




'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
A     =logging.get_logger(__name__)





def 						snake_case_							(_a	:							Dict    ,   _a	:							Dict    ,   _a	:							Optional[int]    ,   _a	:							str     ):
    UpperCAmelCase			       =		original_name.split('''.'''     )[0]
    UpperCAmelCase			       =		key.split('''.'''     )
    UpperCAmelCase			       =		int(key_list[key_list.index(__SCREAMING_SNAKE_CASE     ) - 2]     )
    UpperCAmelCase			       =		int(key_list[key_list.index(__SCREAMING_SNAKE_CASE     ) - 1]     )
    UpperCAmelCase			       =		orig_block_num - offset

    UpperCAmelCase			       =		key.replace(F"{orig_block_num}.{layer_num}.{original_name}"    ,   F"block.{new_block_num}.{layer_num}.{new_name}"     )
    return key





def 						snake_case_							(_a	:							Any     ):
    UpperCAmelCase			       =		OrderedDict()
    UpperCAmelCase       ,     UpperCAmelCase			       =		0, 0
    for key, value in state_dict.items():
        if key.startswith('''network'''     ):
            UpperCAmelCase			       =		key.replace('''network'''    ,   '''poolformer.encoder'''     )
        if "proj" in key:
            # Works for the first embedding as well as the internal embedding layers
            if key.endswith('''bias'''     ) and "patch_embed" not in key:
                patch_emb_offset += 1
            UpperCAmelCase			       =		key[: key.find('''proj'''     )]
            UpperCAmelCase			       =		key.replace(__SCREAMING_SNAKE_CASE    ,   F"patch_embeddings.{total_embed_found}."     )
            UpperCAmelCase			       =		key.replace('''proj'''    ,   '''projection'''     )
            if key.endswith('''bias'''     ):
                total_embed_found += 1
        if "patch_embeddings" in key:
            UpperCAmelCase			       =		'''poolformer.encoder.''' + key
        if "mlp.fc1" in key:
            UpperCAmelCase			       =		replace_key_with_offset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   '''mlp.fc1'''    ,   '''output.conv1'''     )
        if "mlp.fc2" in key:
            UpperCAmelCase			       =		replace_key_with_offset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   '''mlp.fc2'''    ,   '''output.conv2'''     )
        if "norm1" in key:
            UpperCAmelCase			       =		replace_key_with_offset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   '''norm1'''    ,   '''before_norm'''     )
        if "norm2" in key:
            UpperCAmelCase			       =		replace_key_with_offset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   '''norm2'''    ,   '''after_norm'''     )
        if "layer_scale_1" in key:
            UpperCAmelCase			       =		replace_key_with_offset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   '''layer_scale_1'''    ,   '''layer_scale_1'''     )
        if "layer_scale_2" in key:
            UpperCAmelCase			       =		replace_key_with_offset(__SCREAMING_SNAKE_CASE    ,   __SCREAMING_SNAKE_CASE    ,   '''layer_scale_2'''    ,   '''layer_scale_2'''     )
        if "head" in key:
            UpperCAmelCase			       =		key.replace('''head'''    ,   '''classifier'''     )
        UpperCAmelCase			       =		value
    return new_state_dict





def 						snake_case_							():
    UpperCAmelCase			       =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase			       =		Image.open(requests.get(__SCREAMING_SNAKE_CASE    ,   stream=__SCREAMING_SNAKE_CASE     ).raw     )

    return image





@torch.no_grad()
def 						snake_case_							(_a	:							Tuple    ,   _a	:							Any    ,   _a	:							str     ):
    UpperCAmelCase			       =		PoolFormerConfig()

    # set attributes based on model_name
    UpperCAmelCase			       =		'''huggingface/label-files'''
    UpperCAmelCase			       =		model_name[-3:]
    UpperCAmelCase			       =		1_0_0_0
    UpperCAmelCase			       =		'''imagenet-1k-id2label.json'''
    UpperCAmelCase			       =		(1, 1_0_0_0)

    # set config attributes
    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()}
    if size == "s12":
        UpperCAmelCase			       =		[2, 2, 6, 2]
        UpperCAmelCase			       =		[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCAmelCase			       =		4.0
        UpperCAmelCase			       =		0.9
    elif size == "s24":
        UpperCAmelCase			       =		[4, 4, 1_2, 4]
        UpperCAmelCase			       =		[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCAmelCase			       =		4.0
        UpperCAmelCase			       =		0.9
    elif size == "s36":
        UpperCAmelCase			       =		[6, 6, 1_8, 6]
        UpperCAmelCase			       =		[6_4, 1_2_8, 3_2_0, 5_1_2]
        UpperCAmelCase			       =		4.0
        UpperCAmelCase			       =		1E-6
        UpperCAmelCase			       =		0.9
    elif size == "m36":
        UpperCAmelCase			       =		[6, 6, 1_8, 6]
        UpperCAmelCase			       =		[9_6, 1_9_2, 3_8_4, 7_6_8]
        UpperCAmelCase			       =		4.0
        UpperCAmelCase			       =		1E-6
        UpperCAmelCase			       =		0.95
    elif size == "m48":
        UpperCAmelCase			       =		[8, 8, 2_4, 8]
        UpperCAmelCase			       =		[9_6, 1_9_2, 3_8_4, 7_6_8]
        UpperCAmelCase			       =		4.0
        UpperCAmelCase			       =		1E-6
        UpperCAmelCase			       =		0.95
    else:
        raise ValueError(F"Size {size} not supported"     )

    # load image processor
    UpperCAmelCase			       =		PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE     )

    # Prepare image
    UpperCAmelCase			       =		prepare_img()
    UpperCAmelCase			       =		image_processor(images=__SCREAMING_SNAKE_CASE    ,   return_tensors='''pt'''     ).pixel_values

    logger.info(F"Converting model {model_name}..."     )

    # load original state dict
    UpperCAmelCase			       =		torch.load(__SCREAMING_SNAKE_CASE    ,   map_location=torch.device('''cpu'''     )     )

    # rename keys
    UpperCAmelCase			       =		rename_keys(__SCREAMING_SNAKE_CASE     )

    # create HuggingFace model and load state dict
    UpperCAmelCase			       =		PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE     )
    model.load_state_dict(__SCREAMING_SNAKE_CASE     )
    model.eval()

    # Define image processor
    UpperCAmelCase			       =		PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE     )
    UpperCAmelCase			       =		image_processor(images=prepare_img()    ,   return_tensors='''pt'''     ).pixel_values

    # forward pass
    UpperCAmelCase			       =		model(__SCREAMING_SNAKE_CASE     )
    UpperCAmelCase			       =		outputs.logits

    # define expected logit slices for different models
    if size == "s12":
        UpperCAmelCase			       =		torch.tensor([-0.3045, -0.6758, -0.4869]     )
    elif size == "s24":
        UpperCAmelCase			       =		torch.tensor([0.4402, -0.1374, -0.8045]     )
    elif size == "s36":
        UpperCAmelCase			       =		torch.tensor([-0.6080, -0.5133, -0.5898]     )
    elif size == "m36":
        UpperCAmelCase			       =		torch.tensor([0.3952, 0.2263, -1.2668]     )
    elif size == "m48":
        UpperCAmelCase			       =		torch.tensor([0.1167, -0.0656, -0.3423]     )
    else:
        raise ValueError(F"Size {size} not supported"     )

    # verify logits
    assert logits.shape == expected_shape
    assert torch.allclose(logits[0, :3]    ,   __SCREAMING_SNAKE_CASE    ,   atol=1E-2     )

    # finally, save model and image processor
    logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."     )
    Path(__SCREAMING_SNAKE_CASE     ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE     )
    model.save_pretrained(__SCREAMING_SNAKE_CASE     )
    print(F"Saving image processor to {pytorch_dump_folder_path}"     )
    image_processor.save_pretrained(__SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
   A     =argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       default='poolformer_s12',
       type=str,
       help='Name of the model you\'d like to convert.',
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   A     =parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, 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 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE="no" ,							__SCREAMING_SNAKE_CASE = default_json_config_file ,							__SCREAMING_SNAKE_CASE = False							):
	lowercase       =    Path(__SCREAMING_SNAKE_CASE							)
	path.parent.mkdir(parents=__SCREAMING_SNAKE_CASE ,							exist_ok=__SCREAMING_SNAKE_CASE							)
	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
	lowercase       =    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}'''							)
	lowercase       =    {
	    'compute_environment': 'LOCAL_MACHINE',
	    'mixed_precision': mixed_precision,
	}
	if torch.cuda.is_available():
		lowercase       =    torch.cuda.device_count()
		lowercase       =    num_gpus
		lowercase       =    False
		if num_gpus > 1:
			lowercase       =    'MULTI_GPU'
		else:
			lowercase       =    'NO'
	elif is_xpu_available() and use_xpu:
		lowercase       =    torch.xpu.device_count()
		lowercase       =    num_xpus
		lowercase       =    False
		if num_xpus > 1:
			lowercase       =    'MULTI_XPU'
		else:
			lowercase       =    'NO'
	elif is_npu_available():
		lowercase       =    torch.npu.device_count()
		lowercase       =    num_npus
		lowercase       =    False
		if num_npus > 1:
			lowercase       =    'MULTI_NPU'
		else:
			lowercase       =    'NO'
	else:
		lowercase       =    0
		lowercase       =    True
		lowercase       =    1
		lowercase       =    'NO'
	lowercase       =    ClusterConfig(**__SCREAMING_SNAKE_CASE							)
	config.to_json_file(__SCREAMING_SNAKE_CASE							)
	return path






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    parser.add_parser('default' ,							parents=__SCREAMING_SNAKE_CASE ,							help=__SCREAMING_SNAKE_CASE ,							formatter_class=__SCREAMING_SNAKE_CASE							)
	parser.add_argument(
	    '--config_file' ,							default=__SCREAMING_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\'.'
	    ) ,							dest='save_location' ,							)

	parser.add_argument(
	    '--mixed_precision' ,							choices=['no', 'fp16', 'bf16'] ,							type=__SCREAMING_SNAKE_CASE ,							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=__SCREAMING_SNAKE_CASE							)
	return parser






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

