





'''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


__lowerCAmelCase							:     Optional[int]		     =16
__lowerCAmelCase							:     List[str]		     =32





def 						UpperCamelCase    (							_lowerCamelCase : Accelerator						,	_lowerCamelCase : int = 16						,	_lowerCamelCase : str = "bert-base-cased"	):
    A__    =      AutoTokenizer.from_pretrained(_lowerCamelCase	)
    A__    =      load_dataset("glue"						,	"mrpc"	)

    def tokenize_function(_lowerCamelCase : int	):
        # 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
    A__    =      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__    =      tokenized_datasets.rename_column("label"						,	"labels"	)

    def collate_fn(_lowerCamelCase : 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(_lowerCamelCase						,	padding="max_length"						,	max_length=1_28						,	return_tensors="pt"	)
        return tokenizer.pad(_lowerCamelCase						,	padding="longest"						,	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





def 						UpperCamelCase    (							_lowerCamelCase : int						,	_lowerCamelCase : Dict						,	_lowerCamelCase : Any						,	_lowerCamelCase : Any	):
    model.eval()
    A__    =      0
    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	)
        # 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(_lowerCamelCase	) - 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=_lowerCamelCase						,	references=_lowerCamelCase						,	)

    A__    =      metric.compute()
    return eval_metric["accuracy"]





def 						UpperCamelCase    (							_lowerCamelCase : Any						,	_lowerCamelCase : Optional[int]	):
    # 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(_lowerCamelCase	)
    A__,     A__    =      get_dataloaders(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

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

    # 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=_lowerCamelCase	)

    if accelerator.state.deepspeed_plugin is not None:
        A__    =      accelerator.state.deepspeed_plugin.deepspeed_config[
            "gradient_accumulation_steps"
        ]
    else:
        A__    =      1
    A__    =      (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__    =      get_linear_schedule_with_warmup(
            optimizer=_lowerCamelCase						,	num_warmup_steps=0						,	num_training_steps=_lowerCamelCase						,	)
    else:
        A__    =      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__    =      accelerator.prepare(
        _lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)

    # 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
    A__    =      evaluate.load("glue"						,	"mrpc"	)
    A__    =      num_epochs

    if args.partial_train_epoch is not None:
        A__    =      args.partial_train_epoch

    if args.resume_from_checkpoint:
        accelerator.load_state(args.resume_from_checkpoint	)
        A__    =      args.resume_from_checkpoint.split("epoch_"	)[1]
        A__    =      ""
        for char in epoch_string:
            if char.isdigit():
                state_epoch_num += char
            else:
                break
        A__    =      int(_lowerCamelCase	) + 1
        A__    =      evaluation_loop(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
        accelerator.print("resumed checkpoint performance:"						,	_lowerCamelCase	)
        accelerator.print("resumed checkpoint's scheduler's lr:"						,	lr_scheduler.get_lr()[0]	)
        accelerator.print("resumed optimizers's lr:"						,	optimizer.param_groups[0]["lr"]	)
        with open(os.path.join(args.output_dir						,	F"state_{starting_epoch-1}.json"	)						,	"r"	) as f:
            A__    =      json.load(_lowerCamelCase	)
            assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
            assert (
                resumed_state["lr"] == lr_scheduler.get_lr()[0]
            ), "Scheduler learning rate mismatch, loading from checkpoint failed"
            assert (
                resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
            ), "Optimizer learning rate mismatch, loading from checkpoint failed"
            assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
            return

    # Now we train the model
    A__    =      {}
    for epoch in range(_lowerCamelCase						,	_lowerCamelCase	):
        model.train()
        for step, batch in enumerate(_lowerCamelCase	):
            A__    =      model(**_lowerCamelCase	)
            A__    =      outputs.loss
            A__    =      loss / gradient_accumulation_steps
            accelerator.backward(_lowerCamelCase	)
            if step % gradient_accumulation_steps == 0:
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            overall_step += 1
        A__    =      F"epoch_{epoch}"
        A__    =      os.path.join(args.output_dir						,	_lowerCamelCase	)
        accelerator.save_state(_lowerCamelCase	)
        A__    =      evaluation_loop(_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase						,	_lowerCamelCase	)
        A__    =      accuracy
        A__    =      lr_scheduler.get_lr()[0]
        A__    =      optimizer.param_groups[0]["lr"]
        A__    =      epoch
        A__    =      overall_step
        accelerator.print(F"epoch {epoch}:"						,	_lowerCamelCase	)

        accelerator.wait_for_everyone()
        if accelerator.is_main_process:
            with open(os.path.join(args.output_dir						,	F"state_{epoch}.json"	)						,	"w"	) as f:
                json.dump(_lowerCamelCase						,	_lowerCamelCase	)





def 						UpperCamelCase    (							):
    A__    =      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(
        "--resume_from_checkpoint"						,	type=_lowerCamelCase						,	default=_lowerCamelCase						,	help="If the training should continue from a checkpoint folder."						,	)
    parser.add_argument(
        "--partial_train_epoch"						,	type=_lowerCamelCase						,	default=_lowerCamelCase						,	help="If passed, the training will stop after this number of epochs."						,	)
    parser.add_argument(
        "--num_epochs"						,	type=_lowerCamelCase						,	default=2						,	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(_lowerCamelCase						,	_lowerCamelCase	)


if __name__ == "__main__":
  main()










'''simple docstring'''

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch
  from torch import nn

  from transformers import (
      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
      TimesformerForVideoClassification,
      TimesformerModel,
  )
  from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from transformers import VideoMAEImageProcessor






class        UpperCAmelCase     :
 def __init__(    self				:Optional[Any]						,	lowercase_				:int						,	lowercase_				:Union[str, Any]=13						,	lowercase_				:Union[str, Any]=10						,	lowercase_				:Any=3						,	lowercase_				:Tuple=2						,	lowercase_				:List[Any]=2						,	lowercase_				:int=True						,	lowercase_				:int=True						,	lowercase_				:List[str]=32						,	lowercase_				:Dict=5						,	lowercase_				:List[Any]=4						,	lowercase_				:List[Any]=37						,	lowercase_				:List[Any]="gelu"						,	lowercase_				:int=0.1						,	lowercase_				:List[Any]=0.1						,	lowercase_				:List[Any]=10						,	lowercase_				:int=0.0_2						,	lowercase_				:Union[str, Any]="divided_space_time"						,	lowercase_				:Tuple=None						,	)->							Tuple:
     A__    =      parent
     A__    =      batch_size
     A__    =      image_size
     A__    =      num_channels
     A__    =      patch_size
     A__    =      num_frames
     A__    =      is_training
     A__    =      use_labels
     A__    =      hidden_size
     A__    =      num_hidden_layers
     A__    =      num_attention_heads
     A__    =      intermediate_size
     A__    =      hidden_act
     A__    =      hidden_dropout_prob
     A__    =      attention_probs_dropout_prob
     A__    =      attention_type
     A__    =      initializer_range
     A__    =      scope
     A__    =      num_labels

     # in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
     A__    =      (image_size // patch_size) ** 2
     A__    =      (num_frames) * self.num_patches_per_frame + 1
 def 					UpperCAmelCase_							(    self				:str							)->							str:
     A__    =      floats_tensor(
         [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

     A__    =      None
     if self.use_labels:
         A__    =      ids_tensor([self.batch_size]						,	self.num_labels							)

     A__    =      self.get_config()

     return config, pixel_values, labels
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerConfig(
         image_size=self.image_size						,	patch_size=self.patch_size						,	num_channels=self.num_channels						,	num_frames=self.num_frames						,	hidden_size=self.hidden_size						,	num_hidden_layers=self.num_hidden_layers						,	num_attention_heads=self.num_attention_heads						,	intermediate_size=self.intermediate_size						,	hidden_act=self.hidden_act						,	hidden_dropout_prob=self.hidden_dropout_prob						,	attention_probs_dropout_prob=self.attention_probs_dropout_prob						,	initializer_range=self.initializer_range						,	attention_type=self.attention_type						,	)
     A__    =      self.num_labels
     return config
 def 					UpperCAmelCase_							(    self				:Optional[Any]						,	lowercase_				:List[str]						,	lowercase_				:List[Any]						,	lowercase_				:Tuple							)->							Optional[int]:
     A__    =      TimesformerModel(config=lowercase_							)
     model.to(lowercase_							)
     model.eval()
     A__    =      model(lowercase_							)
     self.parent.assertEqual(result.last_hidden_state.shape						,	(self.batch_size, self.seq_length, self.hidden_size)							)
 def 					UpperCAmelCase_							(    self				:List[str]						,	lowercase_				:Tuple						,	lowercase_				:Tuple						,	lowercase_				:Dict							)->							Tuple:
     A__    =      TimesformerForVideoClassification(lowercase_							)
     model.to(lowercase_							)
     model.eval()

     A__    =      model(lowercase_							)

     # verify the logits shape
     A__    =      torch.Size((self.batch_size, self.num_labels)							)
     self.parent.assertEqual(result.logits.shape						,	lowercase_							)



 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							str:
     A__    =      self.prepare_config_and_inputs()
     A__,     A__,     A__    =      config_and_inputs
     A__    =      {"pixel_values": pixel_values}
     return config, inputs_dict






@require_torch
class        UpperCAmelCase     (	UpperCamelCase__      ,	UpperCamelCase__      ,	unittest.TestCase    ):
 __lowercase       		=    (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
 __lowercase       		=    (
     {"""feature-extraction""": TimesformerModel, """video-classification""": TimesformerForVideoClassification}
     if is_torch_available()
     else {}
 )

 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 __lowercase       		=    False
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Optional[int]:
     A__    =      TimesformerModelTester(self							)
     A__    =      ConfigTester(
         self						,	config_class=lowercase_						,	has_text_modality=lowercase_						,	hidden_size=37							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]						,	lowercase_				:int						,	lowercase_				:Dict						,	lowercase_				:int=False							)->							str:
     A__    =      copy.deepcopy(lowercase_							)

     if return_labels:
         if model_class in get_values(lowercase_							):
             A__    =      torch.zeros(
                 self.model_tester.batch_size						,	dtype=torch.long						,	device=lowercase_							)

     return inputs_dict
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							List[Any]:
     self.config_tester.run_common_tests()
 @unittest.skip(reason="TimeSformer does not use inputs_embeds"							)
 def 					UpperCAmelCase_							(    self				:List[Any]							)->							Tuple:
     pass
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[Any]:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         self.assertIsInstance(model.get_input_embeddings()						,	(nn.Module)							)
         A__    =      model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(lowercase_						,	nn.Linear							)							)
 def 					UpperCAmelCase_							(    self				:Union[str, Any]							)->							Dict:
     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      model_class(lowercase_							)
         A__    =      inspect.signature(model.forward							)
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         A__    =      [*signature.parameters.keys()]

         A__    =      ["pixel_values"]
         self.assertListEqual(arg_names[:1]						,	lowercase_							)
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_							)
 def 					UpperCAmelCase_							(    self				:Dict							)->							Optional[int]:
     A__    =      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_video_classification(*lowercase_							)
 @slow
 def 					UpperCAmelCase_							(    self				:Any							)->							List[Any]:
     for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         A__    =      TimesformerModel.from_pretrained(lowercase_							)
         self.assertIsNotNone(lowercase_							)
 def 					UpperCAmelCase_							(    self				:List[str]							)->							str:
     if not self.has_attentions:
         pass

     else:
         A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()
         A__    =      True

         for model_class in self.all_model_classes:
             A__    =      self.model_tester.seq_length
             A__    =      self.model_tester.num_frames

             A__    =      True
             A__    =      False
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)
             A__    =      outputs.attentions
             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)
             A__    =      outputs.attentions
             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)
             A__    =      len(lowercase_							)

             # Check attention is always last and order is fine
             A__    =      True
             A__    =      True
             A__    =      model_class(lowercase_							)
             model.to(lowercase_							)
             model.eval()
             with torch.no_grad():
                 A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

             self.assertEqual(out_len + 1						,	len(lowercase_							)							)

             A__    =      outputs.attentions

             self.assertEqual(len(lowercase_							)						,	self.model_tester.num_hidden_layers							)

             # attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]							)						,	[self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1]						,	)



 def 					UpperCAmelCase_							(    self				:List[Any]							)->							List[str]:
     def check_hidden_states_output(lowercase_				:Dict						,	lowercase_				:int						,	lowercase_				:List[Any]							):
         A__    =      model_class(lowercase_							)
         model.to(lowercase_							)
         model.eval()

         with torch.no_grad():
             A__    =      model(**self._prepare_for_class(lowercase_						,	lowercase_							)							)

         A__    =      outputs.hidden_states
         A__    =      self.model_tester.num_hidden_layers + 1
         self.assertEqual(len(lowercase_							)						,	lowercase_							)

         A__    =      self.model_tester.seq_length

         self.assertListEqual(
             list(hidden_states[0].shape[-2:]							)						,	[seq_length, self.model_tester.hidden_size]						,	)

     A__,     A__    =      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         A__    =      True
         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         A__    =      True

         check_hidden_states_output(lowercase_						,	lowercase_						,	lowercase_							)





def 						UpperCamelCase    (							):
    A__    =      hf_hub_download(
        repo_id="hf-internal-testing/spaghetti-video"						,	filename="eating_spaghetti.npy"						,	repo_type="dataset"	)
    A__    =      np.load(_lowerCamelCase	)
    return list(_lowerCamelCase	)






@require_torch
@require_vision
class        UpperCAmelCase     (	unittest.TestCase    ):
 @cached_property
 def 					UpperCAmelCase_							(    self				:Optional[Any]							)->							int:
     # logits were tested with a different mean and std, so we use the same here
     return (
         VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]						,	image_std=[0.5, 0.5, 0.5]							)
         if is_vision_available()
         else None
     )



 @slow
 def 					UpperCAmelCase_							(    self				:int							)->							Any:
     A__    =      TimesformerForVideoClassification.from_pretrained("facebook/timesformer-base-finetuned-k400"							).to(
         lowercase_							)

     A__    =      self.default_image_processor
     A__    =      prepare_video()
     A__    =      image_processor(video[:8]						,	return_tensors="pt"							).to(lowercase_							)

     # forward pass
     with torch.no_grad():
         A__    =      model(**lowercase_							)

     # verify the logits
     A__    =      torch.Size((1, 4_00)							)
     self.assertEqual(outputs.logits.shape						,	lowercase_							)

     A__    =      torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5]							).to(lowercase_							)

     self.assertTrue(torch.allclose(outputs.logits[0, :3]						,	lowercase_						,	atol=1E-4							)							)



