





"""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()
A_	: int      				=logging.get_logger(__name__)





def SCREAMING_SNAKE_CASE_  (			snake_case  :   Optional[int]		,    snake_case  :   Tuple=False						)->						List[Any]:
       _lowerCamelCase             =   []
       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"
              _lowerCamelCase             =   [(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 SCREAMING_SNAKE_CASE_  (			snake_case  :   Dict		,    snake_case  :   int		,    snake_case  :   List[str]=False						)->						List[str]:




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





def SCREAMING_SNAKE_CASE_  (			snake_case  :   Optional[Any]						)->						Union[str, Any]:
       _lowerCamelCase             =   ['head.weight', 'head.bias']
       for k in ignore_keys:
              state_dict.pop(__A		,    __A						)





def SCREAMING_SNAKE_CASE_  (			snake_case  :   str		,    snake_case  :   List[Any]		,    snake_case  :   Dict						)->						Optional[int]:
       _lowerCamelCase             =   dct.pop(__A						)
       _lowerCamelCase             =   val





def SCREAMING_SNAKE_CASE_  (			)->						Optional[Any]:
       _lowerCamelCase             =   'http://images.cocodataset.org/val2017/000000039769.jpg'
       _lowerCamelCase             =   Image.open(requests.get(__A		,    stream=__A						).raw						)
       return im





@torch.no_grad()
def SCREAMING_SNAKE_CASE_  (			snake_case  :   List[Any]		,    snake_case  :   int						)->						Tuple:
       _lowerCamelCase             =   ViTConfig()
       _lowerCamelCase             =   False
       # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
       if vit_name[-5:] == "in21k":
              _lowerCamelCase             =   True
              _lowerCamelCase             =   int(vit_name[-12:-10]						)
              _lowerCamelCase             =   int(vit_name[-9:-6]						)
       else:
              _lowerCamelCase             =   1_000
              _lowerCamelCase             =   'huggingface/label-files'
              _lowerCamelCase             =   'imagenet-1k-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             =   idalabel
              _lowerCamelCase             =   {v: k for k, v in idalabel.items()}
              _lowerCamelCase             =   int(vit_name[-6:-4]						)
              _lowerCamelCase             =   int(vit_name[-3:]						)
       # size of the architecture
       if "deit" in vit_name:
              if vit_name[9:].startswith('tiny'						):
                     _lowerCamelCase             =   192
                     _lowerCamelCase             =   768
                     _lowerCamelCase             =   12
                     _lowerCamelCase             =   3
              elif vit_name[9:].startswith('small'						):
                     _lowerCamelCase             =   384
                     _lowerCamelCase             =   1_536
                     _lowerCamelCase             =   12
                     _lowerCamelCase             =   6
              else:
                     pass
       else:
              if vit_name[4:].startswith('small'						):
                     _lowerCamelCase             =   768
                     _lowerCamelCase             =   2_304
                     _lowerCamelCase             =   8
                     _lowerCamelCase             =   8
              elif vit_name[4:].startswith('base'						):
                     pass
              elif vit_name[4:].startswith('large'						):
                     _lowerCamelCase             =   1_024
                     _lowerCamelCase             =   4_096
                     _lowerCamelCase             =   24
                     _lowerCamelCase             =   16
              elif vit_name[4:].startswith('huge'						):
                     _lowerCamelCase             =   1_280
                     _lowerCamelCase             =   5_120
                     _lowerCamelCase             =   32
                     _lowerCamelCase             =   16

    # load original model from timm
       _lowerCamelCase             =   timm.create_model(__A		,    pretrained=__A						)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       _lowerCamelCase             =   timm_model.state_dict()
       if base_model:
              remove_classification_head_(__A						)
       _lowerCamelCase             =   create_rename_keys(__A		,    __A						)
       for src, dest in rename_keys:
              rename_key(__A		,    __A		,    __A						)
       read_in_q_k_v(__A		,    __A		,    __A						)

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              _lowerCamelCase             =   ViTModel(__A						).eval()
       else:
              _lowerCamelCase             =   ViTForImageClassification(__A						).eval()
       model.load_state_dict(__A						)

       # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
       if "deit" in vit_name:
              _lowerCamelCase             =   DeiTImageProcessor(size=config.image_size						)
       else:
              _lowerCamelCase             =   ViTImageProcessor(size=config.image_size						)
       _lowerCamelCase             =   image_processor(images=prepare_img()		,    return_tensors='pt'						)
       _lowerCamelCase             =   encoding['pixel_values']
       _lowerCamelCase             =   model(__A						)

       if base_model:
              _lowerCamelCase             =   timm_model.forward_features(__A						)
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(__A		,    outputs.pooler_output		,    atol=1e-3						)
       else:
              _lowerCamelCase             =   timm_model(__A						)
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(__A		,    outputs.logits		,    atol=1e-3						)

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


if __name__ == "__main__":
      A_	: Optional[Any]      				=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."""
      )

      A_	: List[str]      				=parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)









# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCamelCase__		 =     '''Create a default config file for Accelerate with only a few flags set.'''







def 						lowerCamelCase__      ( __A    :List[Any]="no"	,__A    :str = default_json_config_file	,__A    :bool = False		):


       """simple docstring"""




       __snake_case       	=							Path(__A		)
       path.parent.mkdir(parents=__A	,exist_ok=__A		)
       if path.exists():
              print(
                  F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.'		)
              return False
       __snake_case       	=							mixed_precision.lower()
       if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
              raise ValueError(
                  F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}'		)
       __snake_case       	=							{
           """compute_environment""": """LOCAL_MACHINE""",
           """mixed_precision""": mixed_precision,
       }
       if torch.cuda.is_available():
              __snake_case       	=							torch.cuda.device_count()
              __snake_case       	=							num_gpus
              __snake_case       	=							False
              if num_gpus > 1:
                     __snake_case       	=							"""MULTI_GPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_xpu_available() and use_xpu:
              __snake_case       	=							torch.xpu.device_count()
              __snake_case       	=							num_xpus
              __snake_case       	=							False
              if num_xpus > 1:
                     __snake_case       	=							"""MULTI_XPU"""
              else:
                     __snake_case       	=							"""NO"""
       elif is_npu_available():
              __snake_case       	=							torch.npu.device_count()
              __snake_case       	=							num_npus
              __snake_case       	=							False
              if num_npus > 1:
                     __snake_case       	=							"""MULTI_NPU"""
              else:
                     __snake_case       	=							"""NO"""
       else:
              __snake_case       	=							0
              __snake_case       	=							True
              __snake_case       	=							1
              __snake_case       	=							"""NO"""
       __snake_case       	=							ClusterConfig(**__A		)
       config.to_json_file(__A		)
       return path







def 						lowerCamelCase__      ( __A    :Dict	,__A    :List[Any]		):


       """simple docstring"""




       __snake_case       	=							parser.add_parser("""default"""	,parents=__A	,help=__A	,formatter_class=__A		)
       parser.add_argument(
           """--config_file"""	,default=__A	,help=(
               """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
               """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
               """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
               """with 'huggingface'."""
           )	,dest="""save_location"""	,)

       parser.add_argument(
           """--mixed_precision"""	,choices=["""no""", """fp16""", """bf16"""]	,type=__A	,help="""Whether or not to use mixed precision training. """
           """Choose between FP16 and BF16 (bfloat16) training. """
           """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""	,default="""no"""	,)
       parser.set_defaults(func=__A		)
       return parser







def 						lowerCamelCase__      ( __A    :Optional[Any]		):


       """simple docstring"""




       __snake_case       	=							write_basic_config(args.mixed_precision	,args.save_location		)
       if config_file:
              print(F'accelerate configuration saved at {config_file}'		)

