





'''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 :    str    )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			SwinvaConfig()
    _SCREAMING_SNAKE_CASE									=			swinva_name.split("""_"""    )

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

    if model_size == "tiny":
        _SCREAMING_SNAKE_CASE									=			96
        _SCREAMING_SNAKE_CASE									=			(2, 2, 6, 2)
        _SCREAMING_SNAKE_CASE									=			(3, 6, 12, 24)
    elif model_size == "small":
        _SCREAMING_SNAKE_CASE									=			96
        _SCREAMING_SNAKE_CASE									=			(2, 2, 18, 2)
        _SCREAMING_SNAKE_CASE									=			(3, 6, 12, 24)
    elif model_size == "base":
        _SCREAMING_SNAKE_CASE									=			128
        _SCREAMING_SNAKE_CASE									=			(2, 2, 18, 2)
        _SCREAMING_SNAKE_CASE									=			(4, 8, 16, 32)
    else:
        _SCREAMING_SNAKE_CASE									=			192
        _SCREAMING_SNAKE_CASE									=			(2, 2, 18, 2)
        _SCREAMING_SNAKE_CASE									=			(6, 12, 24, 48)

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

    if ("22k" in swinva_name) and ("to" not in swinva_name):
        _SCREAMING_SNAKE_CASE									=			2_1841
        _SCREAMING_SNAKE_CASE									=			"""huggingface/label-files"""
        _SCREAMING_SNAKE_CASE									=			"""imagenet-22k-id2label.json"""
        _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )					,       """r"""    )    )
        _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}
        _SCREAMING_SNAKE_CASE									=			idalabel
        _SCREAMING_SNAKE_CASE									=			{v: k for k, v in idalabel.items()}

    else:
        _SCREAMING_SNAKE_CASE									=			1000
        _SCREAMING_SNAKE_CASE									=			"""huggingface/label-files"""
        _SCREAMING_SNAKE_CASE									=			"""imagenet-1k-id2label.json"""
        _SCREAMING_SNAKE_CASE									=			json.load(open(hf_hub_download(__lowerCamelCase					,       __lowerCamelCase					,       repo_type="""dataset"""    )					,       """r"""    )    )
        _SCREAMING_SNAKE_CASE									=			{int(__lowerCamelCase    ): v for k, v in idalabel.items()}
        _SCREAMING_SNAKE_CASE									=			idalabel
        _SCREAMING_SNAKE_CASE									=			{v: k for k, v in idalabel.items()}

    _SCREAMING_SNAKE_CASE									=			img_size
    _SCREAMING_SNAKE_CASE									=			num_classes
    _SCREAMING_SNAKE_CASE									=			embed_dim
    _SCREAMING_SNAKE_CASE									=			depths
    _SCREAMING_SNAKE_CASE									=			num_heads
    _SCREAMING_SNAKE_CASE									=			window_size

    return config





def lowerCamelCase						(     __lowerCamelCase :    int    )						->Union[str, Any]:
    if "patch_embed.proj" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""patch_embed.proj"""					,       """embeddings.patch_embeddings.projection"""    )
    if "patch_embed.norm" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""patch_embed.norm"""					,       """embeddings.norm"""    )
    if "layers" in name:
        _SCREAMING_SNAKE_CASE									=			"""encoder.""" + name
    if "attn.proj" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""attn.proj"""					,       """attention.output.dense"""    )
    if "attn" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""attn"""					,       """attention.self"""    )
    if "norm1" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""norm1"""					,       """layernorm_before"""    )
    if "norm2" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""norm2"""					,       """layernorm_after"""    )
    if "mlp.fc1" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""mlp.fc1"""					,       """intermediate.dense"""    )
    if "mlp.fc2" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""mlp.fc2"""					,       """output.dense"""    )
    if "q_bias" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""q_bias"""					,       """query.bias"""    )
    if "k_bias" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""k_bias"""					,       """key.bias"""    )
    if "v_bias" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""v_bias"""					,       """value.bias"""    )
    if "cpb_mlp" in name:
        _SCREAMING_SNAKE_CASE									=			name.replace("""cpb_mlp"""					,       """continuous_position_bias_mlp"""    )
    if name == "norm.weight":
        _SCREAMING_SNAKE_CASE									=			"""layernorm.weight"""
    if name == "norm.bias":
        _SCREAMING_SNAKE_CASE									=			"""layernorm.bias"""

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

    return name





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    List[Any]    )						->Optional[Any]:
    for key in orig_state_dict.copy().keys():
        _SCREAMING_SNAKE_CASE									=			orig_state_dict.pop(__lowerCamelCase    )

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

            if "weight" in key:
                _SCREAMING_SNAKE_CASE									=			val[:dim, :]
                _SCREAMING_SNAKE_CASE									=			val[dim : dim * 2, :]
                _SCREAMING_SNAKE_CASE									=			val[-dim:, :]
            else:
                _SCREAMING_SNAKE_CASE									=			val[:dim]
                _SCREAMING_SNAKE_CASE									=			val[
                    dim : dim * 2
                ]
                _SCREAMING_SNAKE_CASE									=			val[-dim:]
        else:
            _SCREAMING_SNAKE_CASE									=			val

    return orig_state_dict





def lowerCamelCase						(     __lowerCamelCase :    Union[str, Any]					,       __lowerCamelCase :    List[str]    )						->Tuple:
    _SCREAMING_SNAKE_CASE									=			timm.create_model(__lowerCamelCase					,       pretrained=__lowerCamelCase    )
    timm_model.eval()

    _SCREAMING_SNAKE_CASE									=			get_swinva_config(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			SwinvaForImageClassification(__lowerCamelCase    )
    model.eval()

    _SCREAMING_SNAKE_CASE									=			convert_state_dict(timm_model.state_dict()					,       __lowerCamelCase    )
    model.load_state_dict(__lowerCamelCase    )

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

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

    _SCREAMING_SNAKE_CASE									=			timm_model(inputs["""pixel_values"""]    )
    _SCREAMING_SNAKE_CASE									=			model(**__lowerCamelCase    ).logits

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

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

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

    model.push_to_hub(
        repo_path_or_name=Path(__lowerCamelCase					,       __lowerCamelCase    )					,       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"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_UpperCamelCase		:  Dict						=		{
    'configuration_roberta': ['ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RobertaConfig', 'RobertaOnnxConfig'],
    'tokenization_roberta': ['RobertaTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  str						=		['RobertaTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Tuple						=		[
        'ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'RobertaForCausalLM',
        'RobertaForMaskedLM',
        'RobertaForMultipleChoice',
        'RobertaForQuestionAnswering',
        'RobertaForSequenceClassification',
        'RobertaForTokenClassification',
        'RobertaModel',
        'RobertaPreTrainedModel',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  List[Any]						=		[
        'TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFRobertaForCausalLM',
        'TFRobertaForMaskedLM',
        'TFRobertaForMultipleChoice',
        'TFRobertaForQuestionAnswering',
        'TFRobertaForSequenceClassification',
        'TFRobertaForTokenClassification',
        'TFRobertaMainLayer',
        'TFRobertaModel',
        'TFRobertaPreTrainedModel',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		:  Optional[int]						=		[
        'FlaxRobertaForCausalLM',
        'FlaxRobertaForMaskedLM',
        'FlaxRobertaForMultipleChoice',
        'FlaxRobertaForQuestionAnswering',
        'FlaxRobertaForSequenceClassification',
        'FlaxRobertaForTokenClassification',
        'FlaxRobertaModel',
        'FlaxRobertaPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
    from .tokenization_roberta import RobertaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_roberta_fast import RobertaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta import (
            ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaForCausalLM,
            RobertaForMaskedLM,
            RobertaForMultipleChoice,
            RobertaForQuestionAnswering,
            RobertaForSequenceClassification,
            RobertaForTokenClassification,
            RobertaModel,
            RobertaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta import (
            TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaForCausalLM,
            TFRobertaForMaskedLM,
            TFRobertaForMultipleChoice,
            TFRobertaForQuestionAnswering,
            TFRobertaForSequenceClassification,
            TFRobertaForTokenClassification,
            TFRobertaMainLayer,
            TFRobertaModel,
            TFRobertaPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta import (
            FlaxRobertaForCausalLM,
            FlaxRobertaForMaskedLM,
            FlaxRobertaForMultipleChoice,
            FlaxRobertaForQuestionAnswering,
            FlaxRobertaForSequenceClassification,
            FlaxRobertaForTokenClassification,
            FlaxRobertaModel,
            FlaxRobertaPreTrainedModel,
        )

else:
    import sys

    _UpperCamelCase		:  Optional[Any]						=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


