


"""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       (     _UpperCamelCase		:      Optional[Any]							)		->       int:

							'''simple docstring'''



							__UpperCAmelCase   :						Any      							=					SwinvaConfig()
							__UpperCAmelCase   :						int      							=					swinva_name.split("""_"""							)

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

							if model_size == "tiny":
														__UpperCAmelCase   :						Optional[Any]      							=					9_6
														__UpperCAmelCase   :						Any      							=					(2, 2, 6, 2)
														__UpperCAmelCase   :						Tuple      							=					(3, 6, 1_2, 2_4)
							elif model_size == "small":
														__UpperCAmelCase   :						List[Any]      							=					9_6
														__UpperCAmelCase   :						Optional[Any]      							=					(2, 2, 1_8, 2)
														__UpperCAmelCase   :						str      							=					(3, 6, 1_2, 2_4)
							elif model_size == "base":
														__UpperCAmelCase   :						Optional[Any]      							=					1_2_8
														__UpperCAmelCase   :						Union[str, Any]      							=					(2, 2, 1_8, 2)
														__UpperCAmelCase   :						str      							=					(4, 8, 1_6, 3_2)
							else:
														__UpperCAmelCase   :						Any      							=					1_9_2
														__UpperCAmelCase   :						Tuple      							=					(2, 2, 1_8, 2)
														__UpperCAmelCase   :						int      							=					(6, 1_2, 2_4, 4_8)

							if "to" in swinva_name:
														__UpperCAmelCase   :						Tuple      							=					(1_2, 1_2, 1_2, 6)

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														__UpperCAmelCase   :						List[Any]      							=					2_1_8_4_1
														__UpperCAmelCase   :						Optional[Any]      							=					"""huggingface/label-files"""
														__UpperCAmelCase   :						Optional[int]      							=					"""imagenet-22k-id2label.json"""
														__UpperCAmelCase   :						Tuple      							=					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="""dataset"""							)       ,			"""r"""							)							)
														__UpperCAmelCase   :						List[Any]      							=					{int(_UpperCamelCase							): v for k, v in idalabel.items()}
														__UpperCAmelCase   :						Tuple      							=					idalabel
														__UpperCAmelCase   :						List[str]      							=					{v: k for k, v in idalabel.items()}

							else:
														__UpperCAmelCase   :						int      							=					1_0_0_0
														__UpperCAmelCase   :						int      							=					"""huggingface/label-files"""
														__UpperCAmelCase   :						List[str]      							=					"""imagenet-1k-id2label.json"""
														__UpperCAmelCase   :						str      							=					json.load(open(hf_hub_download(_UpperCamelCase       ,			_UpperCamelCase       ,			repo_type="""dataset"""							)       ,			"""r"""							)							)
														__UpperCAmelCase   :						Dict      							=					{int(_UpperCamelCase							): v for k, v in idalabel.items()}
														__UpperCAmelCase   :						Tuple      							=					idalabel
														__UpperCAmelCase   :						Union[str, Any]      							=					{v: k for k, v in idalabel.items()}

							__UpperCAmelCase   :						Any      							=					img_size
							__UpperCAmelCase   :						Optional[int]      							=					num_classes
							__UpperCAmelCase   :						int      							=					embed_dim
							__UpperCAmelCase   :						Optional[Any]      							=					depths
							__UpperCAmelCase   :						Any      							=					num_heads
							__UpperCAmelCase   :						List[str]      							=					window_size

							return config
def      lowerCamelCase       (     _UpperCamelCase		:      Dict							)		->       List[Any]:

							'''simple docstring'''



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

							if "head" in name:
														__UpperCAmelCase   :						List[Any]      							=					name.replace("""head"""       ,			"""classifier"""							)
							else:
														__UpperCAmelCase   :						Any      							=					"""swinv2.""" + name

							return name
def      lowerCamelCase       (     _UpperCamelCase		:      Any       ,			_UpperCamelCase		:      Any							)		->       Optional[Any]:

							'''simple docstring'''



							for key in orig_state_dict.copy().keys():
														__UpperCAmelCase   :						List[Any]      							=					orig_state_dict.pop(_UpperCamelCase							)

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

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

							return orig_state_dict
def      lowerCamelCase       (     _UpperCamelCase		:      Optional[int]       ,			_UpperCamelCase		:      Dict							)		->       Tuple:

							'''simple docstring'''



							__UpperCAmelCase   :						List[Any]      							=					timm.create_model(_UpperCamelCase       ,			pretrained=_UpperCamelCase							)
							timm_model.eval()

							__UpperCAmelCase   :						Tuple      							=					get_swinva_config(_UpperCamelCase							)
							__UpperCAmelCase   :						Dict      							=					SwinvaForImageClassification(_UpperCamelCase							)
							model.eval()

							__UpperCAmelCase   :						Dict      							=					convert_state_dict(timm_model.state_dict()       ,			_UpperCamelCase							)
							model.load_state_dict(_UpperCamelCase							)

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

							__UpperCAmelCase   :						Tuple      							=					AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""       ,			"""-"""							)							)							)
							__UpperCAmelCase   :						Union[str, Any]      							=					Image.open(requests.get(_UpperCamelCase       ,			stream=_UpperCamelCase							).raw							)
							__UpperCAmelCase   :						Optional[int]      							=					image_processor(images=_UpperCamelCase       ,			return_tensors="""pt"""							)

							__UpperCAmelCase   :						List[Any]      							=					timm_model(inputs["""pixel_values"""]							)
							__UpperCAmelCase   :						Optional[int]      							=					model(**_UpperCamelCase							).logits

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

							print(f'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''							)
							model.save_pretrained(_UpperCamelCase							)

							print(f'''Saving image processor to {pytorch_dump_folder_path}'''							)
							image_processor.save_pretrained(_UpperCamelCase							)

							model.push_to_hub(
							    repo_path_or_name=Path(_UpperCamelCase       ,			_UpperCamelCase							)       ,			organization="""nandwalritik"""       ,			commit_message="""Add model"""       ,			)


if __name__ == "__main__":
							UpperCAmelCase    :     Optional[int]   						=		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.'
							)

							UpperCAmelCase    :     List[Any]   						=		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    :     Any   						=		{
    'configuration_blenderbot': [
        'BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotConfig',
        'BlenderbotOnnxConfig',
    ],
    'tokenization_blenderbot': ['BlenderbotTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		['BlenderbotTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[Any]   						=		[
							    'BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotForCausalLM',
							    'BlenderbotForConditionalGeneration',
							    'BlenderbotModel',
							    'BlenderbotPreTrainedModel',
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'TFBlenderbotForConditionalGeneration',
							    'TFBlenderbotModel',
							    'TFBlenderbotPreTrainedModel',
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase    :     Optional[int]   						=		[
							    'FlaxBlenderbotForConditionalGeneration',
							    'FlaxBlenderbotModel',
							    'FlaxBlenderbotPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_blenderbot import (
							    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotConfig,
							    BlenderbotOnnxConfig,
							)
							from .tokenization_blenderbot import BlenderbotTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot import (
														    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotForCausalLM,
														    BlenderbotForConditionalGeneration,
														    BlenderbotModel,
														    BlenderbotPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot import (
														    TFBlenderbotForConditionalGeneration,
														    TFBlenderbotModel,
														    TFBlenderbotPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot import (
														    FlaxBlenderbotForConditionalGeneration,
														    FlaxBlenderbotModel,
														    FlaxBlenderbotPreTrainedModel,
														)

else:
							import sys

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