



import argparse
import gc
import json
import os

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


SCREAMING_SNAKE_CASE_:Any					=						16
SCREAMING_SNAKE_CASE_:int					=						32




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  int:



    """simple docstring"""






    return int(x / 2**20		)






class SCREAMING_SNAKE_CASE__					:




    '''simple docstring'''






    def __enter__(	self		):
        gc.collect()
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()  # reset the peak gauge to zero
        A			:	List[str]										=       torch.cuda.memory_allocated()
        return self






    def __exit__(	self,		*lowerCamelCase__		):
        gc.collect()
        torch.cuda.empty_cache()
        A			:	Any										=       torch.cuda.memory_allocated()
        A			:	List[Any]										=       torch.cuda.max_memory_allocated()
        A			:	Optional[int]										=       bamb(self.end - self.begin		)
        A			:	Union[str, Any]										=       bamb(self.peak - self.begin		)
        # print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase = 16				,					_lowerCAmelCase = "bert-base-cased"				,					_lowerCAmelCase = 320				,					_lowerCAmelCase = 160				,					)	->  List[str]:



    """simple docstring"""






    A			:	int										=       AutoTokenizer.from_pretrained(_lowerCAmelCase		)
    A			:	Tuple										=       load_dataset(
        """glue"""				,					"""mrpc"""				,					split={"""train""": f'''train[:{n_train}]''', """validation""": f'''validation[:{n_val}]'''}		)

    def tokenize_function(_lowerCAmelCase		):
        # max_length=None => use the model max length (it's actually the default)
        A			:	str										=       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
    A			:	int										=       datasets.map(
        _lowerCAmelCase				,					batched=_lowerCAmelCase				,					remove_columns=["""idx""", """sentence1""", """sentence2"""]				,					load_from_cache_file=_lowerCAmelCase		)

    # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
    # transformers library
    A			:	Tuple										=       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.
        if accelerator.distributed_type == DistributedType.TPU:
            return tokenizer.pad(_lowerCAmelCase				,					padding="""max_length"""				,					max_length=128				,					return_tensors="""pt"""		)
        return tokenizer.pad(_lowerCAmelCase				,					padding="""longest"""				,					return_tensors="""pt"""		)

    # Instantiate dataloaders.
    A			:	Tuple										=       DataLoader(
        tokenized_datasets["""train"""]				,					shuffle=_lowerCAmelCase				,					collate_fn=_lowerCAmelCase				,					batch_size=_lowerCAmelCase		)
    A			:	Optional[int]										=       DataLoader(
        tokenized_datasets["""validation"""]				,					shuffle=_lowerCAmelCase				,					collate_fn=_lowerCAmelCase				,					batch_size=_lowerCAmelCase		)

    return train_dataloader, eval_dataloader




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  int:



    """simple docstring"""






    A			:	Dict										=       Accelerator()

    # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
    A			:	Tuple										=       config["""lr"""]
    A			:	Any										=       int(config["""num_epochs"""]		)
    A			:	List[str]										=       int(config["""seed"""]		)
    A			:	Union[str, Any]										=       int(config["""batch_size"""]		)
    A			:	int										=       args.model_name_or_path

    set_seed(_lowerCAmelCase		)
    A					,							A			:	Any										=       get_dataloaders(_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					args.n_train				,					args.n_val		)

    # Instantiate the model (we build the model here so that the seed also control new weights initialization)
    A			:	Optional[Any]										=       AutoModelForSequenceClassification.from_pretrained(_lowerCAmelCase				,					return_dict=_lowerCAmelCase		)

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

    if accelerator.state.deepspeed_plugin is not None:
        A			:	Dict										=       accelerator.state.deepspeed_plugin.deepspeed_config[
            """gradient_accumulation_steps"""
        ]
    else:
        A			:	str										=       1
    A			:	Any										=       (len(_lowerCAmelCase		) * 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			:	Union[str, Any]										=       get_linear_schedule_with_warmup(
            optimizer=_lowerCAmelCase				,					num_warmup_steps=0				,					num_training_steps=_lowerCAmelCase				,					)
    else:
        A			:	str										=       DummyScheduler(_lowerCAmelCase				,					total_num_steps=_lowerCAmelCase				,					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			:	int										=       accelerator.prepare(
        _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)

    # We need to keep track of how many total steps we have iterated over
    A			:	Optional[int]										=       0
    # We also need to keep track of the stating epoch so files are named properly
    A			:	List[Any]										=       0

    # Now we train the model
    A			:	Optional[Any]										=       {}
    for epoch in range(_lowerCAmelCase				,					_lowerCAmelCase		):
        with TorchTracemalloc() as tracemalloc:
            model.train()
            for step, batch in enumerate(_lowerCAmelCase		):
                A			:	int										=       model(**_lowerCAmelCase		)
                A			:	str										=       outputs.loss
                A			:	Tuple										=       loss / gradient_accumulation_steps
                accelerator.backward(_lowerCAmelCase		)
                if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

                overall_step += 1

        # Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
        accelerator.print("""Memory before entering the train : {}""".format(bamb(tracemalloc.begin		)		)		)
        accelerator.print("""Memory consumed at the end of the train (end-begin): {}""".format(tracemalloc.used		)		)
        accelerator.print("""Peak Memory consumed during the train (max-begin): {}""".format(tracemalloc.peaked		)		)
        accelerator.print(
            """Total Peak Memory consumed during the train (max): {}""".format(
                tracemalloc.peaked + bamb(tracemalloc.begin		)		)		)
        A			:	str										=       tracemalloc.peaked + bamb(tracemalloc.begin		)
        if args.peak_memory_upper_bound is not None:
            assert (
                train_total_peak_memory[f'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
            ), "Peak memory usage exceeded the upper bound"

    accelerator.wait_for_everyone()
    if accelerator.is_main_process:
        with open(os.path.join(args.output_dir				,					"""peak_memory_utilization.json"""		)				,					"""w"""		) as f:
            json.dump(_lowerCAmelCase				,					_lowerCAmelCase		)




def      __UpperCamelCase     (  )	->  Optional[int]:



    """simple docstring"""






    A			:	Dict										=       argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage."""		)
    parser.add_argument(
        """--model_name_or_path"""				,					type=_lowerCAmelCase				,					default="""bert-base-cased"""				,					help="""Path to pretrained model or model identifier from huggingface.co/models."""				,					required=_lowerCAmelCase				,					)
    parser.add_argument(
        """--output_dir"""				,					type=_lowerCAmelCase				,					default="""."""				,					help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory."""				,					)
    parser.add_argument(
        """--peak_memory_upper_bound"""				,					type=_lowerCAmelCase				,					default=_lowerCAmelCase				,					help="""The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value."""				,					)
    parser.add_argument(
        """--n_train"""				,					type=_lowerCAmelCase				,					default=320				,					help="""Number of training examples to use."""				,					)
    parser.add_argument(
        """--n_val"""				,					type=_lowerCAmelCase				,					default=160				,					help="""Number of validation examples to use."""				,					)
    parser.add_argument(
        """--num_epochs"""				,					type=_lowerCAmelCase				,					default=1				,					help="""Number of train epochs."""				,					)
    A			:	List[Any]										=       parser.parse_args()
    A			:	Tuple										=       {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
    training_function(_lowerCAmelCase				,					_lowerCAmelCase		)


if __name__ == "__main__":
     main()







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
     from PIL import Image

     from transformers import OwlViTImageProcessor, OwlViTProcessor






@require_vision
class SCREAMING_SNAKE_CASE__					(		unittest.TestCase   ):




    '''simple docstring'''






    def       _lowerCAmelCase (	self		):
        A			:	str										=       tempfile.mkdtemp()

        # fmt: off
        A			:	List[Any]										=       ["""""", """l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
        # fmt: on
        A			:	Optional[int]										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[Any]										=       ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
        A			:	Union[str, Any]										=       {"""unk_token""": """<unk>"""}

        A			:	List[Any]										=       os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES["""vocab_file"""]		)
        A			:	int										=       os.path.join(self.tmpdirname,		VOCAB_FILES_NAMES["""merges_file"""]		)
        with open(self.vocab_file,		"""w""",		encoding="""utf-8"""		) as fp:
            fp.write(json.dumps(lowerCamelCase__		) + """\n"""		)
        with open(self.merges_file,		"""w""",		encoding="""utf-8"""		) as fp:
            fp.write("""\n""".join(lowerCamelCase__		)		)

        A			:	int										=       {
            """do_resize""": True,
            """size""": 20,
            """do_center_crop""": True,
            """crop_size""": 18,
            """do_normalize""": True,
            """image_mean""": [0.4814_5466, 0.457_8275, 0.4082_1073],
            """image_std""": [0.2686_2954, 0.2613_0258, 0.2757_7711],
        }
        A			:	List[Any]										=       os.path.join(self.tmpdirname,		lowerCamelCase__		)
        with open(self.image_processor_file,		"""w""",		encoding="""utf-8"""		) as fp:
            json.dump(lowerCamelCase__,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizer.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname,		pad_token="""!""",		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		**lowerCamelCase__		):
        return OwlViTImageProcessor.from_pretrained(self.tmpdirname,		**lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        shutil.rmtree(self.tmpdirname		)






    def       _lowerCAmelCase (	self		):
        A			:	str										=       [np.random.randint(255,		size=(3, 30, 400),		dtype=np.uinta		)]

        A			:	Optional[int]										=       [Image.fromarray(np.moveaxis(lowerCamelCase__,		0,		-1		)		) for x in image_inputs]

        return image_inputs






    def       _lowerCAmelCase (	self		):
        A			:	Optional[Any]										=       self.get_tokenizer()
        A			:	Optional[Any]										=       self.get_rust_tokenizer()
        A			:	Optional[int]										=       self.get_image_processor()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_slow.save_pretrained(self.tmpdirname		)
        A			:	Optional[int]										=       OwlViTProcessor.from_pretrained(self.tmpdirname,		use_fast=lowerCamelCase__		)

        A			:	Tuple										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)
        processor_fast.save_pretrained(self.tmpdirname		)
        A			:	str										=       OwlViTProcessor.from_pretrained(self.tmpdirname		)

        self.assertEqual(processor_slow.tokenizer.get_vocab(),		tokenizer_slow.get_vocab()		)
        self.assertEqual(processor_fast.tokenizer.get_vocab(),		tokenizer_fast.get_vocab()		)
        self.assertEqual(tokenizer_slow.get_vocab(),		tokenizer_fast.get_vocab()		)
        self.assertIsInstance(processor_slow.tokenizer,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.tokenizer,		lowerCamelCase__		)

        self.assertEqual(processor_slow.image_processor.to_json_string(),		image_processor.to_json_string()		)
        self.assertEqual(processor_fast.image_processor.to_json_string(),		image_processor.to_json_string()		)
        self.assertIsInstance(processor_slow.image_processor,		lowerCamelCase__		)
        self.assertIsInstance(processor_fast.image_processor,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       OwlViTProcessor(tokenizer=self.get_tokenizer(),		image_processor=self.get_image_processor()		)
        processor.save_pretrained(self.tmpdirname		)

        A			:	Optional[int]										=       self.get_tokenizer(bos_token="""(BOS)""",		eos_token="""(EOS)"""		)
        A			:	Tuple										=       self.get_image_processor(do_normalize=lowerCamelCase__		)

        A			:	Optional[Any]										=       OwlViTProcessor.from_pretrained(
            self.tmpdirname,		bos_token="""(BOS)""",		eos_token="""(EOS)""",		do_normalize=lowerCamelCase__		)

        self.assertEqual(processor.tokenizer.get_vocab(),		tokenizer_add_kwargs.get_vocab()		)
        self.assertIsInstance(processor.tokenizer,		lowerCamelCase__		)

        self.assertEqual(processor.image_processor.to_json_string(),		image_processor_add_kwargs.to_json_string()		)
        self.assertIsInstance(processor.image_processor,		lowerCamelCase__		)






    def       _lowerCAmelCase (	self		):
        A			:	List[Any]										=       self.get_image_processor()
        A			:	str										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	Optional[Any]										=       image_processor(lowerCamelCase__,		return_tensors="""np"""		)
        A			:	Any										=       processor(images=lowerCamelCase__,		return_tensors="""np"""		)

        for key in input_image_proc.keys():
            self.assertAlmostEqual(input_image_proc[key].sum(),		input_processor[key].sum(),		delta=1e-2		)






    def       _lowerCAmelCase (	self		):
        A			:	int										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	Optional[int]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       """lower newer"""

        A			:	Union[str, Any]										=       processor(text=lowerCamelCase__,		return_tensors="""np"""		)

        A			:	str										=       tokenizer(lowerCamelCase__,		return_tensors="""np"""		)

        for key in encoded_tok.keys():
            self.assertListEqual(encoded_tok[key][0].tolist(),		encoded_processor[key][0].tolist()		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	int										=       self.get_tokenizer()

        A			:	str										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	List[str]										=       """lower newer"""
        A			:	Any										=       self.prepare_image_inputs()

        A			:	Tuple										=       processor(text=lowerCamelCase__,		images=lowerCamelCase__		)

        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask""", """pixel_values"""]		)

        # test if it raises when no input is passed
        with pytest.raises(lowerCamelCase__		):
            processor()






    def       _lowerCAmelCase (	self		):
        A			:	str										=       """google/owlvit-base-patch32"""
        A			:	Dict										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[int]										=       processor(text=lowerCamelCase__		)

        A			:	Any										=       16
        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(2, seq_length)		)

        # test if it raises when no input is passed
        with pytest.raises(lowerCamelCase__		):
            processor()






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       """google/owlvit-base-patch32"""
        A			:	Any										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	int										=       [["""cat""", """nasa badge"""], ["""person"""]]
        A			:	List[Any]										=       processor(text=lowerCamelCase__		)

        A			:	Dict										=       16
        A			:	List[str]										=       len(lowerCamelCase__		)
        A			:	List[str]										=       max([len(lowerCamelCase__		) for texts in input_texts]		)

        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(batch_size * num_max_text_queries, seq_length)		)

        # test if it raises when no input is passed
        with pytest.raises(lowerCamelCase__		):
            processor()






    def       _lowerCAmelCase (	self		):
        A			:	Dict										=       """google/owlvit-base-patch32"""
        A			:	int										=       OwlViTProcessor.from_pretrained(lowerCamelCase__		)

        A			:	str										=       ["""cat""", """nasa badge"""]
        A			:	Optional[Any]										=       processor(text=lowerCamelCase__		)

        A			:	int										=       16
        A			:	Optional[Any]										=       inputs["""input_ids"""]
        A			:	Optional[int]										=       [
            [4_9406, 2368, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
            [4_9406, 6841, 1_1301, 4_9407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
        ]

        self.assertListEqual(list(inputs.keys()		),		["""input_ids""", """attention_mask"""]		)
        self.assertEqual(inputs["""input_ids"""].shape,		(2, seq_length)		)
        self.assertListEqual(list(input_ids[0]		),		predicted_ids[0]		)
        self.assertListEqual(list(input_ids[1]		),		predicted_ids[1]		)






    def       _lowerCAmelCase (	self		):
        A			:	Tuple										=       self.get_image_processor()
        A			:	Dict										=       self.get_tokenizer()

        A			:	Optional[Any]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       self.prepare_image_inputs()
        A			:	Optional[Any]										=       self.prepare_image_inputs()

        A			:	List[str]										=       processor(images=lowerCamelCase__,		query_images=lowerCamelCase__		)

        self.assertListEqual(list(inputs.keys()		),		["""query_pixel_values""", """pixel_values"""]		)

        # test if it raises when no input is passed
        with pytest.raises(lowerCamelCase__		):
            processor()






    def       _lowerCAmelCase (	self		):
        A			:	Any										=       self.get_image_processor()
        A			:	Optional[Any]										=       self.get_tokenizer()

        A			:	List[str]										=       OwlViTProcessor(tokenizer=lowerCamelCase__,		image_processor=lowerCamelCase__		)

        A			:	Any										=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A			:	Optional[Any]										=       processor.batch_decode(lowerCamelCase__		)
        A			:	Union[str, Any]										=       tokenizer.batch_decode(lowerCamelCase__		)

        self.assertListEqual(lowerCamelCase__,		lowerCamelCase__		)


