





"""simple docstring"""





import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						List[Any]			):







      '''simple docstring'''




      return 1.0 / (1.0 + np.exp(-_outputs			))


def 			UpperCAmelCase__							(  SCREAMING_SNAKE_CASE     :						Dict			):







      '''simple docstring'''




      lowerCAmelCase  						=       np.max(_outputs							,						axis=-1							,						keepdims=SCREAMING_SNAKE_CASE			)
      lowerCAmelCase  						=       np.exp(_outputs - maxes			)
      return shifted_exp / shifted_exp.sum(axis=-1							,						keepdims=SCREAMING_SNAKE_CASE			)

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      'sigmoid'
      _SCREAMING_SNAKE_CASE					=      'softmax'
      _SCREAMING_SNAKE_CASE					=      'none'







@add_end_docstrings(
    _UpperCAmelCase     ,  r'\n        return_all_scores (`bool`, *optional*, defaults to `False`):\n            Whether to return all prediction scores or just the one of the predicted class.\n        function_to_apply (`str`, *optional*, defaults to `"default"`):\n            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:\n\n            - `"default"`: if the model has a single label, will apply the sigmoid function on the output. If the model\n              has several labels, will apply the softmax function on the output.\n            - `"sigmoid"`: Applies the sigmoid function on the output.\n            - `"softmax"`: Applies the softmax function on the output.\n            - `"none"`: Does not apply any function on the output.\n    '     ,  )
class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      False
      _SCREAMING_SNAKE_CASE					=      ClassificationFunction.NONE





      def __init__(       self      ,						**lowercase )		->     Tuple:
            super().__init__(**lowercase )

            self.check_model_type(
                TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
                if self.framework == """tf"""
                else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )





      def     _snake_case			(       self      ,						lowercase=None      ,						lowercase=None      ,						lowercase=""      ,						**lowercase )		->     Dict:
            # Using "" as default argument because we're going to use `top_k=None` in user code to declare
            # "No top_k"
            lowerCAmelCase  						=       tokenizer_kwargs

            lowerCAmelCase  						=       {}
            if hasattr(self.model.config      ,						"""return_all_scores""" ) and return_all_scores is None:
                  lowerCAmelCase  						=       self.model.config.return_all_scores

            if isinstance(lowercase      ,						lowercase ) or top_k is None:
                  lowerCAmelCase  						=       top_k
                  lowerCAmelCase  						=       False
            elif return_all_scores is not None:
                  warnings.warn(
                      """`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of"""
                      """ `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`."""      ,						lowercase      ,						)
                  if return_all_scores:
                        lowerCAmelCase  						=       None
                  else:
                        lowerCAmelCase  						=       1

            if isinstance(lowercase      ,						lowercase ):
                  lowerCAmelCase  						=       ClassificationFunction[function_to_apply.upper()]

            if function_to_apply is not None:
                  lowerCAmelCase  						=       function_to_apply
            return preprocess_params, {}, postprocess_params





      def __call__(       self      ,						*lowercase      ,						**lowercase )		->     int:
            lowerCAmelCase  						=       super().__call__(*lowercase      ,						**lowercase )
            # TODO try and retrieve it in a nicer way from _sanitize_parameters.
            lowerCAmelCase  						=       """top_k""" not in kwargs
            if isinstance(args[0]      ,						lowercase ) and _legacy:
                  # This pipeline is odd, and return a list when single item is run
                  return [result]
            else:
                  return result





      def     _snake_case			(       self      ,						lowercase      ,						**lowercase )		->     Dict[str, GenericTensor]:
            lowerCAmelCase  						=       self.framework
            if isinstance(lowercase      ,						lowercase ):
                  return self.tokenizer(**lowercase      ,						return_tensors=lowercase      ,						**lowercase )
            elif isinstance(lowercase      ,						lowercase ) and len(lowercase ) == 1 and isinstance(inputs[0]      ,						lowercase ) and len(inputs[0] ) == 2:
                  # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
                  return self.tokenizer(
                      text=inputs[0][0]      ,						text_pair=inputs[0][1]      ,						return_tensors=lowercase      ,						**lowercase )
            elif isinstance(lowercase      ,						lowercase ):
                  # This is likely an invalid usage of the pipeline attempting to pass text pairs.
                  raise ValueError(
                      """The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
                      """ dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.""" )
            return self.tokenizer(lowercase      ,						return_tensors=lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     Optional[Any]:
            return self.model(**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase=None      ,						lowercase=1      ,						lowercase=True )		->     Optional[int]:
            # `_legacy` is used to determine if we're running the naked pipeline and in backward
            # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
            # the more natural result containing the list.
            # Default value before `set_parameters`
            if function_to_apply is None:
                  if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                        lowerCAmelCase  						=       ClassificationFunction.SIGMOID
                  elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                        lowerCAmelCase  						=       ClassificationFunction.SOFTMAX
                  elif hasattr(self.model.config      ,						"""function_to_apply""" ) and function_to_apply is None:
                        lowerCAmelCase  						=       self.model.config.function_to_apply
                  else:
                        lowerCAmelCase  						=       ClassificationFunction.NONE

            lowerCAmelCase  						=       model_outputs["""logits"""][0]
            lowerCAmelCase  						=       outputs.numpy()

            if function_to_apply == ClassificationFunction.SIGMOID:
                  lowerCAmelCase  						=       sigmoid(lowercase )
            elif function_to_apply == ClassificationFunction.SOFTMAX:
                  lowerCAmelCase  						=       softmax(lowercase )
            elif function_to_apply == ClassificationFunction.NONE:
                  lowerCAmelCase  						=       outputs
            else:
                  raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}' )

            if top_k == 1 and _legacy:
                  return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

            lowerCAmelCase  						=       [
                {"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(lowercase )
            ]
            if not _legacy:
                  dict_scores.sort(key=lambda lowercase : x["score"]      ,						reverse=lowercase )
                  if top_k is not None:
                        lowerCAmelCase  						=       dict_scores[:top_k]
            return dict_scores












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					:       Any    					=      16
__UpperCamelCase					:       Union[str, Any]    					=      32





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase = 16    )      ->       List[str]:
	a										=							AutoTokenizer.from_pretrained("""bert-base-cased"""    )
	a										=							load_dataset("""glue"""							,			"""mrpc"""    )

	def tokenize_function(__lowerCamelCase    ):
		# max_length=None => use the model max length (it's actually the default)
		a										=							tokenizer(examples["""sentence1"""]							,			examples["""sentence2"""]							,			truncation=__lowerCamelCase							,			max_length=__lowerCamelCase    )
		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():
		a										=							datasets.map(
		    __lowerCamelCase							,			batched=__lowerCamelCase							,			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
	a										=							tokenized_datasets.rename_column("""label"""							,			"""labels"""    )

	def collate_fn(__lowerCamelCase    ):
		# On TPU it's best to pad everything to the same length or training will be very slow.
		a										=							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":
			a										=							16
		elif accelerator.mixed_precision != "no":
			a										=							8
		else:
			a										=							None

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

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

	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					:       Any    					=      mocked_dataloaders  # noqa: F811





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       List[str]:
	# For testing only
	if os.environ.get("""TESTING_MOCKED_DATALOADERS"""							,			__lowerCamelCase    ) == "1":
		a										=							2
	# New Code #
	a										=							int(args.gradient_accumulation_steps    )
	# Initialize accelerator
	a										=							Accelerator(
	    cpu=args.cpu							,			mixed_precision=args.mixed_precision							,			gradient_accumulation_steps=__lowerCamelCase    )
	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
	a										=							config["""lr"""]
	a										=							int(config["""num_epochs"""]    )
	a										=							int(config["""seed"""]    )
	a										=							int(config["""batch_size"""]    )

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

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

	# 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).
	a										=							model.to(accelerator.device    )

	# Instantiate optimizer
	a										=							AdamW(params=model.parameters()							,			lr=__lowerCamelCase    )

	# Instantiate scheduler
	a										=							get_linear_schedule_with_warmup(
	    optimizer=__lowerCamelCase							,			num_warmup_steps=100							,			num_training_steps=(len(__lowerCamelCase    ) * 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.
	a				, a				, a				, a				, a										=							accelerator.prepare(
	    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )

	# Now we train the model
	for epoch in range(__lowerCamelCase    ):
		model.train()
		for step, batch in enumerate(__lowerCamelCase    ):
			# 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(__lowerCamelCase    ):
				a										=							model(**__lowerCamelCase    )
				a										=							output.loss
				accelerator.backward(__lowerCamelCase    )
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()

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

		a										=							metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(f'epoch {epoch}:'							,			__lowerCamelCase    )





def 		__A		(	)      ->       int:
	a										=							argparse.ArgumentParser(description="""Simple example of training script."""    )
	parser.add_argument(
	    """--mixed_precision"""							,			type=__lowerCamelCase							,			default=__lowerCamelCase							,			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=__lowerCamelCase							,			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."""    )
	a										=							parser.parse_args()
	a										=							{"""lr""": 2E-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
	training_function(__lowerCamelCase							,			__lowerCamelCase    )


if __name__ == "__main__":
		main()

