import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      XCLIPTextConfig()

     # derive patch size from model name
     UpperCamelCase__										=      model_name.find('''patch'''							)
     UpperCamelCase__										=      int(model_name[start_idx + len('''patch'''							) : start_idx + len('''patch'''							) + 2]							)
     UpperCamelCase__										=      XCLIPVisionConfig(patch_size=A ,							num_frames=A							)

     if "large" in model_name:
          UpperCamelCase__										=      768
          UpperCamelCase__										=      3072
          UpperCamelCase__										=      12

          UpperCamelCase__										=      1024
          UpperCamelCase__										=      4096
          UpperCamelCase__										=      16
          UpperCamelCase__										=      24
          UpperCamelCase__										=      768
          UpperCamelCase__										=      3072

     if model_name == "xclip-large-patch14-16-frames":
          UpperCamelCase__										=      336

     UpperCamelCase__										=      XCLIPConfig.from_text_vision_configs(A ,							A							)

     if "large" in model_name:
          UpperCamelCase__										=      768

     return config







def  __UpperCamelCase    (						A							):
     # text encoder
     if name == "token_embedding.weight":
          UpperCamelCase__										=      name.replace('''token_embedding.weight''' ,							'''text_model.embeddings.token_embedding.weight'''							)
     if name == "positional_embedding":
          UpperCamelCase__										=      name.replace('''positional_embedding''' ,							'''text_model.embeddings.position_embedding.weight'''							)
     if "ln_1" in name:
          UpperCamelCase__										=      name.replace('''ln_1''' ,							'''layer_norm1'''							)
     if "ln_2" in name:
          UpperCamelCase__										=      name.replace('''ln_2''' ,							'''layer_norm2'''							)
     if "c_fc" in name:
          UpperCamelCase__										=      name.replace('''c_fc''' ,							'''fc1'''							)
     if "c_proj" in name:
          UpperCamelCase__										=      name.replace('''c_proj''' ,							'''fc2'''							)
     if name.startswith('''transformer.resblocks'''							):
          UpperCamelCase__										=      name.replace('''transformer.resblocks''' ,							'''text_model.encoder.layers'''							)
     if "attn.out_proj" in name and "message" not in name:
          UpperCamelCase__										=      name.replace('''attn.out_proj''' ,							'''self_attn.out_proj'''							)
     if "ln_final" in name:
          UpperCamelCase__										=      name.replace('''ln_final''' ,							'''text_model.final_layer_norm'''							)
     # visual encoder
     if name == "visual.class_embedding":
          UpperCamelCase__										=      name.replace('''visual.class_embedding''' ,							'''vision_model.embeddings.class_embedding'''							)
     if name == "visual.positional_embedding":
          UpperCamelCase__										=      name.replace('''visual.positional_embedding''' ,							'''vision_model.embeddings.position_embedding.weight'''							)
     if name.startswith('''visual.transformer.resblocks'''							):
          UpperCamelCase__										=      name.replace('''visual.transformer.resblocks''' ,							'''vision_model.encoder.layers'''							)
     if "visual.conv1" in name:
          UpperCamelCase__										=      name.replace('''visual.conv1''' ,							'''vision_model.embeddings.patch_embedding'''							)
     if "visual.ln_pre" in name:
          UpperCamelCase__										=      name.replace('''visual.ln_pre''' ,							'''vision_model.pre_layernorm'''							)
     if "visual.ln_post" in name:
          UpperCamelCase__										=      name.replace('''visual.ln_post''' ,							'''vision_model.post_layernorm'''							)
     if "visual.proj" in name:
          UpperCamelCase__										=      name.replace('''visual.proj''' ,							'''visual_projection.weight'''							)
     if "text_projection" in name:
          UpperCamelCase__										=      name.replace('''text_projection''' ,							'''text_projection.weight'''							)
     # things on top
     if "prompts_visual_proj" in name:
          UpperCamelCase__										=      name.replace('''prompts_visual_proj''' ,							'''prompts_visual_projection'''							)
     if "prompts_visual_ln" in name:
          UpperCamelCase__										=      name.replace('''prompts_visual_ln''' ,							'''prompts_visual_layernorm'''							)
     # mit
     if name == "mit.positional_embedding":
          UpperCamelCase__										=      name.replace('''positional''' ,							'''position'''							)
     if name.startswith('''mit.resblocks'''							):
          UpperCamelCase__										=      name.replace('''mit.resblocks''' ,							'''mit.encoder.layers'''							)
     # prompts generator
     if name.startswith('''prompts_generator.norm'''							):
          UpperCamelCase__										=      name.replace('''prompts_generator.norm''' ,							'''prompts_generator.layernorm'''							)

     return name







def  __UpperCamelCase    (						A ,							A							):
     for key in orig_state_dict.copy().keys():
          UpperCamelCase__										=      orig_state_dict.pop(A							)

          if "attn.in_proj" in key:
               UpperCamelCase__										=      key.split('''.'''							)
               if key.startswith('''visual'''							):
                    UpperCamelCase__										=      key_split[3]
                    UpperCamelCase__										=      config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              UpperCamelCase__										=      val[
                                  :dim, :
                              ]
                              UpperCamelCase__										=      val[
                                  dim : dim * 2, :
                              ]
                              UpperCamelCase__										=      val[
                                  -dim:, :
                              ]
                         else:
                              UpperCamelCase__										=      val[
                                  :dim
                              ]
                              UpperCamelCase__										=      val[
                                  dim : dim * 2
                              ]
                              UpperCamelCase__										=      val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              UpperCamelCase__										=      val[
                                  :dim, :
                              ]
                              UpperCamelCase__										=      val[
                                  dim : dim * 2, :
                              ]
                              UpperCamelCase__										=      val[
                                  -dim:, :
                              ]
                         else:
                              UpperCamelCase__										=      val[:dim]
                              UpperCamelCase__										=      val[
                                  dim : dim * 2
                              ]
                              UpperCamelCase__										=      val[-dim:]
               elif key.startswith('''mit'''							):
                    UpperCamelCase__										=      key_split[2]
                    UpperCamelCase__										=      config.vision_config.mit_hidden_size
                    if "weight" in key:
                         UpperCamelCase__										=      val[:dim, :]
                         UpperCamelCase__										=      val[dim : dim * 2, :]
                         UpperCamelCase__										=      val[-dim:, :]
                    else:
                         UpperCamelCase__										=      val[:dim]
                         UpperCamelCase__										=      val[dim : dim * 2]
                         UpperCamelCase__										=      val[-dim:]
               else:
                    UpperCamelCase__										=      key_split[2]
                    UpperCamelCase__										=      config.text_config.hidden_size
                    if "weight" in key:
                         UpperCamelCase__										=      val[:dim, :]
                         UpperCamelCase__										=      val[
                             dim : dim * 2, :
                         ]
                         UpperCamelCase__										=      val[-dim:, :]
                    else:
                         UpperCamelCase__										=      val[:dim]
                         UpperCamelCase__										=      val[
                             dim : dim * 2
                         ]
                         UpperCamelCase__										=      val[-dim:]
          else:
               UpperCamelCase__										=      rename_key(A							)
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    UpperCamelCase__										=      val.T
               UpperCamelCase__										=      val

     return orig_state_dict







def  __UpperCamelCase    (						A							):
     if num_frames == 8:
          UpperCamelCase__										=      '''eating_spaghetti_8_frames.npy'''
     elif num_frames == 16:
          UpperCamelCase__										=      '''eating_spaghetti.npy'''
     elif num_frames == 32:
          UpperCamelCase__										=      '''eating_spaghetti_32_frames.npy'''
     UpperCamelCase__										=      hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video''' ,							filename=A ,							repo_type='''dataset''' ,							)
     UpperCamelCase__										=      np.load(A							)
     return list(A							)







def  __UpperCamelCase    (						A ,							A=None ,							A=False							):
     UpperCamelCase__										=      {
         # fully supervised kinetics-400 checkpoints
         '''xclip-base-patch32''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth''',
         '''xclip-base-patch32-16-frames''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth'''
         ),
         '''xclip-base-patch16''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth''',
         '''xclip-base-patch16-16-frames''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth'''
         ),
         '''xclip-large-patch14''': '''https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb''',
         '''xclip-large-patch14-16-frames''': '''https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f''',
         # fully supervised kinetics-600 checkpoints
         '''xclip-base-patch16-kinetics-600''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth'''
         ),
         '''xclip-base-patch16-kinetics-600-16-frames''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth'''
         ),
         '''xclip-large-patch14-kinetics-600''': '''https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be''',
         # few shot
         '''xclip-base-patch16-hmdb-2-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth'''
         ),
         '''xclip-base-patch16-hmdb-4-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth'''
         ),
         '''xclip-base-patch16-hmdb-8-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth'''
         ),
         '''xclip-base-patch16-hmdb-16-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth'''
         ),
         '''xclip-base-patch16-ucf-2-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth'''
         ),
         '''xclip-base-patch16-ucf-4-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth'''
         ),
         '''xclip-base-patch16-ucf-8-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth'''
         ),
         '''xclip-base-patch16-ucf-16-shot''': (
             '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth'''
         ),
         # zero shot
         '''xclip-base-patch16-zero-shot''': '''https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth''',
     }

     UpperCamelCase__										=      model_to_url[model_name]
     UpperCamelCase__										=      8
     if "16-frames" in model_name:
          UpperCamelCase__										=      16
     elif "shot" in model_name:
          UpperCamelCase__										=      32

     UpperCamelCase__										=      get_xclip_config(A ,							A							)
     UpperCamelCase__										=      XCLIPModel(A							)
     model.eval()

     if "drive" in checkpoint_url:
          UpperCamelCase__										=      '''pytorch_model.bin'''
          gdown.cached_download(A ,							A ,							quiet=A							)
          UpperCamelCase__										=      torch.load(A ,							map_location='''cpu'''							)['''model''']
     else:
          UpperCamelCase__										=      torch.hub.load_state_dict_from_url(A							)['''model''']

     UpperCamelCase__										=      convert_state_dict(A ,							A							)

     UpperCamelCase__										=      XCLIPModel(A							)
     UpperCamelCase__						,       UpperCamelCase__										=      model.load_state_dict(A ,							strict=A							)
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     UpperCamelCase__										=      336 if model_name == '''xclip-large-patch14-16-frames''' else 224
     UpperCamelCase__										=      VideoMAEImageProcessor(size=A							)
     UpperCamelCase__										=      CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''							)
     UpperCamelCase__										=      CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''							)
     UpperCamelCase__										=      XCLIPProcessor(image_processor=A ,							tokenizer=A							)

     UpperCamelCase__										=      prepare_video(A							)
     UpperCamelCase__										=      processor(
         text=['''playing sports''', '''eating spaghetti''', '''go shopping'''] ,							videos=A ,							return_tensors='''pt''' ,							padding=A							)

     print('''Shape of pixel values:''' ,							inputs.pixel_values.shape							)

     with torch.no_grad():
          UpperCamelCase__										=      model(**A							)

     # Verify outputs
     UpperCamelCase__										=      outputs.logits_per_video
     UpperCamelCase__										=      logits_per_video.softmax(dim=1							)
     print('''Probs:''' ,							A							)
     # kinetics-400
     if model_name == "xclip-base-patch32":
          UpperCamelCase__										=      torch.tensor([[0.00_19, 0.99_51, 0.00_30]]							)
     elif model_name == "xclip-base-patch32-16-frames":
          UpperCamelCase__										=      torch.tensor([[7.09_99e-04, 9.98_83e-01, 4.55_80e-04]]							)
     elif model_name == "xclip-base-patch16":
          UpperCamelCase__										=      torch.tensor([[0.00_83, 0.96_81, 0.02_36]]							)
     elif model_name == "xclip-base-patch16-16-frames":
          UpperCamelCase__										=      torch.tensor([[7.69_37e-04, 9.97_28e-01, 1.94_73e-03]]							)
     elif model_name == "xclip-large-patch14":
          UpperCamelCase__										=      torch.tensor([[0.00_62, 0.98_64, 0.00_75]]							)
     elif model_name == "xclip-large-patch14-16-frames":
          UpperCamelCase__										=      torch.tensor([[3.38_77e-04, 9.99_37e-01, 2.88_88e-04]]							)
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          UpperCamelCase__										=      torch.tensor([[0.05_55, 0.89_14, 0.05_31]]							)
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          UpperCamelCase__										=      torch.tensor([[3.85_54e-04, 9.99_29e-01, 3.27_54e-04]]							)
     elif model_name == "xclip-large-patch14-kinetics-600":
          UpperCamelCase__										=      torch.tensor([[0.00_36, 0.99_20, 0.00_45]]							)
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          UpperCamelCase__										=      torch.tensor([[7.18_90e-06, 9.99_94e-01, 5.65_59e-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          UpperCamelCase__										=      torch.tensor([[1.03_20e-05, 9.99_93e-01, 6.24_35e-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          UpperCamelCase__										=      torch.tensor([[4.13_77e-06, 9.99_90e-01, 9.83_86e-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          UpperCamelCase__										=      torch.tensor([[4.13_47e-05, 9.99_62e-01, 3.34_11e-04]]							)
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          UpperCamelCase__										=      torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]							)
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          UpperCamelCase__										=      torch.tensor([[8.58_57e-05, 9.99_28e-01, 6.32_91e-04]]							)
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          UpperCamelCase__										=      torch.tensor([[0.00_27, 0.99_04, 0.00_70]]							)
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          UpperCamelCase__										=      torch.tensor([[9.82_19e-04, 9.95_93e-01, 3.08_63e-03]]							)
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          UpperCamelCase__										=      torch.tensor([[3.50_82e-04, 9.97_85e-01, 1.79_66e-03]]							)
     else:
          raise ValueError(f"Model name {model_name} not supported"							)
     assert torch.allclose(A ,							A ,							atol=1e-3							)
     print('''Looks ok!'''							)

     if pytorch_dump_folder_path is not None:
          print(f"Saving model {model_name} to {pytorch_dump_folder_path}"							)
          model.save_pretrained(A							)

     if push_to_hub:
          print('''Pushing model, processor and slow tokenizer files to the hub...'''							)
          model.push_to_hub(A ,							organization='''nielsr'''							)
          processor.push_to_hub(A ,							organization='''nielsr'''							)
          slow_tokenizer.push_to_hub(A ,							organization='''nielsr'''							)


if __name__ == "__main__":
     __magic_name__								=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--model_name''',
         default='''xclip-base-patch32''',
         type=str,
         help='''Name of the model.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
     )
     parser.add_argument(
         '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
     )

     __magic_name__								=parser.parse_args()
     convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




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,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# 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
#
########################################################################


__magic_name__								=16
__magic_name__								=32







def  __UpperCamelCase    (						A ,							A = 16							):
     UpperCamelCase__										=      AutoTokenizer.from_pretrained('''bert-base-cased'''							)
     UpperCamelCase__										=      load_dataset('''glue''' ,							'''mrpc'''							)

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

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

          return tokenizer.pad(
              A ,							padding='''longest''' ,							max_length=A ,							pad_to_multiple_of=A ,							return_tensors='''pt''' ,							)

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

     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

     __magic_name__								=mocked_dataloaders  # noqa: F811







def  __UpperCamelCase    (						A ,							A							):
     # For testing only
     if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,							A							) == "1":
          UpperCamelCase__										=      2
     # Initialize accelerator
     UpperCamelCase__										=      Accelerator(cpu=args.cpu ,							mixed_precision=args.mixed_precision							)
     # 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__										=      evaluate.load('''glue''' ,							'''mrpc'''							)

     # If the batch size is too big we use gradient accumulation
     UpperCamelCase__										=      1
     if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
          UpperCamelCase__										=      batch_size // MAX_GPU_BATCH_SIZE
          UpperCamelCase__										=      MAX_GPU_BATCH_SIZE

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

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

     # Instantiate optimizer
     UpperCamelCase__										=      AdamW(params=model.parameters() ,							lr=A							)

     # Instantiate scheduler
     UpperCamelCase__										=      get_linear_schedule_with_warmup(
         optimizer=A ,							num_warmup_steps=100 ,							num_training_steps=(len(A							) * num_epochs) // gradient_accumulation_steps ,							)

     # 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(
         A ,							A ,							A ,							A ,							A							)

     # Now we train the model
     for epoch in range(A							):
          model.train()
          for step, batch in enumerate(A							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               UpperCamelCase__										=      model(**A							)
               UpperCamelCase__										=      outputs.loss
               UpperCamelCase__										=      loss / gradient_accumulation_steps
               accelerator.backward(A							)
               if step % gradient_accumulation_steps == 0:
                    optimizer.step()
                    lr_scheduler.step()
                    optimizer.zero_grad()

          model.eval()
          UpperCamelCase__										=      0
          for step, batch in enumerate(A							):
               # We could avoid this line since we set the accelerator with `device_placement=True`.
               batch.to(accelerator.device							)
               with torch.no_grad():
                    UpperCamelCase__										=      model(**A							)
               UpperCamelCase__										=      outputs.logits.argmax(dim=-1							)
               UpperCamelCase__						,       UpperCamelCase__										=      accelerator.gather((predictions, batch['''labels'''])							)
               # New Code #
               # First we check if it's a distributed system
               if accelerator.use_distributed:
                    # Then see if we're on the last batch of our eval dataloader
                    if step == len(A							) - 1:
                         # Last batch needs to be truncated on distributed systems as it contains additional samples
                         UpperCamelCase__										=      predictions[: len(eval_dataloader.dataset							) - samples_seen]
                         UpperCamelCase__										=      references[: len(eval_dataloader.dataset							) - samples_seen]
                    else:
                         # Otherwise we add the number of samples seen
                         samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
               metric.add_batch(
                   predictions=A ,							references=A ,							)

          UpperCamelCase__										=      metric.compute()
          # Use accelerator.print to print only on the main process.
          accelerator.print(f"epoch {epoch}:" ,							A							)







def  __UpperCamelCase    (						):
     UpperCamelCase__										=      argparse.ArgumentParser(description='''Simple example of training script.'''							)
     parser.add_argument(
         '''--mixed_precision''' ,							type=A ,							default=A ,							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.''' ,							)
     parser.add_argument('''--cpu''' ,							action='''store_true''' ,							help='''If passed, will train on the CPU.'''							)
     UpperCamelCase__										=      parser.parse_args()
     UpperCamelCase__										=      {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
     training_function(A ,							A							)


if __name__ == "__main__":
     main()



