






"""simple docstring"""





import logging
import os
from typing import Dict, List, Optional, Union

import torch
import torch.nn as nn

from accelerate.utils.imports import (
    is_abit_bnb_available,
    is_abit_bnb_available,
    is_bnb_available,
)

from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
    find_tied_parameters,
    get_balanced_memory,
    infer_auto_device_map,
    load_checkpoint_in_model,
    offload_weight,
    set_module_tensor_to_device,
)


if is_bnb_available():
      import bitsandbytes as bnb

from copy import deepcopy


A__					: Optional[int]=	logging.getLogger(__name__)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = None ,	SCREAMING_SNAKE_CASE = False ,	)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    bnb_quantization_config.load_in_abit
    UpperCamelCase__         =    bnb_quantization_config.load_in_abit

    if load_in_abit and not is_abit_bnb_available():
        raise ImportError(
            'You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'
            ' make sure you have the latest version of `bitsandbytes` installed.'					)
    if load_in_abit and not is_abit_bnb_available():
        raise ValueError(
            'You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'
            'make sure you have the latest version of `bitsandbytes` installed.'					)

    UpperCamelCase__         =    []
    # custom device map
    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					) and len(device_map.keys()					) > 1:
        UpperCamelCase__         =    [key for key, value in device_map.items() if value in ['disk', 'cpu']]

    # We keep some modules such as the lm_head in their original dtype for numerical stability reasons
    if bnb_quantization_config.skip_modules is None:
        UpperCamelCase__         =    get_keys_to_not_convert(SCREAMING_SNAKE_CASE					)

    # add cpu modules to skip modules only for 4-bit modules
    if load_in_abit:
        bnb_quantization_config.skip_modules.extend(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    bnb_quantization_config.skip_modules

    # We add the modules we want to keep in full precision
    if bnb_quantization_config.keep_in_fpaa_modules is None:
        UpperCamelCase__         =    []
    UpperCamelCase__         =    bnb_quantization_config.keep_in_fpaa_modules
    modules_to_not_convert.extend(SCREAMING_SNAKE_CASE					)

    # compatibility with peft
    UpperCamelCase__         =    load_in_abit
    UpperCamelCase__         =    load_in_abit

    UpperCamelCase__         =    get_parameter_device(SCREAMING_SNAKE_CASE					)
    if model_device.type != "meta":
        # quantization of an already loaded model
        logger.warning(
            'It is not recommended to quantize a loaded model. '
            'The model should be instantiated under the `init_empty_weights` context manager.'					)
        UpperCamelCase__         =    replace_with_bnb_layers(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	modules_to_not_convert=SCREAMING_SNAKE_CASE					)
        # convert param to the right dtype
        UpperCamelCase__         =    bnb_quantization_config.torch_dtype
        for name, param in model.state_dict().items():
            if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules					):
                param.to(torch.floataa					)
                if param.dtype != torch.floataa:
                    UpperCamelCase__         =    name.replace('.weight' ,	''					).replace('.bias' ,	''					)
                    UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
                    if param is not None:
                        param.to(torch.floataa					)
            elif torch.is_floating_point(SCREAMING_SNAKE_CASE					):
                param.to(SCREAMING_SNAKE_CASE					)
        if model_device.type == "cuda":
            # move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
            model.cuda(torch.cuda.current_device()					)
            torch.cuda.empty_cache()
        elif torch.cuda.is_available():
            model.to(torch.cuda.current_device()					)
        else:
            raise RuntimeError('No GPU found. A GPU is needed for quantization.'					)
        logger.info(
            F'The model device type is {model_device.type}. However, cuda is needed for quantization.'
            'We move the model to cuda.'					)
        return model

    elif weights_location is None:
        raise RuntimeError(
            F'`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} '					)

    else:
        with init_empty_weights():
            UpperCamelCase__         =    replace_with_bnb_layers(
                SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	modules_to_not_convert=SCREAMING_SNAKE_CASE					)

        UpperCamelCase__         =    get_quantized_model_device_map(
            SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	max_memory=SCREAMING_SNAKE_CASE ,	no_split_module_classes=SCREAMING_SNAKE_CASE ,	)
        if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
            UpperCamelCase__         =    True

        UpperCamelCase__         =    any(x in list(device_map.values()					) for x in ['cpu', 'disk']					)

        load_checkpoint_in_model(
            SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	dtype=bnb_quantization_config.torch_dtype ,	offload_folder=SCREAMING_SNAKE_CASE ,	offload_state_dict=SCREAMING_SNAKE_CASE ,	keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules ,	offload_abit_bnb=load_in_abit and offload ,	)
        return dispatch_model(SCREAMING_SNAKE_CASE ,	device_map=SCREAMING_SNAKE_CASE ,	offload_dir=SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None					)					->	List[str]:
    """simple docstring"""

    if device_map is None:
        if torch.cuda.is_available():
            UpperCamelCase__         =    {'': torch.cuda.current_device()}
        else:
            raise RuntimeError('No GPU found. A GPU is needed for quantization.'					)
        logger.info('The device_map was not initialized.' 'Setting device_map to `{\'\':torch.cuda.current_device()}`.'					)

    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
            raise ValueError(
                'If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '
                '\'sequential\'.'					)

        UpperCamelCase__         =    {}
        special_dtypes.update(
            {
                name: bnb_quantization_config.torch_dtype
                for name, _ in model.named_parameters()
                if any(m in name for m in bnb_quantization_config.skip_modules					)
            }					)
        special_dtypes.update(
            {
                name: torch.floataa
                for name, _ in model.named_parameters()
                if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules					)
            }					)

        UpperCamelCase__         =    {}
        UpperCamelCase__         =    special_dtypes
        UpperCamelCase__         =    no_split_module_classes
        UpperCamelCase__         =    bnb_quantization_config.target_dtype

        # get max_memory for each device.
        if device_map != "sequential":
            UpperCamelCase__         =    get_balanced_memory(
                SCREAMING_SNAKE_CASE ,	low_zero=(device_map == 'balanced_low_0') ,	max_memory=SCREAMING_SNAKE_CASE ,	**SCREAMING_SNAKE_CASE ,	)

        UpperCamelCase__         =    max_memory
        UpperCamelCase__         =    infer_auto_device_map(SCREAMING_SNAKE_CASE ,	**SCREAMING_SNAKE_CASE					)

    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        # check if don't have any quantized module on the cpu
        UpperCamelCase__         =    bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules

        UpperCamelCase__         =    {
            key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
        }
        for device in ["cpu", "disk"]:
            if device in device_map_without_some_modules.values():
                if bnb_quantization_config.load_in_abit:
                    raise ValueError(
                        '\n                        Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit\n                        the quantized model. If you want to dispatch the model on the CPU or the disk while keeping\n                        these modules in `torch_dtype`, you need to pass a custom `device_map` to\n                        `load_and_quantize_model`. Check\n                        https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk\n                        for more details.\n                        '					)
                else:
                    logger.info(
                        'Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit'					)
        del device_map_without_some_modules
    return device_map







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None					)					->	List[Any]:
    """simple docstring"""

    if modules_to_not_convert is None:
        UpperCamelCase__         =    []

    UpperCamelCase__       ,						UpperCamelCase__         =    _replace_with_bnb_layers(
        SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    if not has_been_replaced:
        logger.warning(
            'You are loading your model in 8bit or 4bit but no linear modules were found in your model.'
            ' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'
            ' Please double check your model architecture, or submit an issue on github if you think this is'
            ' a bug.'					)
    return model







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None ,	)					->	List[str]:
    """simple docstring"""

    UpperCamelCase__         =    False
    for name, module in model.named_children():
        if current_key_name is None:
            UpperCamelCase__         =    []
        current_key_name.append(SCREAMING_SNAKE_CASE					)
        if isinstance(SCREAMING_SNAKE_CASE ,	nn.Linear					) and name not in modules_to_not_convert:
            # Check if the current key is not in the `modules_to_not_convert`
            UpperCamelCase__         =    '.'.join(SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    True
            for key in modules_to_not_convert:
                if (
                    (key in current_key_name_str) and (key + "." in current_key_name_str)
                ) or key == current_key_name_str:
                    UpperCamelCase__         =    False
                    break
            if proceed:
                # Load bnb module with empty weight and replace ``nn.Linear` module
                if bnb_quantization_config.load_in_abit:
                    UpperCamelCase__         =    bnb.nn.LinearabitLt(
                        module.in_features ,	module.out_features ,	module.bias is not None ,	has_fpaa_weights=SCREAMING_SNAKE_CASE ,	threshold=bnb_quantization_config.llm_inta_threshold ,	)
                elif bnb_quantization_config.load_in_abit:
                    UpperCamelCase__         =    bnb.nn.Linearabit(
                        module.in_features ,	module.out_features ,	module.bias is not None ,	bnb_quantization_config.bnb_abit_compute_dtype ,	compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant ,	quant_type=bnb_quantization_config.bnb_abit_quant_type ,	)
                else:
                    raise ValueError('load_in_8bit and load_in_4bit can\'t be both False'					)
                UpperCamelCase__         =    module.weight.data
                if module.bias is not None:
                    UpperCamelCase__         =    module.bias.data
                bnb_module.requires_grad_(SCREAMING_SNAKE_CASE					)
                setattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
                UpperCamelCase__         =    True
        if len(list(module.children()					)					) > 0:
            UpperCamelCase__       ,						UpperCamelCase__         =    _replace_with_bnb_layers(
                SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    has_been_replaced | _has_been_replaced
        # Remove the last key for recursion
        current_key_name.pop(-1					)
    return model, has_been_replaced







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    with init_empty_weights():
        UpperCamelCase__         =    deepcopy(SCREAMING_SNAKE_CASE					)  # this has 0 cost since it is done inside `init_empty_weights` context manager`

    UpperCamelCase__         =    find_tied_parameters(SCREAMING_SNAKE_CASE					)
    # For compatibility with Accelerate < 0.18
    if isinstance(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					):
        UpperCamelCase__         =    sum(list(tied_params.values()					) ,	[]					) + list(tied_params.keys()					)
    else:
        UpperCamelCase__         =    sum(SCREAMING_SNAKE_CASE ,	[]					)
    UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					) > 0

    # Check if it is a base model
    UpperCamelCase__         =    False
    if hasattr(SCREAMING_SNAKE_CASE ,	'base_model_prefix'					):
        UpperCamelCase__         =    not hasattr(SCREAMING_SNAKE_CASE ,	model.base_model_prefix					)

    # Ignore this for base models (BertModel, GPT2Model, etc.)
    if (not has_tied_params) and is_base_model:
        return []

    # otherwise they have an attached head
    UpperCamelCase__         =    list(model.named_children()					)
    UpperCamelCase__         =    [list_modules[-1][0]]

    # add last module together with tied weights
    UpperCamelCase__         =    set(SCREAMING_SNAKE_CASE					) - set(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    list(set(SCREAMING_SNAKE_CASE					)					) + list(SCREAMING_SNAKE_CASE					)

    # remove ".weight" from the keys
    UpperCamelCase__         =    ['.weight', '.bias']
    UpperCamelCase__         =    []
    for name in list_untouched:
        for name_to_remove in names_to_remove:
            if name_to_remove in name:
                UpperCamelCase__         =    name.replace(SCREAMING_SNAKE_CASE ,	''					)
        filtered_module_names.append(SCREAMING_SNAKE_CASE					)

    return filtered_module_names







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    for m in model.modules():
        if isinstance(SCREAMING_SNAKE_CASE ,	bnb.nn.Linearabit					):
            return True
    return False







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    return next(parameter.parameters()					).device







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    if fpaa_statistics is None:
        set_module_tensor_to_device(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	0 ,	dtype=SCREAMING_SNAKE_CASE ,	value=SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    param_name
        UpperCamelCase__         =    model
        if "." in tensor_name:
            UpperCamelCase__         =    tensor_name.split('.'					)
            for split in splits[:-1]:
                UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
                if new_module is None:
                    raise ValueError(F'{module} has no attribute {split}.'					)
                UpperCamelCase__         =    new_module
            UpperCamelCase__         =    splits[-1]
        # offload weights
        UpperCamelCase__         =    False
        offload_weight(module._parameters[tensor_name] ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	index=SCREAMING_SNAKE_CASE					)
        if hasattr(module._parameters[tensor_name] ,	'SCB'					):
            offload_weight(
                module._parameters[tensor_name].SCB ,	param_name.replace('weight' ,	'SCB'					) ,	SCREAMING_SNAKE_CASE ,	index=SCREAMING_SNAKE_CASE ,	)
    else:
        offload_weight(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	index=SCREAMING_SNAKE_CASE					)
        offload_weight(SCREAMING_SNAKE_CASE ,	param_name.replace('weight' ,	'SCB'					) ,	SCREAMING_SNAKE_CASE ,	index=SCREAMING_SNAKE_CASE					)

    set_module_tensor_to_device(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	'meta' ,	dtype=SCREAMING_SNAKE_CASE ,	value=torch.empty(*param.size()					)					)












"""simple docstring"""





import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    SwinConfig()
    UpperCamelCase__         =    swin_name.split('_'					)

    UpperCamelCase__         =    name_split[1]
    UpperCamelCase__         =    int(name_split[4]					)
    UpperCamelCase__         =    int(name_split[3][-1]					)

    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 "in22k" in swin_name:
        UpperCamelCase__         =    2_18_41
    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_( SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    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 name == "norm.weight":
        UpperCamelCase__         =    'layernorm.weight'
    if name == "norm.bias":
        UpperCamelCase__         =    'layernorm.bias'

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

    return name







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    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.swin.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_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    timm.create_model(SCREAMING_SNAKE_CASE ,	pretrained=SCREAMING_SNAKE_CASE					)
    timm_model.eval()

    UpperCamelCase__         =    get_swin_config(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    SwinForImageClassification(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(swin_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 {swin_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					)


if __name__ == "__main__":
  A__					: Optional[Any]=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--swin_name""",
      default="""swin_tiny_patch4_window7_224""",
      type=str,
      help="""Name of the Swin 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__					: Tuple=	parser.parse_args()
  convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)




