0] ,					__A		):
								raise ValueError("""Input labels must be a list of list integers."""		)
							UpperCAmelCase_								=     [np.array(__A		) for label in input_labels]
						else:
							UpperCAmelCase_								=     None

						if input_boxes is not None:
							if hasattr(__A ,					"""numpy"""		):
								UpperCAmelCase_								=     input_boxes.numpy().tolist()

							if (
							    not isinstance(__A ,					__A		)
							    or not isinstance(input_boxes[0] ,					__A		)
							    or not isinstance(input_boxes[0][0] ,					__A		)
							):
								raise ValueError("""Input boxes must be a list of list of list of floating points."""		)
							UpperCAmelCase_								=     [np.array(__A		).astype(np.floataa		) for box in input_boxes]
						else:
							UpperCAmelCase_								=     None

						return input_points, input_labels, input_boxes





					@property
					def        SCREAMING_SNAKE_CASE							(						self    :  Optional[int]		):






						'''simple docstring'''







						UpperCAmelCase_								=     self.image_processor.model_input_names
						return list(dict.fromkeys(__A		)		)





					def        SCREAMING_SNAKE_CASE							(						self    :  List[str] ,					*__lowercase    :  int ,					**__lowercase    :  Union[str, Any]		):






						'''simple docstring'''







						return self.image_processor.post_process_masks(*__A ,					**__A		)









import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
# and perform gradient accumulation
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


UpperCamelCase__   : Union[str, Any]					  = 16
UpperCamelCase__   : Tuple					  = 32




def    A_(			A				,      A = 16   ):
	UpperCAmelCase_								=     AutoTokenizer.from_pretrained("""bert-base-cased"""   )
	UpperCAmelCase_								=     load_dataset("""glue"""				,      """mrpc"""   )

	def tokenize_function(A   ):
		# max_length=None => use the model max length (it's actually the default)
		UpperCAmelCase_								=     tokenizer(examples["""sentence1"""]				,      examples["""sentence2"""]				,      truncation=A				,      max_length=A   )
		return outputs

	# Apply the method we just defined to all the examples in all the splits of the dataset
	# starting with the main process first:
	with accelerator.main_process_first():
		UpperCAmelCase_								=     datasets.map(
		    A				,      batched=A				,      remove_columns=["""idx""", """sentence1""", """sentence2"""]				,      )

	# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
	# transformers library
	UpperCAmelCase_								=     tokenized_datasets.rename_column("""label"""				,      """labels"""   )

	def collate_fn(A   ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		UpperCAmelCase_								=     128 if accelerator.distributed_type == DistributedType.TPU else None
		# When using mixed precision we want round multiples of 8/16
		if accelerator.mixed_precision == "fp8":
			UpperCAmelCase_								=     16
		elif accelerator.mixed_precision != "no":
			UpperCAmelCase_								=     8
		else:
			UpperCAmelCase_								=     None

		return tokenizer.pad(
		    A				,      padding="""longest"""				,      max_length=A				,      pad_to_multiple_of=A				,      return_tensors="""pt"""				,      )

	# Instantiate dataloaders.
	UpperCAmelCase_								=     DataLoader(
	    tokenized_datasets["""train"""]				,      shuffle=A				,      collate_fn=A				,      batch_size=A   )
	UpperCAmelCase_								=     DataLoader(
	    tokenized_datasets["""validation"""]				,      shuffle=A				,      collate_fn=A				,      batch_size=A   )

	return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
				from accelerate.test_utils.training import mocked_dataloaders

				UpperCamelCase__   : str					  = mocked_dataloaders  # noqa: F811




def    A_(			A				,      A   ):
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""				,      A   ) == "1":
		UpperCAmelCase_								=     2
	# New Code #
	UpperCAmelCase_								=     int(args.gradient_accumulation_steps   )
	# Initialize accelerator
	UpperCAmelCase_								=     Accelerator(
	    cpu=args.cpu				,      mixed_precision=args.mixed_precision				,      gradient_accumulation_steps=A   )
	if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
		raise NotImplementedError(
		    """Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`"""   )
	# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
	UpperCAmelCase_								=     config["""lr"""]
	UpperCAmelCase_								=     int(config["""num_epochs"""]   )
	UpperCAmelCase_								=     int(config["""seed"""]   )
	UpperCAmelCase_								=     int(config["""batch_size"""]   )

	UpperCAmelCase_								=     evaluate.load("""glue"""				,      """mrpc"""   )

	set_seed(A   )
	UpperCAmelCase_						,	UpperCAmelCase_								=     get_dataloaders(A				,      A   )
	# Instantiate the model (we build the model here so that the seed also control new weights initialization)
	UpperCAmelCase_								=     AutoModelForSequenceClassification.from_pretrained("""bert-base-cased"""				,      return_dict=A   )

	# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
	# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
	# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
	UpperCAmelCase_								=     model.to(accelerator.device   )

	# Instantiate optimizer
	UpperCAmelCase_								=     AdamW(params=model.parameters()				,      lr=A   )

	# Instantiate scheduler
	UpperCAmelCase_								=     get_linear_schedule_with_warmup(
	    optimizer=A				,      num_warmup_steps=100				,      num_training_steps=(len(A   ) * num_epochs)				,      )

	# Prepare everything
	# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
	# prepare method.
	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_						,	UpperCAmelCase_								=     accelerator.prepare(
	    A				,      A				,      A				,      A				,      A   )

	# Now we train the model
	for epoch in range(A   ):
		model.train()
		for step, batch in enumerate(A   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			# New code #
			# We use the new `accumulate` context manager to perform gradient accumulation
			# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
			with accelerator.accumulate(A   ):
				UpperCAmelCase_								=     model(**A   )
				UpperCAmelCase_								=     output.loss
				accelerator.backward(A   )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

		model.eval()
		for step, batch in enumerate(A   ):
			# We could avoid this line since we set the accelerator with `device_placement=True`.
			batch.to(accelerator.device   )
			with torch.no_grad():
				UpperCAmelCase_								=     model(**A   )
			UpperCAmelCase_								=     outputs.logits.argmax(dim=-1   )
			UpperCAmelCase_						,	UpperCAmelCase_								=     accelerator.gather_for_metrics((predictions, batch["""labels"""])   )
			metric.add_batch(
			    predictions=A				,      references=A				,      )

		UpperCAmelCase_								=     metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f"""epoch {epoch}:"""				,      A   )




def    A_(			):
	UpperCAmelCase_								=     argparse.ArgumentParser(description="""Simple example of training script."""   )
	parser.add_argument(
	    """--mixed_precision"""				,      type=A				,      default=A				,      choices=["""no""", """fp16""", """bf16""", """fp8"""]				,      help="""Whether to use mixed precision. Choose"""
	    """between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
	    """and an Nvidia Ampere GPU."""				,      )
	# New Code #
	parser.add_argument(
	    """--gradient_accumulation_steps"""				,      type=A				,      default=1				,      help="""The number of minibatches to be ran before gradients are accumulated."""				,      )
	parser.add_argument("""--cpu"""				,      action="""store_true"""				,      help="""If passed, will train on the CPU."""   )
	UpperCAmelCase_								=     parser.parse_args()
	UpperCAmelCase_								=     {"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(A				,      A   )


if __name__ == "__main__":
				main()


