



'''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 AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Tuple:
 __UpperCAmelCase					=   SwinvaConfig()
 __UpperCAmelCase					=   swinva_name.split('_'    )

 __UpperCAmelCase					=   name_split[1]
 if "to" in name_split[3]:
  __UpperCAmelCase					=   int(name_split[3][-3:]    )
 else:
  __UpperCAmelCase					=   int(name_split[3]    )
 if "to" in name_split[2]:
  __UpperCAmelCase					=   int(name_split[2][-2:]    )
 else:
  __UpperCAmelCase					=   int(name_split[2][6:]    )

 if model_size == "tiny":
  __UpperCAmelCase					=   96
  __UpperCAmelCase					=   (2, 2, 6, 2)
  __UpperCAmelCase					=   (3, 6, 12, 24)
 elif model_size == "small":
  __UpperCAmelCase					=   96
  __UpperCAmelCase					=   (2, 2, 18, 2)
  __UpperCAmelCase					=   (3, 6, 12, 24)
 elif model_size == "base":
  __UpperCAmelCase					=   1_28
  __UpperCAmelCase					=   (2, 2, 18, 2)
  __UpperCAmelCase					=   (4, 8, 16, 32)
 else:
  __UpperCAmelCase					=   1_92
  __UpperCAmelCase					=   (2, 2, 18, 2)
  __UpperCAmelCase					=   (6, 12, 24, 48)

 if "to" in swinva_name:
  __UpperCAmelCase					=   (12, 12, 12, 6)

 if ("22k" in swinva_name) and ("to" not in swinva_name):
  __UpperCAmelCase					=   2_18_41
  __UpperCAmelCase					=   "huggingface/label-files"
  __UpperCAmelCase					=   "imagenet-22k-id2label.json"
  __UpperCAmelCase					=   json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    repo_type='dataset'    )       ,    'r'    )    )
  __UpperCAmelCase					=   {int(__SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
  __UpperCAmelCase					=   idalabel
  __UpperCAmelCase					=   {v: k for k, v in idalabel.items()}

 else:
  __UpperCAmelCase					=   10_00
  __UpperCAmelCase					=   "huggingface/label-files"
  __UpperCAmelCase					=   "imagenet-1k-id2label.json"
  __UpperCAmelCase					=   json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    repo_type='dataset'    )       ,    'r'    )    )
  __UpperCAmelCase					=   {int(__SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}
  __UpperCAmelCase					=   idalabel
  __UpperCAmelCase					=   {v: k for k, v in idalabel.items()}

 __UpperCAmelCase					=   img_size
 __UpperCAmelCase					=   num_classes
 __UpperCAmelCase					=   embed_dim
 __UpperCAmelCase					=   depths
 __UpperCAmelCase					=   num_heads
 __UpperCAmelCase					=   window_size

 return config







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Optional[int]:






 if "patch_embed.proj" in name:
  __UpperCAmelCase					=   name.replace('patch_embed.proj'       ,    'embeddings.patch_embeddings.projection'    )
 if "patch_embed.norm" in name:
  __UpperCAmelCase					=   name.replace('patch_embed.norm'       ,    'embeddings.norm'    )
 if "layers" in name:
  __UpperCAmelCase					=   "encoder." + name
 if "attn.proj" in name:
  __UpperCAmelCase					=   name.replace('attn.proj'       ,    'attention.output.dense'    )
 if "attn" in name:
  __UpperCAmelCase					=   name.replace('attn'       ,    'attention.self'    )
 if "norm1" in name:
  __UpperCAmelCase					=   name.replace('norm1'       ,    'layernorm_before'    )
 if "norm2" in name:
  __UpperCAmelCase					=   name.replace('norm2'       ,    'layernorm_after'    )
 if "mlp.fc1" in name:
  __UpperCAmelCase					=   name.replace('mlp.fc1'       ,    'intermediate.dense'    )
 if "mlp.fc2" in name:
  __UpperCAmelCase					=   name.replace('mlp.fc2'       ,    'output.dense'    )
 if "q_bias" in name:
  __UpperCAmelCase					=   name.replace('q_bias'       ,    'query.bias'    )
 if "k_bias" in name:
  __UpperCAmelCase					=   name.replace('k_bias'       ,    'key.bias'    )
 if "v_bias" in name:
  __UpperCAmelCase					=   name.replace('v_bias'       ,    'value.bias'    )
 if "cpb_mlp" in name:
  __UpperCAmelCase					=   name.replace('cpb_mlp'       ,    'continuous_position_bias_mlp'    )
 if name == "norm.weight":
  __UpperCAmelCase					=   "layernorm.weight"
 if name == "norm.bias":
  __UpperCAmelCase					=   "layernorm.bias"

 if "head" in name:
  __UpperCAmelCase					=   name.replace('head'       ,    'classifier'    )
 else:
  __UpperCAmelCase					=   "swinv2." + name

 return name







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     int:






 for key in orig_state_dict.copy().keys():
  __UpperCAmelCase					=   orig_state_dict.pop(__SCREAMING_SNAKE_CASE    )

  if "mask" in key:
   continue
  elif "qkv" in key:
   __UpperCAmelCase					=   key.split('.'    )
   __UpperCAmelCase					=   int(key_split[1]    )
   __UpperCAmelCase					=   int(key_split[3]    )
   __UpperCAmelCase					=   model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_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					=   val

 return orig_state_dict







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 __UpperCAmelCase					=   timm.create_model(__SCREAMING_SNAKE_CASE       ,    pretrained=__SCREAMING_SNAKE_CASE    )
 timm_model.eval()

 __UpperCAmelCase					=   get_swinva_config(__SCREAMING_SNAKE_CASE    )
 __UpperCAmelCase					=   SwinvaForImageClassification(__SCREAMING_SNAKE_CASE    )
 model.eval()

 __UpperCAmelCase					=   convert_state_dict(timm_model.state_dict()       ,    __SCREAMING_SNAKE_CASE    )
 model.load_state_dict(__SCREAMING_SNAKE_CASE    )

 __UpperCAmelCase					=   "http://images.cocodataset.org/val2017/000000039769.jpg"

 __UpperCAmelCase					=   AutoImageProcessor.from_pretrained('microsoft/{}'.format(swinva_name.replace('_'       ,    '-'    )    )    )
 __UpperCAmelCase					=   Image.open(requests.get(__SCREAMING_SNAKE_CASE       ,    stream=__SCREAMING_SNAKE_CASE    ).raw    )
 __UpperCAmelCase					=   image_processor(images=__SCREAMING_SNAKE_CASE       ,    return_tensors='pt'    )

 __UpperCAmelCase					=   timm_model(inputs['pixel_values']    )
 __UpperCAmelCase					=   model(**__SCREAMING_SNAKE_CASE    ).logits

 assert torch.allclose(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE       ,    atol=1e-3    )

 print(F'Saving model {swinva_name} to {pytorch_dump_folder_path}'    )
 model.save_pretrained(__SCREAMING_SNAKE_CASE    )

 print(F'Saving image processor to {pytorch_dump_folder_path}'    )
 image_processor.save_pretrained(__SCREAMING_SNAKE_CASE    )

 model.push_to_hub(
     repo_path_or_name=Path(__SCREAMING_SNAKE_CASE       ,    __SCREAMING_SNAKE_CASE    )       ,    organization='nandwalritik'       ,    commit_message='Add model'       ,    )


if __name__ == "__main__":
    _A:					Optional[int]							=				argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--swinv2_name""",
        default="""swinv2_tiny_patch4_window8_256""",
        type=str,
        help="""Name of the Swinv2 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_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)












'''simple docstring'''




# 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


_A:					str							=				"""Create a default config file for Accelerate with only a few flags set."""







def _lowerCAmelCase     (      _lowerCAmelCase="no"       ,    _lowerCAmelCase = default_json_config_file       ,    _lowerCAmelCase = False    )->     List[Any]:
 __UpperCAmelCase					=   Path(_lowerCAmelCase    )
 path.parent.mkdir(parents=_lowerCAmelCase       ,    exist_ok=_lowerCAmelCase    )
 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
 __UpperCAmelCase					=   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}'    )
 __UpperCAmelCase					=   {
     'compute_environment': 'LOCAL_MACHINE',
     'mixed_precision': mixed_precision,
 }
 if torch.cuda.is_available():
  __UpperCAmelCase					=   torch.cuda.device_count()
  __UpperCAmelCase					=   num_gpus
  __UpperCAmelCase					=   False
  if num_gpus > 1:
   __UpperCAmelCase					=   'MULTI_GPU'
  else:
   __UpperCAmelCase					=   'NO'
 elif is_xpu_available() and use_xpu:
  __UpperCAmelCase					=   torch.xpu.device_count()
  __UpperCAmelCase					=   num_xpus
  __UpperCAmelCase					=   False
  if num_xpus > 1:
   __UpperCAmelCase					=   'MULTI_XPU'
  else:
   __UpperCAmelCase					=   'NO'
 elif is_npu_available():
  __UpperCAmelCase					=   torch.npu.device_count()
  __UpperCAmelCase					=   num_npus
  __UpperCAmelCase					=   False
  if num_npus > 1:
   __UpperCAmelCase					=   'MULTI_NPU'
  else:
   __UpperCAmelCase					=   'NO'
 else:
  __UpperCAmelCase					=   0
  __UpperCAmelCase					=   True
  __UpperCAmelCase					=   1
  __UpperCAmelCase					=   'NO'
 __UpperCAmelCase					=   ClusterConfig(**_lowerCAmelCase    )
 config.to_json_file(_lowerCAmelCase    )
 return path







def _lowerCAmelCase     (      _lowerCAmelCase       ,    _lowerCAmelCase    )->     List[str]:
 __UpperCAmelCase					=   parser.add_parser('default'       ,    parents=_lowerCAmelCase       ,    help=_lowerCAmelCase       ,    formatter_class=_lowerCAmelCase    )
 parser.add_argument(
     '--config_file'       ,    default=_lowerCAmelCase       ,    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=_lowerCAmelCase       ,    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=_lowerCAmelCase    )
 return parser







def _lowerCAmelCase     (      _lowerCAmelCase    )->     Union[str, Any]:
 __UpperCAmelCase					=   write_basic_config(args.mixed_precision       ,    args.save_location    )
 if config_file:
  print(F'accelerate configuration saved at {config_file}'    )





