import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = 3_8_4
       lowerCAmelCase__					     = 7
       if "tiny" in model_name:
              lowerCAmelCase__					     = 9_6
              lowerCAmelCase__					     = (2, 2, 6, 2)
              lowerCAmelCase__					     = (3, 6, 1_2, 2_4)
       elif "small" in model_name:
              lowerCAmelCase__					     = 9_6
              lowerCAmelCase__					     = (2, 2, 1_8, 2)
              lowerCAmelCase__					     = (3, 6, 1_2, 2_4)
       elif "base" in model_name:
              lowerCAmelCase__					     = 1_2_8
              lowerCAmelCase__					     = (2, 2, 1_8, 2)
              lowerCAmelCase__					     = (4, 8, 1_6, 3_2)
              lowerCAmelCase__					     = 1_2
              lowerCAmelCase__					     = 5_1_2
       elif "large" in model_name:
              lowerCAmelCase__					     = 1_9_2
              lowerCAmelCase__					     = (2, 2, 1_8, 2)
              lowerCAmelCase__					     = (6, 1_2, 2_4, 4_8)
              lowerCAmelCase__					     = 1_2
              lowerCAmelCase__					     = 7_6_8

       # set label information
       lowerCAmelCase__					     = 1_5_0
       lowerCAmelCase__					     = """huggingface/label-files"""
       lowerCAmelCase__					     = """ade20k-id2label.json"""
       lowerCAmelCase__					     = json.load(open(hf_hub_download(_a     ,  _a     ,  repo_type='dataset'     )     ,  'r'     )     )
       lowerCAmelCase__					     = {int(_a     ): v for k, v in idalabel.items()}
       lowerCAmelCase__					     = {v: k for k, v in idalabel.items()}

       lowerCAmelCase__					     = SwinConfig(
           embed_dim=_a     ,  depths=_a     ,  num_heads=_a     ,  window_size=_a     ,  out_features=['stage1', 'stage2', 'stage3', 'stage4']     ,  )
       lowerCAmelCase__					     = UperNetConfig(
           backbone_config=_a     ,  auxiliary_in_channels=_a     ,  num_labels=_a     ,  idalabel=_a     ,  labelaid=_a     ,  )

       return config






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = []

       # fmt: off
       # stem
       rename_keys.append(('backbone.patch_embed.projection.weight', 'backbone.embeddings.patch_embeddings.projection.weight')     )
       rename_keys.append(('backbone.patch_embed.projection.bias', 'backbone.embeddings.patch_embeddings.projection.bias')     )
       rename_keys.append(('backbone.patch_embed.norm.weight', 'backbone.embeddings.norm.weight')     )
       rename_keys.append(('backbone.patch_embed.norm.bias', 'backbone.embeddings.norm.bias')     )
       # stages
       for i in range(len(config.backbone_config.depths     )     ):
              for j in range(config.backbone_config.depths[i]     ):
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.norm2.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight""")     )
                     rename_keys.append((F"""backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias""", F"""backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias""")     )

              if i < 3:
                     rename_keys.append((F"""backbone.stages.{i}.downsample.reduction.weight""", F"""backbone.encoder.layers.{i}.downsample.reduction.weight""")     )
                     rename_keys.append((F"""backbone.stages.{i}.downsample.norm.weight""", F"""backbone.encoder.layers.{i}.downsample.norm.weight""")     )
                     rename_keys.append((F"""backbone.stages.{i}.downsample.norm.bias""", F"""backbone.encoder.layers.{i}.downsample.norm.bias""")     )
              rename_keys.append((F"""backbone.norm{i}.weight""", F"""backbone.hidden_states_norms.stage{i+1}.weight""")     )
              rename_keys.append((F"""backbone.norm{i}.bias""", F"""backbone.hidden_states_norms.stage{i+1}.bias""")     )

       # decode head
       rename_keys.extend(
           [
               ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
               ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
               ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
               ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
           ]     )
       # fmt: on

       return rename_keys






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = dct.pop(_a     )
       lowerCAmelCase__					     = val






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = [int(backbone_config.embed_dim * 2**i     ) for i in range(len(backbone_config.depths     )     )]
       for i in range(len(backbone_config.depths     )     ):
              lowerCAmelCase__					     = num_features[i]
              for j in range(backbone_config.depths[i]     ):
                     # fmt: off
                     # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
                     lowerCAmelCase__					     = state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight"""     )
                     lowerCAmelCase__					     = state_dict.pop(F"""backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias"""     )
                     # next, add query, keys and values (in that order) to the state dict
                     lowerCAmelCase__					     = in_proj_weight[:dim, :]
                     lowerCAmelCase__					     = in_proj_bias[: dim]
                     lowerCAmelCase__					     = in_proj_weight[
                         dim : dim * 2, :
                     ]
                     lowerCAmelCase__					     = in_proj_bias[
                         dim : dim * 2
                     ]
                     lowerCAmelCase__					     = in_proj_weight[
                         -dim :, :
                     ]
                     lowerCAmelCase__					     = in_proj_bias[-dim :]
                     # fmt: on






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = x.shape
       lowerCAmelCase__					     = x.reshape(_a     ,  4     ,  in_channel // 4     )
       lowerCAmelCase__					     = x[:, [0, 2, 1, 3], :].transpose(1     ,  2     ).reshape(_a     ,  _a     )
       return x






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = x.shape
       lowerCAmelCase__					     = x.reshape(_a     ,  in_channel // 4     ,  4     )
       lowerCAmelCase__					     = x[:, :, [0, 2, 1, 3]].transpose(1     ,  2     ).reshape(_a     ,  _a     )

       return x






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = x.shape[0]
       lowerCAmelCase__					     = x.reshape(4     ,  in_channel // 4     )
       lowerCAmelCase__					     = x[[0, 2, 1, 3], :].transpose(0     ,  1     ).reshape(_a     )
       return x






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = x.shape[0]
       lowerCAmelCase__					     = x.reshape(in_channel // 4     ,  4     )
       lowerCAmelCase__					     = x[:, [0, 2, 1, 3]].transpose(0     ,  1     ).reshape(_a     )
       return x






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = {
           """upernet-swin-tiny""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth""",
           """upernet-swin-small""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth""",
           """upernet-swin-base""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth""",
           """upernet-swin-large""": """https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth""",
       }
       lowerCAmelCase__					     = model_name_to_url[model_name]
       lowerCAmelCase__					     = torch.hub.load_state_dict_from_url(_a     ,  map_location='cpu'     ,  file_name=_a     )[
           """state_dict"""
       ]

       for name, param in state_dict.items():
              print(_a     ,  param.shape     )

       lowerCAmelCase__					     = get_upernet_config(_a     )
       lowerCAmelCase__					     = UperNetForSemanticSegmentation(_a     )
       model.eval()

       # replace "bn" => "batch_norm"
       for key in state_dict.copy().keys():
              lowerCAmelCase__					     = state_dict.pop(_a     )
              if "bn" in key:
                     lowerCAmelCase__					     = key.replace('bn'     ,  'batch_norm'     )
              lowerCAmelCase__					     = val

       # rename keys
       lowerCAmelCase__					     = create_rename_keys(_a     )
       for src, dest in rename_keys:
              rename_key(_a     ,  _a     ,  _a     )
       read_in_q_k_v(_a     ,  config.backbone_config     )

       # fix downsample parameters
       for key, value in state_dict.items():
              if "downsample" in key:
                     if "reduction" in key:
                            lowerCAmelCase__					     = reverse_correct_unfold_reduction_order(_a     )
                     if "norm" in key:
                            lowerCAmelCase__					     = reverse_correct_unfold_norm_order(_a     )

       model.load_state_dict(_a     )

       # verify on image
       lowerCAmelCase__					     = """https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"""
       lowerCAmelCase__					     = Image.open(requests.get(_a     ,  stream=_a     ).raw     ).convert('RGB'     )

       lowerCAmelCase__					     = SegformerImageProcessor()
       lowerCAmelCase__					     = processor(_a     ,  return_tensors='pt'     ).pixel_values

       with torch.no_grad():
              lowerCAmelCase__					     = model(_a     )
              lowerCAmelCase__					     = outputs.logits

       print(logits.shape     )
       print('First values of logits:'     ,  logits[0, 0, :3, :3]     )
       # assert values
       if model_name == "upernet-swin-tiny":
              lowerCAmelCase__					     = torch.tensor(
                  [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]]     )
       elif model_name == "upernet-swin-small":
              lowerCAmelCase__					     = torch.tensor(
                  [[-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.1_9_2_1, -7.1_9_2_1, -6.9_5_3_2], [-7.0_9_0_8, -7.0_9_0_8, -6.8_5_3_4]]     )
       elif model_name == "upernet-swin-base":
              lowerCAmelCase__					     = torch.tensor(
                  [[-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.5_8_5_1, -6.5_8_5_1, -6.4_3_3_0], [-6.4_7_6_3, -6.4_7_6_3, -6.3_2_5_4]]     )
       elif model_name == "upernet-swin-large":
              lowerCAmelCase__					     = torch.tensor(
                  [[-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.5_2_9_7, -7.5_2_9_7, -7.3_8_0_2], [-7.4_0_4_4, -7.4_0_4_4, -7.2_5_8_6]]     )
       print('Logits:'     ,  outputs.logits[0, 0, :3, :3]     )
       assert torch.allclose(outputs.logits[0, 0, :3, :3]     ,  _a     ,  atol=1e-4     )
       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     )
              print(F"""Saving processor to {pytorch_dump_folder_path}"""     )
              processor.save_pretrained(_a     )

       if push_to_hub:
              print(F"""Pushing model and processor for {model_name} to hub"""     )
              model.push_to_hub(F"""openmmlab/{model_name}"""     )
              processor.push_to_hub(F"""openmmlab/{model_name}"""     )


if __name__ == "__main__":
 lowerCAmelCase__     						=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='upernet-swin-tiny',
     type=str,
     choices=[F'upernet-swin-{size}' for size in ['tiny', 'small', 'base', 'large']],
     help='Name of the Swin + UperNet model you\'d like to convert.',
 )
 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.'
 )

 lowerCAmelCase__     						=			parser.parse_args()
 convert_upernet_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
#
########################################################################


lowerCAmelCase__     						=			16
lowerCAmelCase__     						=			32






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__ = 1_6     ):
       lowerCAmelCase__					     = AutoTokenizer.from_pretrained('bert-base-cased'     )
       lowerCAmelCase__					     = load_dataset('glue'     ,  'mrpc'     )

       def tokenize_function(lowerCAmelCase__     ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCAmelCase__					     = 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
       # starting with the main process first:
       with accelerator.main_process_first():
              lowerCAmelCase__					     = datasets.map(
                  lowerCAmelCase__     ,  batched=lowerCAmelCase__     ,  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
       lowerCAmelCase__					     = 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.
              lowerCAmelCase__					     = 1_2_8 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCAmelCase__					     = 1_6
              elif accelerator.mixed_precision != "no":
                     lowerCAmelCase__					     = 8
              else:
                     lowerCAmelCase__					     = None

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

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

       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

    lowerCAmelCase__     						=			mocked_dataloaders  # noqa: F811






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       # For testing only
       if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,  lowerCAmelCase__     ) == "1":
              lowerCAmelCase__					     = 2
       # Initialize accelerator
       lowerCAmelCase__					     = Accelerator(cpu=args.cpu     ,  mixed_precision=args.mixed_precision     )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCAmelCase__					     = config['lr']
       lowerCAmelCase__					     = int(config['num_epochs']     )
       lowerCAmelCase__					     = int(config['seed']     )
       lowerCAmelCase__					     = int(config['batch_size']     )

       lowerCAmelCase__					     = evaluate.load('glue'     ,  'mrpc'     )

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

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

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

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

       # Instantiate scheduler
       lowerCAmelCase__					     = get_linear_schedule_with_warmup(
           optimizer=lowerCAmelCase__     ,  num_warmup_steps=1_0_0     ,  num_training_steps=(len(lowerCAmelCase__     ) * 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.
       lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = accelerator.prepare(
           lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )

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

              model.eval()
              lowerCAmelCase__					     = 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():
                            lowerCAmelCase__					     = model(**lowerCAmelCase__     )
                     lowerCAmelCase__					     = outputs.logits.argmax(dim=-1     )
                     lowerCAmelCase__     , lowerCAmelCase__					     = 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(lowerCAmelCase__     ) - 1:
                                   # Last batch needs to be truncated on distributed systems as it contains additional samples
                                   lowerCAmelCase__					     = predictions[: len(eval_dataloader.dataset     ) - samples_seen]
                                   lowerCAmelCase__					     = 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=lowerCAmelCase__     ,  references=lowerCAmelCase__     ,  )

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






def 				__lowerCamelCase		(						):
       lowerCAmelCase__					     = argparse.ArgumentParser(description='Simple example of training script.'     )
       parser.add_argument(
           '--mixed_precision'     ,  type=lowerCAmelCase__     ,  default=lowerCAmelCase__     ,  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.'     )
       lowerCAmelCase__					     = parser.parse_args()
       lowerCAmelCase__					     = {'lr': 2e-5, 'num_epochs': 3, 'seed': 4_2, 'batch_size': 1_6}
       training_function(lowerCAmelCase__     ,  lowerCAmelCase__     )


if __name__ == "__main__":
    main()
