

import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel

def       __snake_case						( __UpperCamelCase	: Optional[int]    ):



      """simple docstring"""

      if "img_encoder.pos_embed" in name:
            A_						  =				name.replace("img_encoder.pos_embed"				,"vision_model.embeddings.position_embeddings"    )
      if "img_encoder.patch_embed.proj" in name:
            A_						  =				name.replace("img_encoder.patch_embed.proj"				,"vision_model.embeddings.patch_embeddings.projection"    )
      if "img_encoder.patch_embed.norm" in name:
            A_						  =				name.replace("img_encoder.patch_embed.norm"				,"vision_model.embeddings.layernorm"    )
      if "img_encoder.layers" in name:
            A_						  =				name.replace("img_encoder.layers"				,"vision_model.encoder.stages"    )
      if "blocks" in name and "res" not in name:
            A_						  =				name.replace("blocks"				,"layers"    )
      if "attn" in name and "pre_assign" not in name:
            A_						  =				name.replace("attn"				,"self_attn"    )
      if "proj" in name and "self_attn" in name and "text" not in name:
            A_						  =				name.replace("proj"				,"out_proj"    )
      if "pre_assign_attn.attn.proj" in name:
            A_						  =				name.replace("pre_assign_attn.attn.proj"				,"pre_assign_attn.attn.out_proj"    )
      if "norm1" in name:
            A_						  =				name.replace("norm1"				,"layer_norm1"    )
      if "norm2" in name and "pre_assign" not in name:
            A_						  =				name.replace("norm2"				,"layer_norm2"    )
      if "img_encoder.norm" in name:
            A_						  =				name.replace("img_encoder.norm"				,"vision_model.layernorm"    )
      # text encoder
      if "text_encoder.token_embedding" in name:
            A_						  =				name.replace("text_encoder.token_embedding"				,"text_model.embeddings.token_embedding"    )
      if "text_encoder.positional_embedding" in name:
            A_						  =				name.replace("text_encoder.positional_embedding"				,"text_model.embeddings.position_embedding.weight"    )
      if "text_encoder.transformer.resblocks." in name:
            A_						  =				name.replace("text_encoder.transformer.resblocks."				,"text_model.encoder.layers."    )
      if "ln_1" in name:
            A_						  =				name.replace("ln_1"				,"layer_norm1"    )
      if "ln_2" in name:
            A_						  =				name.replace("ln_2"				,"layer_norm2"    )
      if "c_fc" in name:
            A_						  =				name.replace("c_fc"				,"fc1"    )
      if "c_proj" in name:
            A_						  =				name.replace("c_proj"				,"fc2"    )
      if "text_encoder" in name:
            A_						  =				name.replace("text_encoder"				,"text_model"    )
      if "ln_final" in name:
            A_						  =				name.replace("ln_final"				,"final_layer_norm"    )
      # projection layers
      if "img_projector.linear_hidden." in name:
            A_						  =				name.replace("img_projector.linear_hidden."				,"visual_projection."    )
      if "img_projector.linear_out." in name:
            A_						  =				name.replace("img_projector.linear_out."				,"visual_projection.3."    )
      if "text_projector.linear_hidden" in name:
            A_						  =				name.replace("text_projector.linear_hidden"				,"text_projection"    )
      if "text_projector.linear_out" in name:
            A_						  =				name.replace("text_projector.linear_out"				,"text_projection.3"    )

      return name

def       __snake_case						( __UpperCamelCase	: List[str]				,__UpperCamelCase	: List[Any]    ):



      """simple docstring"""

      for key in orig_state_dict.copy().keys():
            A_						  =				orig_state_dict.pop(__UpperCamelCase    )

            if "qkv" in key:
                  # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
                  # we need to split them up into separate matrices/vectors
                  A_						  =				key.split("."    )
                  A_  ,     A_						  =				int(key_split[2]    ), int(key_split[4]    )
                  A_						  =				config.vision_config.hidden_size
                  if "weight" in key:
                        A_						  =				val[:dim, :]
                        A_						  =				val[dim : dim * 2, :]
                        A_						  =				val[-dim:, :]
                  else:
                        A_						  =				val[:dim]
                        A_						  =				val[dim : dim * 2]
                        A_						  =				val[-dim:]
            elif "in_proj" in key:
                  # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
                  # we need to split them up into separate matrices/vectors
                  A_						  =				key.split("."    )
                  A_						  =				int(key_split[3]    )
                  A_						  =				config.text_config.hidden_size
                  if "weight" in key:
                        A_						  =				val[:dim, :]
                        A_						  =				val[
                            dim : dim * 2, :
                        ]
                        A_						  =				val[-dim:, :]
                  else:
                        A_						  =				val[:dim]
                        A_						  =				val[dim : dim * 2]
                        A_						  =				val[-dim:]
            else:
                  A_						  =				rename_key(__UpperCamelCase    )
                  # squeeze if necessary
                  if (
                      "text_projection.0" in new_name
                      or "text_projection.3" in new_name
                      or "visual_projection.0" in new_name
                      or "visual_projection.3" in new_name
                  ):
                        A_						  =				val.squeeze_()
                  else:
                        A_						  =				val

      return orig_state_dict

def       __snake_case						( ):



      """simple docstring"""

      A_						  =				"http://images.cocodataset.org/val2017/000000039769.jpg"
      A_						  =				Image.open(requests.get(__UpperCamelCase				,stream=__UpperCamelCase    ).raw    )
      return im

@torch.no_grad()
def       __snake_case						( __UpperCamelCase	: Optional[int]				,__UpperCamelCase	: Union[str, Any]				,__UpperCamelCase	: Optional[int]="groupvit-gcc-yfcc"				,__UpperCamelCase	: List[Any]=False    ):



      """simple docstring"""

      A_						  =				GroupViTConfig()
      A_						  =				GroupViTModel(__UpperCamelCase    ).eval()

      A_						  =				torch.load(__UpperCamelCase				,map_location="cpu"    )["model"]
      A_						  =				convert_state_dict(__UpperCamelCase				,__UpperCamelCase    )
      A_  ,     A_						  =				model.load_state_dict(__UpperCamelCase				,strict=__UpperCamelCase    )
      assert missing_keys == ["text_model.embeddings.position_ids"]
      assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__UpperCamelCase    ) == 0)

      # verify result
      A_						  =				CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"    )
      A_						  =				prepare_img()
      A_						  =				processor(text=["a photo of a cat", "a photo of a dog"]				,images=__UpperCamelCase				,padding=__UpperCamelCase				,return_tensors="pt"    )

      with torch.no_grad():
            A_						  =				model(**__UpperCamelCase    )

      if model_name == "groupvit-gcc-yfcc":
            A_						  =				torch.tensor([[13.3523, 6.3629]]    )
      elif model_name == "groupvit-gcc-redcaps":
            A_						  =				torch.tensor([[16.1873, 8.6230]]    )
      else:
            raise ValueError(f'''Model name {model_name} not supported.'''    )
      assert torch.allclose(outputs.logits_per_image				,__UpperCamelCase				,atol=1E-3    )

      processor.save_pretrained(__UpperCamelCase    )
      model.save_pretrained(__UpperCamelCase    )
      print("Successfully saved processor and model to"				,__UpperCamelCase    )

      if push_to_hub:
            print("Pushing to the hub..."    )
            processor.push_to_hub(__UpperCamelCase				,organization="nielsr"    )
            model.push_to_hub(__UpperCamelCase				,organization="nielsr"    )


if __name__ == "__main__":
      __a    :Tuple      =			argparse.ArgumentParser()
      parser.add_argument(
          '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
      )
      parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
      parser.add_argument(
          '--model_name',
          default='groupvit-gccy-fcc',
          type=str,
          help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
      )
      parser.add_argument(
          '--push_to_hub',
          action='store_true',
          help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
      )
      __a    :int      =			parser.parse_args()

      convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, 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
# and perform gradient accumulation
#
# 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 run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


snake_case__   :						Dict													=   1_6
snake_case__   :						List[str]													=   3_2






def      _snake_case     (__lowercase     ,   __lowercase = 16):
   UpperCamelCase_						=  AutoTokenizer.from_pretrained('bert-base-cased')
   UpperCamelCase_						=  load_dataset('glue'     ,   'mrpc')

   def tokenize_function(__lowercase):
      # max_length=None => use the model max length (it's actually the default)
      UpperCamelCase_						=  tokenizer(examples['sentence1']     ,   examples['sentence2']     ,   truncation=__lowercase     ,   max_length=__lowercase)
      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(
          __lowercase     ,   batched=__lowercase     ,   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(__lowercase):
      # 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(
          __lowercase     ,   padding='longest'     ,   max_length=__lowercase     ,   pad_to_multiple_of=__lowercase     ,   return_tensors='pt'     ,   )

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

   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

 snake_case__   :						List[str]													=   mocked_dataloaders  # noqa: F811






def      _snake_case     (__lowercase     ,   __lowercase):
   # For testing only
   if os.environ.get('TESTING_MOCKED_DATALOADERS'     ,   __lowercase) == "1":
      UpperCamelCase_						=  2
   # New Code #
   UpperCamelCase_						=  int(args.gradient_accumulation_steps)
   # Initialize accelerator
   UpperCamelCase_						=  Accelerator(
       cpu=args.cpu     ,   mixed_precision=args.mixed_precision     ,   gradient_accumulation_steps=__lowercase)
   if accelerator.distributed_type == DistributedType.TPU and gradient_accumulation_steps > 1:
      raise NotImplementedError(
          'Gradient accumulation on TPUs is currently not supported. Pass `gradient_accumulation_steps=1`')
   # 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')

   set_seed(__lowercase)
   UpperCamelCase_		,   UpperCamelCase_						=  get_dataloaders(__lowercase     ,   __lowercase)
   # 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=__lowercase)

   # 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=__lowercase)

   # Instantiate scheduler
   UpperCamelCase_						=  get_linear_schedule_with_warmup(
       optimizer=__lowercase     ,   num_warmup_steps=100     ,   num_training_steps=(len(__lowercase) * num_epochs)     ,   )

   # 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(
       __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase)

   # Now we train the model
   for epoch in range(__lowercase):
      model.train()
      for step, batch in enumerate(__lowercase):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device)
         # New code #
         # We use the new `accumulate` context manager to perform gradient accumulation
         # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
         with accelerator.accumulate(__lowercase):
            UpperCamelCase_						=  model(**__lowercase)
            UpperCamelCase_						=  output.loss
            accelerator.backward(__lowercase)
            optimizer.step()
            lr_scheduler.step()
            optimizer.zero_grad()

      model.eval()
      for step, batch in enumerate(__lowercase):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         batch.to(accelerator.device)
         with torch.no_grad():
            UpperCamelCase_						=  model(**__lowercase)
         UpperCamelCase_						=  outputs.logits.argmax(dim=-1)
         UpperCamelCase_		,   UpperCamelCase_						=  accelerator.gather_for_metrics((predictions, batch['labels']))
         metric.add_batch(
             predictions=__lowercase     ,   references=__lowercase     ,   )

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






def      _snake_case     ():
   UpperCamelCase_						=  argparse.ArgumentParser(description='Simple example of training script.')
   parser.add_argument(
       '--mixed_precision'     ,   type=__lowercase     ,   default=__lowercase     ,   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.'     ,   )
   # New Code #
   parser.add_argument(
       '--gradient_accumulation_steps'     ,   type=__lowercase     ,   default=1     ,   help='The number of minibatches to be ran before gradients are accumulated.'     ,   )
   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(__lowercase     ,   __lowercase)


if __name__ == "__main__":
 main()





