
import argparse
import json
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
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowercase__							:							Optional[int]									=			1_6
lowercase__							:							Tuple									=			3_2




def 	A_ (							snake_case							:   Accelerator ,      snake_case							:   int = 16 ,      snake_case							:   str = "bert-base-cased" )      ->    Optional[int]:



						'''simple docstring'''

						__UpperCamelCase       =			AutoTokenizer.from_pretrained(snake_case )
						__UpperCamelCase       =			load_dataset('''glue''' ,      '''mrpc''' )

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

						# Apply the method we just defined to all the examples in all the splits of the dataset
						__UpperCamelCase       =			datasets.map(
						    snake_case ,      batched=snake_case ,      remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,      load_from_cache_file=snake_case )

						# 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(snake_case							:   Tuple ):
												# On TPU it's best to pad everything to the same length or training will be very slow.
												if accelerator.distributed_type == DistributedType.TPU:
																		return tokenizer.pad(snake_case ,      padding='''max_length''' ,      max_length=128 ,      return_tensors='''pt''' )
												return tokenizer.pad(snake_case ,      padding='''longest''' ,      return_tensors='''pt''' )

						# Instantiate dataloaders.
						__UpperCamelCase       =			DataLoader(
						    tokenized_datasets['''train'''] ,      shuffle=snake_case ,      collate_fn=snake_case ,      batch_size=snake_case )
						__UpperCamelCase       =			DataLoader(
						    tokenized_datasets['''validation'''] ,      shuffle=snake_case ,      collate_fn=snake_case ,      batch_size=snake_case )

						return train_dataloader, eval_dataloader




def 	A_ (							snake_case							:   Optional[Any] ,      snake_case							:   Optional[Any] )      ->    Tuple:



						'''simple docstring'''

						__UpperCamelCase       =			Accelerator()

						# 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       =			args.model_name_or_path

						set_seed(snake_case )
						__UpperCamelCase  ,							__UpperCamelCase       =			get_dataloaders(snake_case ,      snake_case ,      snake_case )

						# Instantiate the model (we build the model here so that the seed also control new weights initialization)
						__UpperCamelCase       =			AutoModelForSequenceClassification.from_pretrained(snake_case ,      return_dict=snake_case )

						# Instantiate optimizer
						__UpperCamelCase       =			(
						    AdamW
						    if accelerator.state.deepspeed_plugin is None
						    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
						    else DummyOptim
						)
						__UpperCamelCase       =			optimizer_cls(params=model.parameters() ,      lr=snake_case )

						if accelerator.state.deepspeed_plugin is not None:
												__UpperCamelCase       =			accelerator.state.deepspeed_plugin.deepspeed_config[
												    '''gradient_accumulation_steps'''
												]
						else:
												__UpperCamelCase       =			1
						__UpperCamelCase       =			(len(snake_case ) * num_epochs) // gradient_accumulation_steps

						# Instantiate scheduler
						if (
						    accelerator.state.deepspeed_plugin is None
						    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
						):
												__UpperCamelCase       =			get_linear_schedule_with_warmup(
												    optimizer=snake_case ,      num_warmup_steps=0 ,      num_training_steps=snake_case ,      )
						else:
												__UpperCamelCase       =			DummyScheduler(snake_case ,      total_num_steps=snake_case ,      warmup_num_steps=0 )

						# 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(
						    snake_case ,      snake_case ,      snake_case ,      snake_case ,      snake_case )

						# We need to keep track of how many total steps we have iterated over
						__UpperCamelCase       =			0
						# We also need to keep track of the stating epoch so files are named properly
						__UpperCamelCase       =			0

						# Now we train the model
						__UpperCamelCase       =			evaluate.load('''glue''' ,      '''mrpc''' )
						__UpperCamelCase       =			0
						__UpperCamelCase       =			{}
						for epoch in range(snake_case ,      snake_case ):
												model.train()
												for step, batch in enumerate(snake_case ):
																		__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.loss
																		__UpperCamelCase       =			loss / gradient_accumulation_steps
																		accelerator.backward(snake_case )
																		if step % gradient_accumulation_steps == 0:
																								optimizer.step()
																								lr_scheduler.step()
																								optimizer.zero_grad()

																		overall_step += 1

												model.eval()
												__UpperCamelCase       =			0
												for step, batch in enumerate(snake_case ):
																		# We could avoid this line since we set the accelerator with `device_placement=True`.
																		batch.to(accelerator.device )
																		with torch.no_grad():
																								__UpperCamelCase       =			model(**snake_case )
																		__UpperCamelCase       =			outputs.logits.argmax(dim=-1 )
																		# It is slightly faster to call this once, than multiple times
																		__UpperCamelCase  ,							__UpperCamelCase       =			accelerator.gather(
																		    (predictions, batch['''labels''']) )  # If we are in a multiprocess environment, the last batch has duplicates
																		if accelerator.use_distributed:
																								if step == len(snake_case ) - 1:
																														__UpperCamelCase       =			predictions[: len(eval_dataloader.dataset ) - samples_seen]
																														__UpperCamelCase       =			references[: len(eval_dataloader.dataset ) - samples_seen]
																								else:
																														samples_seen += references.shape[0]
																		metric.add_batch(
																		    predictions=snake_case ,      references=snake_case ,      )

												__UpperCamelCase       =			metric.compute()
												# Use accelerator.print to print only on the main process.
												accelerator.print(f"epoch {epoch}:" ,      snake_case )
												__UpperCamelCase       =			eval_metric['''accuracy''']

												if best_performance < eval_metric["accuracy"]:
																		__UpperCamelCase       =			eval_metric['''accuracy''']

						if args.performance_lower_bound is not None:
												assert (
												    args.performance_lower_bound <= best_performance
												), f"Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
												with open(os.path.join(args.output_dir ,      '''all_results.json''' ) ,      '''w''' ) as f:
																		json.dump(snake_case ,      snake_case )




def 	A_ (							)      ->    str:



						'''simple docstring'''

						__UpperCamelCase       =			argparse.ArgumentParser(description='''Simple example of training script tracking peak GPU memory usage.''' )
						parser.add_argument(
						    '''--model_name_or_path''' ,      type=snake_case ,      default='''bert-base-cased''' ,      help='''Path to pretrained model or model identifier from huggingface.co/models.''' ,      required=snake_case ,      )
						parser.add_argument(
						    '''--output_dir''' ,      type=snake_case ,      default='''.''' ,      help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.''' ,      )
						parser.add_argument(
						    '''--performance_lower_bound''' ,      type=snake_case ,      default=snake_case ,      help='''Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.''' ,      )
						parser.add_argument(
						    '''--num_epochs''' ,      type=snake_case ,      default=3 ,      help='''Number of train epochs.''' ,      )
						__UpperCamelCase       =			parser.parse_args()
						__UpperCamelCase       =			{'''lr''': 2e-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}
						training_function(snake_case ,      snake_case )


if __name__ == "__main__":
				main()





from collections import UserDict
from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
				from PIL import Image

				from ..image_utils import load_image

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

if is_tf_available():
				from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
				from ..tf_utils import stable_softmax

lowercase__							:							int									=			logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE_    )
class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							def __init__(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													super().__init__(**SCREAMING_SNAKE_CASE_			)

													requires_backends(self     ,	'''vision'''			)
													self.check_model_type(
													    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
													    if self.framework == '''tf'''
													    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING			)



							def __call__(  self     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   str:





													'''simple docstring'''






													return super().__call__(SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	**SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






													__UpperCamelCase       =			{}
													if "candidate_labels" in kwargs:
																			__UpperCamelCase       =			kwargs['''candidate_labels''']
													if "hypothesis_template" in kwargs:
																			__UpperCamelCase       =			kwargs['''hypothesis_template''']

													return preprocess_params, {}, {}



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_="This is a photo of {}."			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			load_image(SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			self.image_processor(images=[image]     ,	return_tensors=self.framework			)
													__UpperCamelCase       =			candidate_labels
													__UpperCamelCase       =			[hypothesis_template.format(SCREAMING_SNAKE_CASE_			) for x in candidate_labels]
													__UpperCamelCase       =			self.tokenizer(SCREAMING_SNAKE_CASE_     ,	return_tensors=self.framework     ,	padding=SCREAMING_SNAKE_CASE_			)
													__UpperCamelCase       =			[text_inputs]
													return inputs



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   Dict:





													'''simple docstring'''






													__UpperCamelCase       =			model_inputs.pop('''candidate_labels'''			)
													__UpperCamelCase       =			model_inputs.pop('''text_inputs'''			)
													if isinstance(text_inputs[0]     ,	SCREAMING_SNAKE_CASE_			):
																			__UpperCamelCase       =			text_inputs[0]
													else:
																			# Batching case.
																			__UpperCamelCase       =			text_inputs[0][0]

													__UpperCamelCase       =			self.model(**SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)

													__UpperCamelCase       =			{
													    '''candidate_labels''': candidate_labels,
													    '''logits''': outputs.logits_per_image,
													}
													return model_outputs



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_			)->   int:





													'''simple docstring'''






													__UpperCamelCase       =			model_outputs.pop('''candidate_labels'''			)
													__UpperCamelCase       =			model_outputs['''logits'''][0]
													if self.framework == "pt":
																			__UpperCamelCase       =			logits.softmax(dim=-1			).squeeze(-1			)
																			__UpperCamelCase       =			probs.tolist()
																			if not isinstance(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			):
																									__UpperCamelCase       =			[scores]
													elif self.framework == "tf":
																			__UpperCamelCase       =			stable_softmax(SCREAMING_SNAKE_CASE_     ,	axis=-1			)
																			__UpperCamelCase       =			probs.numpy().tolist()
													else:
																			raise ValueError(F"Unsupported framework: {self.framework}"			)

													__UpperCamelCase       =			[
													    {'''score''': score, '''label''': candidate_label}
													    for score, candidate_label in sorted(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)     ,	key=lambda SCREAMING_SNAKE_CASE_			: -x[0]			)
													]
													return result




