'''simple docstring'''





import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase  :Tuple  				=    logging.get_logger(__name__)


def     __lowerCAmelCase						(       a_       ,      a_=False		)      ->	Optional[int]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       []
     for i in range(config.num_hidden_layers		):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")		)
          rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")		)
          rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")		)
          rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")		)
          rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")		)
          rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")		)

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('cls_token', 'vit.embeddings.cls_token'),
             ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
             ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
             ('pos_embed', 'vit.embeddings.position_embeddings'),
         ]		)

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
                  ('pre_logits.fc.weight', 'pooler.dense.weight'),
                  ('pre_logits.fc.bias', 'pooler.dense.bias'),
              ]		)

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          SCREAMING_SNAKE_CASE					:			List[str]    	=       [(pair[0], pair[1][4:]) if pair[1].startswith('vit'		) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]		)

     return rename_keys


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_=False		)      ->	Any:





     '''simple docstring'''





     for i in range(config.num_hidden_layers		):
          if base_model:
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ''
          else:
               SCREAMING_SNAKE_CASE					:			List[str]    	=       'vit.'
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          SCREAMING_SNAKE_CASE					:			List[str]    	=       state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""		)
          SCREAMING_SNAKE_CASE					:			List[str]    	=       state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""		)
          # next, add query, keys and values (in that order) to the state dict
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_weight[
              : config.hidden_size, :
          ]
          SCREAMING_SNAKE_CASE					:			List[str]    	=       in_proj_bias[: config.hidden_size]
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_weight[
              -config.hidden_size :, :
          ]
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       in_proj_bias[-config.hidden_size :]


def     __lowerCAmelCase						(       a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			int    	=       ['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(_snake_case       ,      _snake_case		)


def     __lowerCAmelCase						(       a_       ,      a_       ,      a_		)      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       dct.pop(_snake_case		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       val


def     __lowerCAmelCase						(       )      ->	int:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			List[str]    	=       'http://images.cocodataset.org/val2017/000000039769.jpg'
     SCREAMING_SNAKE_CASE					:			List[str]    	=       Image.open(requests.get(_snake_case       ,      stream=_snake_case		).raw		)
     return im


@torch.no_grad()
def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Any:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       ViTConfig()
     SCREAMING_SNAKE_CASE					:			int    	=       False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          SCREAMING_SNAKE_CASE					:			Dict    	=       True
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       int(vit_name[-12:-10]		)
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       int(vit_name[-9:-6]		)
     else:
          SCREAMING_SNAKE_CASE					:			int    	=       1000
          SCREAMING_SNAKE_CASE					:			List[str]    	=       'huggingface/label-files'
          SCREAMING_SNAKE_CASE					:			str    	=       'imagenet-1k-id2label.json'
          SCREAMING_SNAKE_CASE					:			Any    	=       json.load(open(hf_hub_download(_snake_case       ,      _snake_case       ,      repo_type='dataset'		)       ,      'r'		)		)
          SCREAMING_SNAKE_CASE					:			Tuple    	=       {int(_snake_case		): v for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE					:			Optional[int]    	=       idalabel
          SCREAMING_SNAKE_CASE					:			Dict    	=       {v: k for k, v in idalabel.items()}
          SCREAMING_SNAKE_CASE					:			List[str]    	=       int(vit_name[-6:-4]		)
          SCREAMING_SNAKE_CASE					:			List[str]    	=       int(vit_name[-3:]		)
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith('tiny'		):
               SCREAMING_SNAKE_CASE					:			Tuple    	=       192
               SCREAMING_SNAKE_CASE					:			Tuple    	=       768
               SCREAMING_SNAKE_CASE					:			Tuple    	=       12
               SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       3
          elif vit_name[9:].startswith('small'		):
               SCREAMING_SNAKE_CASE					:			Any    	=       384
               SCREAMING_SNAKE_CASE					:			Tuple    	=       1536
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       12
               SCREAMING_SNAKE_CASE					:			int    	=       6
          else:
               pass
     else:
          if vit_name[4:].startswith('small'		):
               SCREAMING_SNAKE_CASE					:			List[str]    	=       768
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       2304
               SCREAMING_SNAKE_CASE					:			int    	=       8
               SCREAMING_SNAKE_CASE					:			Optional[int]    	=       8
          elif vit_name[4:].startswith('base'		):
               pass
          elif vit_name[4:].startswith('large'		):
               SCREAMING_SNAKE_CASE					:			List[Any]    	=       1024
               SCREAMING_SNAKE_CASE					:			Tuple    	=       4096
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       24
               SCREAMING_SNAKE_CASE					:			Any    	=       16
          elif vit_name[4:].startswith('huge'		):
               SCREAMING_SNAKE_CASE					:			Dict    	=       1280
               SCREAMING_SNAKE_CASE					:			Tuple    	=       5120
               SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       32
               SCREAMING_SNAKE_CASE					:			Dict    	=       16

    # load original model from timm
     SCREAMING_SNAKE_CASE					:			Any    	=       timm.create_model(_snake_case       ,      pretrained=_snake_case		)
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     SCREAMING_SNAKE_CASE					:			List[str]    	=       timm_model.state_dict()
     if base_model:
          remove_classification_head_(_snake_case		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       create_rename_keys(_snake_case       ,      _snake_case		)
     for src, dest in rename_keys:
          rename_key(_snake_case       ,      _snake_case       ,      _snake_case		)
     read_in_q_k_v(_snake_case       ,      _snake_case       ,      _snake_case		)

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ViTModel(_snake_case		).eval()
     else:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ViTForImageClassification(_snake_case		).eval()
     model.load_state_dict(_snake_case		)

     # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
     if "deit" in vit_name:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       DeiTImageProcessor(size=config.image_size		)
     else:
          SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       ViTImageProcessor(size=config.image_size		)
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       image_processor(images=prepare_img()       ,      return_tensors='pt'		)
     SCREAMING_SNAKE_CASE					:			str    	=       encoding['pixel_values']
     SCREAMING_SNAKE_CASE					:			List[str]    	=       model(_snake_case		)

     if base_model:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       timm_model.forward_features(_snake_case		)
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(_snake_case       ,      outputs.pooler_output       ,      atol=1e-3		)
     else:
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       timm_model(_snake_case		)
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(_snake_case       ,      outputs.logits       ,      atol=1e-3		)

     Path(_snake_case		).mkdir(exist_ok=_snake_case		)
     print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""		)
     model.save_pretrained(_snake_case		)
     print(f"""Saving image processor to {pytorch_dump_folder_path}"""		)
     image_processor.save_pretrained(_snake_case		)


if __name__ == "__main__":
 _lowerCAmelCase  :str  				=    argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--vit_name""",
     default="""vit_base_patch16_224""",
     type=str,
     help="""Name of the ViT timm 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."""
 )

 _lowerCAmelCase  :Optional[Any]  				=    parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)




'''simple docstring'''





import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler






@dataclass
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE ):




    '''simple docstring'''







    snake_case__							:   int							=						0
    snake_case__							:   bool							=						False
    snake_case__							:   float							=						3.0






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Tuple:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         self.assertDictEqual(MockClass().to_kwargs()    ,							{} )
         self.assertDictEqual(MockClass(a=2 ).to_kwargs()    ,							{'a': 2} )
         self.assertDictEqual(MockClass(a=2    ,							b=lowercase__ ).to_kwargs()    ,							{'a': 2, 'b': True} )
         self.assertDictEqual(MockClass(a=2    ,							c=2.2_5 ).to_kwargs()    ,							{'a': 2, 'c': 2.2_5} )






    @require_cuda
    def       _UpperCamelCase    (       self )							-> Dict:
         # If no defaults are changed, `to_kwargs` returns an empty dict.
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       GradScalerKwargs(init_scale=1_024    ,							growth_factor=2 )
         AcceleratorState._reset_state()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       Accelerator(mixed_precision='fp16'    ,							kwargs_handlers=[scaler_handler] )
         print(accelerator.use_fpaa )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       accelerator.scaler

         # Check the kwargs have been applied
         self.assertEqual(scaler._init_scale    ,							1_0_2_4.0 )
         self.assertEqual(scaler._growth_factor    ,							2.0 )

         # Check the other values are at the default
         self.assertEqual(scaler._backoff_factor    ,							0.5 )
         self.assertEqual(scaler._growth_interval    ,							2_000 )
         self.assertEqual(scaler._enabled    ,							lowercase__ )






    @require_multi_gpu
    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       ['torchrun', F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__ )]
         execute_subprocess_async(lowercase__    ,							env=os.environ.copy() )


if __name__ == "__main__":
 _lowerCAmelCase  :int  				=    DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
 _lowerCAmelCase  :int  				=    Accelerator(kwargs_handlers=[ddp_scaler])
 _lowerCAmelCase  :str  				=    torch.nn.Linear(100, 200)
 _lowerCAmelCase  :Optional[Any]  				=    accelerator.prepare(model)

 # Check the values changed in kwargs
 _lowerCAmelCase  :Dict  				=    """"""
 _lowerCAmelCase  :Optional[int]  				=    model.bucket_bytes_cap // (1_024 * 1_024)
 if observed_bucket_cap_map != 15:
  error_msg += f"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
 if model.find_unused_parameters is not True:
  error_msg += f"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

 # Check the values of the defaults
 if model.dim != 0:
  error_msg += f"Default value not respected, should have `0` but found {model.dim}.\n"
 if model.broadcast_buffers is not True:
  error_msg += f"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
 if model.gradient_as_bucket_view is not False:
  error_msg += f"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

 # Raise error at the end to make sure we don't stop at the first failure.
 if len(error_msg) > 0:
  raise ValueError(error_msg)



