



"""simple docstring"""


import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
					import numpy as np
					import torch

					from . import (
					    AlbertForPreTraining,
					    BartForConditionalGeneration,
					    BertForPreTraining,
					    BertForQuestionAnswering,
					    BertForSequenceClassification,
					    CamembertForMaskedLM,
					    CTRLLMHeadModel,
					    DistilBertForMaskedLM,
					    DistilBertForQuestionAnswering,
					    DPRContextEncoder,
					    DPRQuestionEncoder,
					    DPRReader,
					    ElectraForPreTraining,
					    FlaubertWithLMHeadModel,
					    GPTaLMHeadModel,
					    LayoutLMForMaskedLM,
					    LxmertForPreTraining,
					    LxmertVisualFeatureEncoder,
					    OpenAIGPTLMHeadModel,
					    RobertaForMaskedLM,
					    RobertaForSequenceClassification,
					    TaForConditionalGeneration,
					    TransfoXLLMHeadModel,
					    XLMRobertaForMaskedLM,
					    XLMWithLMHeadModel,
					    XLNetLMHeadModel,
					)


logging.set_verbosity_info()

__A   :							Tuple													=   {
    """bart""": (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """bert""": (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-uncased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-large-cased-whole-word-masking-finetuned-squad""": (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """bert-base-cased-finetuned-mrpc""": (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """dpr""": (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """gpt2""": (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlnet""": (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm""": (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """xlm-roberta""": (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """transfo-xl""": (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """openai-gpt""": (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """roberta""": (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """layoutlm""": (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    """roberta-large-mnli""": (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """camembert""": (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """flaubert""": (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert""": (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """distilbert-base-distilled-squad""": (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert""": (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """lxmert-visual-feature-encoder""": (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """ctrl""": (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """albert""": (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """t5""": (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """electra""": (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    """wav2vec2""": (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						Any=False					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]=True				):






				'''simple docstring'''



				if model_type not in MODEL_CLASSES:
								raise ValueError(f'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()				)}.'				)

				_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    MODEL_CLASSES[model_type]

				# Initialise TF model
				if config_file in aws_config_map:
								_UpperCAmelCase					=    cached_file(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     force_download=not use_cached_models				)
				_UpperCAmelCase					=    config_class.from_json_file(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    True
				_UpperCAmelCase					=    True
				print(f'Building TensorFlow model from configuration: {config}'				)
				_UpperCAmelCase					=    model_class(_SCREAMING_SNAKE_CASE				)

				# Load weights from tf checkpoint
				if pytorch_checkpoint_path in aws_config_map.keys():
								_UpperCAmelCase					=    cached_file(
								    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     force_download=not use_cached_models				)
				# Load PyTorch checkpoint in tf2 model:
				_UpperCAmelCase					=    load_pytorch_checkpoint_in_tfa_model(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				if compare_with_pt_model:
								_UpperCAmelCase					=    tf_model(tf_model.dummy_inputs					,     training=_SCREAMING_SNAKE_CASE				)  # build the network

								_UpperCAmelCase					=    torch.load(_SCREAMING_SNAKE_CASE					,     map_location='''cpu'''				)
								_UpperCAmelCase					=    pt_model_class.from_pretrained(
								    pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE					,     config=_SCREAMING_SNAKE_CASE					,     state_dict=_SCREAMING_SNAKE_CASE				)

								with torch.no_grad():
												_UpperCAmelCase					=    pt_model(**pt_model.dummy_inputs				)

								_UpperCAmelCase					=    pto[0].numpy()
								_UpperCAmelCase					=    tfo[0].numpy()
								_UpperCAmelCase					=    np.amax(np.abs(np_pt - np_tf				)				)
								print(f'Max absolute difference between models outputs {diff}'				)
								assert diff <= 2E-2, f'Error, model absolute difference is >2e-2: {diff}'

				# Save pytorch-model
				print(f'Save TensorFlow model to {tf_dump_path}'				)
				tf_model.save_weights(_SCREAMING_SNAKE_CASE					,     save_format='''h5'''				)







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						List[Any]					,     _SCREAMING_SNAKE_CASE   :						Tuple					,     _SCREAMING_SNAKE_CASE   :						List[Any]=None					,     _SCREAMING_SNAKE_CASE   :						Union[str, Any]=None					,     _SCREAMING_SNAKE_CASE   :						str=False					,     _SCREAMING_SNAKE_CASE   :						Dict=False					,     _SCREAMING_SNAKE_CASE   :						Optional[Any]=False					,     _SCREAMING_SNAKE_CASE   :						Tuple=False					,     ):






				'''simple docstring'''



				if args_model_type is None:
								_UpperCAmelCase					=    list(MODEL_CLASSES.keys()				)
				else:
								_UpperCAmelCase					=    [args_model_type]

				for j, model_type in enumerate(_SCREAMING_SNAKE_CASE					,     start=1				):
								print('''=''' * 100				)
								print(f' Converting model type {j}/{len(_SCREAMING_SNAKE_CASE				)}: {model_type}'				)
								print('''=''' * 100				)
								if model_type not in MODEL_CLASSES:
												raise ValueError(f'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()				)}.'				)

								_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    MODEL_CLASSES[model_type]

								if model_shortcut_names_or_path is None:
												_UpperCAmelCase					=    list(aws_model_maps.keys()				)
								if config_shortcut_names_or_path is None:
												_UpperCAmelCase					=    model_shortcut_names_or_path

								for i, (model_shortcut_name, config_shortcut_name) in enumerate(
								    zip(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)					,     start=1				):
												print('''-''' * 100				)
												if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
																if not only_convert_finetuned_models:
																				print(f'    Skipping finetuned checkpoint {model_shortcut_name}'				)
																				continue
																_UpperCAmelCase					=    model_shortcut_name
												elif only_convert_finetuned_models:
																print(f'    Skipping not finetuned checkpoint {model_shortcut_name}'				)
																continue
												print(
												    f'    Converting checkpoint {i}/{len(_SCREAMING_SNAKE_CASE				)}: {model_shortcut_name} - model_type {model_type}'				)
												print('''-''' * 100				)

												if config_shortcut_name in aws_config_map:
																_UpperCAmelCase					=    cached_file(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     force_download=not use_cached_models				)
												else:
																_UpperCAmelCase					=    config_shortcut_name

												if model_shortcut_name in aws_model_maps:
																_UpperCAmelCase					=    cached_file(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     force_download=not use_cached_models				)
												else:
																_UpperCAmelCase					=    model_shortcut_name

												if os.path.isfile(_SCREAMING_SNAKE_CASE				):
																_UpperCAmelCase					=    '''converted_model'''

												convert_pt_checkpoint_to_tf(
												    model_type=_SCREAMING_SNAKE_CASE					,     pytorch_checkpoint_path=_SCREAMING_SNAKE_CASE					,     config_file=_SCREAMING_SNAKE_CASE					,     tf_dump_path=os.path.join(_SCREAMING_SNAKE_CASE					,     model_shortcut_name + '''-tf_model.h5'''				)					,     compare_with_pt_model=_SCREAMING_SNAKE_CASE					,     )
												if remove_cached_files:
																os.remove(_SCREAMING_SNAKE_CASE				)
																os.remove(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							List[Any]													=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--tf_dump_path", default=None, type=str, required=True, help="Path to the output Tensorflow dump file."
					)
					parser.add_argument(
					    "--model_type",
					    default=None,
					    type=str,
					    help=(
					        f'''Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and '''
					        "convert all the models from AWS."
					    ),
					)
					parser.add_argument(
					    "--pytorch_checkpoint_path",
					    default=None,
					    type=str,
					    help=(
					        "Path to the PyTorch checkpoint path or shortcut name to download from AWS. "
					        "If not given, will download and convert all the checkpoints from AWS."
					    ),
					)
					parser.add_argument(
					    "--config_file",
					    default=None,
					    type=str,
					    help=(
					        "The config json file corresponding to the pre-trained model. \n"
					        "This specifies the model architecture. If not given and "
					        "--pytorch_checkpoint_path is not given or is a shortcut name "
					        "use the configuration associated to the shortcut name on the AWS"
					    ),
					)
					parser.add_argument(
					    "--compare_with_pt_model", action="store_true", help="Compare Tensorflow and PyTorch model predictions."
					)
					parser.add_argument(
					    "--use_cached_models",
					    action="store_true",
					    help="Use cached models if possible instead of updating to latest checkpoint versions.",
					)
					parser.add_argument(
					    "--remove_cached_files",
					    action="store_true",
					    help="Remove pytorch models after conversion (save memory when converting in batches).",
					)
					parser.add_argument("--only_convert_finetuned_models", action="store_true", help="Only convert finetuned models.")
					__A   :							List[str]													=   parser.parse_args()

					# if args.pytorch_checkpoint_path is not None:
					#     convert_pt_checkpoint_to_tf(args.model_type.lower(),
					#                                 args.pytorch_checkpoint_path,
					#                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
					#                                 args.tf_dump_path,
					#                                 compare_with_pt_model=args.compare_with_pt_model,
					#                                 use_cached_models=args.use_cached_models)
					# else:
					convert_all_pt_checkpoints_to_tf(
					    args.model_type.lower() if args.model_type is not None else None,
					    args.tf_dump_path,
					    model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
					    if args.pytorch_checkpoint_path is not None
					    else None,
					    config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
					    compare_with_pt_model=args.compare_with_pt_model,
					    use_cached_models=args.use_cached_models,
					    remove_cached_files=args.remove_cached_files,
					    only_convert_finetuned_models=args.only_convert_finetuned_models,
					)








"""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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''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					=    128
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (4, 8, 16, 32)
				else:
								_UpperCAmelCase					=    192
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (6, 12, 24, 48)

				if "in22k" in swin_name:
								_UpperCAmelCase					=    2_1841
				else:
								_UpperCAmelCase					=    1000
								_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  lowercase	(      _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  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''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  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _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   :							int										=   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)



