
"""simple docstring"""


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							   =				16
_lowercase							   =				32






def       _snake_case				(		snake_case__   :     Accelerator	,      snake_case__   :     int = 16	,      snake_case__   :     str = "bert-base-cased"     ):
	A						   =   AutoTokenizer.from_pretrained(snake_case__     )
	A						   =   load_dataset('glue'	,      'mrpc'     )

	def tokenize_function(snake_case__   :     Tuple     ):
		# max_length=None => use the model max length (it's actually the default)
		A						   =   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
	A						   =   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
	A						   =   tokenized_datasets.rename_column('label'	,      'labels'     )

	def collate_fn(snake_case__   :     List[str]     ):
		# 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.
	A						   =   DataLoader(
	    tokenized_datasets['train']	,      shuffle=snake_case__	,      collate_fn=snake_case__	,      batch_size=snake_case__     )
	A						   =   DataLoader(
	    tokenized_datasets['validation']	,      shuffle=snake_case__	,      collate_fn=snake_case__	,      batch_size=snake_case__     )

	return train_dataloader, eval_dataloader






def       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     Union[str, Any]     ):
	# Initialize accelerator
	A						   =   Accelerator()

	# 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						   =   args.model_name_or_path

	set_seed(snake_case__     )
	A   ,      A						   =   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)
	A						   =   AutoModelForSequenceClassification.from_pretrained(snake_case__	,      return_dict=snake_case__     )

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

	if accelerator.state.deepspeed_plugin is not None:
		A						   =   accelerator.state.deepspeed_plugin.deepspeed_config[
		    'gradient_accumulation_steps'
		]
	else:
		A						   =   1
	A						   =   (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
	):
		A						   =   get_linear_schedule_with_warmup(
		    optimizer=snake_case__	,      num_warmup_steps=0	,      num_training_steps=snake_case__	,      )
	else:
		A						   =   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.
	A   ,      A   ,      A   ,      A   ,      A						   =   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
	A						   =   0
	# We also need to keep track of the stating epoch so files are named properly
	A						   =   0

	# Now we train the model
	A						   =   evaluate.load('glue'	,      'mrpc'     )
	A						   =   0
	A						   =   {}
	for epoch in range(snake_case__	,      snake_case__     ):
		model.train()
		for step, batch in enumerate(snake_case__     ):
			A						   =   model(**snake_case__     )
			A						   =   outputs.loss
			A						   =   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()
		A						   =   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():
				A						   =   model(**snake_case__     )
			A						   =   outputs.logits.argmax(dim=-1     )
			# It is slightly faster to call this once, than multiple times
			A   ,      A						   =   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:
					A						   =   predictions[: len(eval_dataloader.dataset     ) - samples_seen]
					A						   =   references[: len(eval_dataloader.dataset     ) - samples_seen]
				else:
					samples_seen += references.shape[0]
			metric.add_batch(
			    predictions=snake_case__	,      references=snake_case__	,      )

		A						   =   metric.compute()
		# Use accelerator.print to print only on the main process.
		accelerator.print(F'epoch {epoch}:'	,      snake_case__     )
		A						   =   eval_metric['accuracy']

		if best_performance < eval_metric["accuracy"]:
			A						   =   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       _snake_case				(		):
	A						   =   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.'	,      )
	A						   =   parser.parse_args()
	A						   =   {'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
	training_function(snake_case__	,      snake_case__     )


if __name__ == "__main__":
		main()

"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import ImageGPTImageProcessor







class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						def __init__( self     :      int  ,A_     :      Tuple  ,A_     :      str=7  ,A_     :      Tuple=3  ,A_     :      List[Any]=18  ,A_     :      List[str]=30  ,A_     :      Optional[Any]=400  ,A_     :      Any=True  ,A_     :      Optional[Any]=None  ,A_     :      List[str]=True  ,)     ->   str:
							A						   =   size if size is not None else {'height': 18, 'width': 18}
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   min_resolution
							A						   =   max_resolution
							A						   =   do_resize
							A						   =   size
							A						   =   do_normalize




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   List[Any]:
							return {
							    # here we create 2 clusters for the sake of simplicity
							    "clusters": np.asarray(
							        [
							            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
							            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
							        ]      ),
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							}







@require_torch
@require_vision
class 				lowerCAmelCase_				(      _lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    ImageGPTImageProcessor if is_vision_available() else None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Any:
							A						   =   ImageGPTImageProcessingTester(self      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Union[str, Any]:
							return self.image_processor_tester.prepare_image_processor_dict()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							self.assertTrue(hasattr(A_  ,'clusters'      )      )
							self.assertTrue(hasattr(A_  ,'do_resize'      )      )
							self.assertTrue(hasattr(A_  ,'size'      )      )
							self.assertTrue(hasattr(A_  ,'do_normalize'      )      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Tuple:
							A						   =   self.image_processing_class.from_dict(self.image_processor_dict      )
							self.assertEqual(image_processor.size  ,{'height': 18, 'width': 18}      )

							A						   =   self.image_processing_class.from_dict(self.image_processor_dict  ,size=42      )
							self.assertEqual(image_processor.size  ,{'height': 42, 'width': 42}      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   str:
							A						   =   self.image_processing_class(**self.image_processor_dict      )
							A						   =   json.loads(image_processor.to_json_string()      )
							for key, value in self.image_processor_dict.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,obj[key]      )      )
								else:
									self.assertEqual(obj[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Union[str, Any]:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								A						   =   os.path.join(A_  ,'image_processor.json'      )
								image_processor_first.to_json_file(A_      )
								A						   =   self.image_processing_class.from_json_file(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   int:
							A						   =   self.image_processing_class(**self.image_processor_dict      )

							with tempfile.TemporaryDirectory() as tmpdirname:
								image_processor_first.save_pretrained(A_      )
								A						   =   self.image_processing_class.from_pretrained(A_      ).to_dict()

							A						   =   image_processor_first.to_dict()
							for key, value in image_processor_first.items():
								if key == "clusters":
									self.assertTrue(np.array_equal(A_  ,image_processor_second[key]      )      )
								else:
									self.assertEqual(image_processor_first[key]  ,A_      )




						@unittest.skip('ImageGPT requires clusters at initialization'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							pass






def       _snake_case				(		):
	A						   =   load_dataset('hf-internal-testing/fixtures_image_utils'	,      split='test'     )

	A						   =   Image.open(dataset[4]['file']     )
	A						   =   Image.open(dataset[5]['file']     )

	A						   =   [imagea, imagea]

	return images







@require_vision
@require_torch
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   int:
							A						   =   ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'      )

							A						   =   prepare_images()

							# test non-batched
							A						   =   image_processing(images[0]  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(1, 1024)      )

							A						   =   [306, 191, 191]
							self.assertEqual(encoding.input_ids[0, :3].tolist()  ,A_      )

							# test batched
							A						   =   image_processing(A_  ,return_tensors='pt'      )

							self.assertIsInstance(encoding.input_ids  ,torch.LongTensor      )
							self.assertEqual(encoding.input_ids.shape  ,(2, 1024)      )

							A						   =   [303, 13, 13]
							self.assertEqual(encoding.input_ids[1, -3:].tolist()  ,A_      )