
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 							a__					(							snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	List[str]					=		SwinvaConfig()
  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		swinva_name.split('''_'''							)

  __SCREAMING_SNAKE_CASE      :	Dict					=		name_split[1]
  if "to" in name_split[3]:
    __SCREAMING_SNAKE_CASE      :	int					=		int(name_split[3][-3:]							)
  else:
    __SCREAMING_SNAKE_CASE      :	str					=		int(name_split[3]							)
  if "to" in name_split[2]:
    __SCREAMING_SNAKE_CASE      :	Any					=		int(name_split[2][-2:]							)
  else:
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		int(name_split[2][6:]							)

  if model_size == "tiny":
    __SCREAMING_SNAKE_CASE      :	int					=		96
    __SCREAMING_SNAKE_CASE      :	int					=		(2, 2, 6, 2)
    __SCREAMING_SNAKE_CASE      :	Tuple					=		(3, 6, 12, 24)
  elif model_size == "small":
    __SCREAMING_SNAKE_CASE      :	Any					=		96
    __SCREAMING_SNAKE_CASE      :	Any					=		(2, 2, 18, 2)
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(3, 6, 12, 24)
  elif model_size == "base":
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		128
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(2, 2, 18, 2)
    __SCREAMING_SNAKE_CASE      :	Tuple					=		(4, 8, 16, 32)
  else:
    __SCREAMING_SNAKE_CASE      :	Any					=		192
    __SCREAMING_SNAKE_CASE      :	Tuple					=		(2, 2, 18, 2)
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(6, 12, 24, 48)

  if "to" in swinva_name:
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(12, 12, 12, 6)

  if ("22k" in swinva_name) and ("to" not in swinva_name):
    __SCREAMING_SNAKE_CASE      :	int					=		21_841
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		'''huggingface/label-files'''
    __SCREAMING_SNAKE_CASE      :	List[str]					=		'''imagenet-22k-id2label.json'''
    __SCREAMING_SNAKE_CASE      :	int					=		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      :	Union[str, Any]					=		idalabel
    __SCREAMING_SNAKE_CASE      :	List[str]					=		{v: k for k, v in idalabel.items()}

  else:
    __SCREAMING_SNAKE_CASE      :	Tuple					=		1_000
    __SCREAMING_SNAKE_CASE      :	str					=		'''huggingface/label-files'''
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		'''imagenet-1k-id2label.json'''
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		json.load(open(hf_hub_download(snake_case       ,			snake_case       ,			repo_type='''dataset'''							)       ,			'''r'''							)							)
    __SCREAMING_SNAKE_CASE      :	str					=		{int(snake_case							): v for k, v in idalabel.items()}
    __SCREAMING_SNAKE_CASE      :	int					=		idalabel
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		{v: k for k, v in idalabel.items()}

  __SCREAMING_SNAKE_CASE      :	int					=		img_size
  __SCREAMING_SNAKE_CASE      :	Tuple					=		num_classes
  __SCREAMING_SNAKE_CASE      :	str					=		embed_dim
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		depths
  __SCREAMING_SNAKE_CASE      :	str					=		num_heads
  __SCREAMING_SNAKE_CASE      :	Dict					=		window_size

  return config


def 							a__					(							snake_case							):





  """simple docstring"""
  if "patch_embed.proj" in name:
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		name.replace('''patch_embed.proj'''       ,			'''embeddings.patch_embeddings.projection'''							)
  if "patch_embed.norm" in name:
    __SCREAMING_SNAKE_CASE      :	int					=		name.replace('''patch_embed.norm'''       ,			'''embeddings.norm'''							)
  if "layers" in name:
    __SCREAMING_SNAKE_CASE      :	str					=		'''encoder.''' + name
  if "attn.proj" in name:
    __SCREAMING_SNAKE_CASE      :	Optional[int]					=		name.replace('''attn.proj'''       ,			'''attention.output.dense'''							)
  if "attn" in name:
    __SCREAMING_SNAKE_CASE      :	List[str]					=		name.replace('''attn'''       ,			'''attention.self'''							)
  if "norm1" in name:
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		name.replace('''norm1'''       ,			'''layernorm_before'''							)
  if "norm2" in name:
    __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		name.replace('''norm2'''       ,			'''layernorm_after'''							)
  if "mlp.fc1" in name:
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		name.replace('''mlp.fc1'''       ,			'''intermediate.dense'''							)
  if "mlp.fc2" in name:
    __SCREAMING_SNAKE_CASE      :	int					=		name.replace('''mlp.fc2'''       ,			'''output.dense'''							)
  if "q_bias" in name:
    __SCREAMING_SNAKE_CASE      :	Dict					=		name.replace('''q_bias'''       ,			'''query.bias'''							)
  if "k_bias" in name:
    __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		name.replace('''k_bias'''       ,			'''key.bias'''							)
  if "v_bias" in name:
    __SCREAMING_SNAKE_CASE      :	Any					=		name.replace('''v_bias'''       ,			'''value.bias'''							)
  if "cpb_mlp" in name:
    __SCREAMING_SNAKE_CASE      :	List[Any]					=		name.replace('''cpb_mlp'''       ,			'''continuous_position_bias_mlp'''							)
  if name == "norm.weight":
    __SCREAMING_SNAKE_CASE      :	Any					=		'''layernorm.weight'''
  if name == "norm.bias":
    __SCREAMING_SNAKE_CASE      :	int					=		'''layernorm.bias'''

  if "head" in name:
    __SCREAMING_SNAKE_CASE      :	str					=		name.replace('''head'''       ,			'''classifier'''							)
  else:
    __SCREAMING_SNAKE_CASE      :	List[str]					=		'''swinv2.''' + name

  return name


def 							a__					(							snake_case       ,			snake_case							):





  """simple docstring"""
  for key in orig_state_dict.copy().keys():
    __SCREAMING_SNAKE_CASE      :	str					=		orig_state_dict.pop(snake_case							)

    if "mask" in key:
      continue
    elif "qkv" in key:
      __SCREAMING_SNAKE_CASE      :	Dict					=		key.split('''.'''							)
      __SCREAMING_SNAKE_CASE      :	List[Any]					=		int(key_split[1]							)
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		int(key_split[3]							)
      __SCREAMING_SNAKE_CASE      :	Optional[int]					=		model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

      if "weight" in key:
        __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		val[:dim, :]
        __SCREAMING_SNAKE_CASE      :	Any					=		val[dim : dim * 2, :]
        __SCREAMING_SNAKE_CASE      :	Dict					=		val[-dim:, :]
      else:
        __SCREAMING_SNAKE_CASE      :	Tuple					=		val[:dim]
        __SCREAMING_SNAKE_CASE      :	Any					=		val[
            dim : dim * 2
        ]
        __SCREAMING_SNAKE_CASE      :	List[str]					=		val[-dim:]
    else:
      __SCREAMING_SNAKE_CASE      :	str					=		val

  return orig_state_dict


def 							a__					(							snake_case       ,			snake_case							):





  """simple docstring"""
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		timm.create_model(snake_case       ,			pretrained=snake_case							)
  timm_model.eval()

  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		get_swinva_config(snake_case							)
  __SCREAMING_SNAKE_CASE      :	Optional[int]					=		SwinvaForImageClassification(snake_case							)
  model.eval()

  __SCREAMING_SNAKE_CASE      :	List[str]					=		convert_state_dict(timm_model.state_dict()       ,			snake_case							)
  model.load_state_dict(snake_case							)

  __SCREAMING_SNAKE_CASE      :	Tuple					=		'''http://images.cocodataset.org/val2017/000000039769.jpg'''

  __SCREAMING_SNAKE_CASE      :	str					=		AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swinva_name.replace('''_'''       ,			'''-'''							)							)							)
  __SCREAMING_SNAKE_CASE      :	Any					=		Image.open(requests.get(snake_case       ,			stream=snake_case							).raw							)
  __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		image_processor(images=snake_case       ,			return_tensors='''pt'''							)

  __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		timm_model(inputs['''pixel_values''']							)
  __SCREAMING_SNAKE_CASE      :	Dict					=		model(**snake_case							).logits

  assert torch.allclose(snake_case       ,			snake_case       ,			atol=1E-3							)

  print(F'''Saving model {swinva_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							)

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


if __name__ == "__main__":
   lowercase_ 				=			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."""
   )

   lowercase_ 				=			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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase				 =					'''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''






def    _snake_case					(		):
	A     = _ask_options(
	    'In which compute environment are you running?'						,   ['This machine', 'AWS (Amazon SageMaker)']						,   _convert_compute_environment						,   )
	if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
		A     = get_sagemaker_input()
	else:
		A     = get_cluster_input()
	return config






def    _snake_case					(		snake_case__ :  Any=None  ):
	if subparsers is not None:
		A     = subparsers.add_parser('config'						,   description=snake_case__  )
	else:
		A     = argparse.ArgumentParser('Accelerate config command'						,   description=snake_case__  )

	parser.add_argument(
	    '--config_file'						,   default=snake_case__						,   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\'.'
	    )						,   )

	if subparsers is not None:
		parser.set_defaults(func=snake_case__  )
	return parser






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = get_user_input()
	if args.config_file is not None:
		A     = args.config_file
	else:
		if not os.path.isdir(snake_case__  ):
			os.makedirs(snake_case__  )
		A     = default_yaml_config_file

	if config_file.endswith('.json'  ):
		config.to_json_file(snake_case__  )
	else:
		config.to_yaml_file(snake_case__  )
	print(F'accelerate configuration saved at {config_file}'  )






def    _snake_case					(		):
	A     = config_command_parser()
	A     = parser.parse_args()
	config_command(snake_case__  )


if __name__ == "__main__":
		main()