


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 							__lowercase   ( __lowerCAmelCase							:				Optional[int]   ):
							a__             =     SwinvaConfig()
							a__             =     swinva_name.split('_'   )

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

							if model_size == "tiny":
														a__             =     9_6
														a__             =     (2, 2, 6, 2)
														a__             =     (3, 6, 1_2, 2_4)
							elif model_size == "small":
														a__             =     9_6
														a__             =     (2, 2, 1_8, 2)
														a__             =     (3, 6, 1_2, 2_4)
							elif model_size == "base":
														a__             =     1_2_8
														a__             =     (2, 2, 1_8, 2)
														a__             =     (4, 8, 1_6, 3_2)
							else:
														a__             =     1_9_2
														a__             =     (2, 2, 1_8, 2)
														a__             =     (6, 1_2, 2_4, 4_8)

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

							if ("22k" in swinva_name) and ("to" not in swinva_name):
														a__             =     2_1_8_4_1
														a__             =     'huggingface/label-files'
														a__             =     'imagenet-22k-id2label.json'
														a__             =     json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type='dataset'   )					,    'r'   )   )
														a__             =     {int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
														a__             =     idalabel
														a__             =     {v: k for k, v in idalabel.items()}

							else:
														a__             =     1_0_0_0
														a__             =     'huggingface/label-files'
														a__             =     'imagenet-1k-id2label.json'
														a__             =     json.load(open(hf_hub_download(__lowerCAmelCase					,    __lowerCAmelCase					,    repo_type='dataset'   )					,    'r'   )   )
														a__             =     {int(__lowerCAmelCase   ): v for k, v in idalabel.items()}
														a__             =     idalabel
														a__             =     {v: k for k, v in idalabel.items()}

							a__             =     img_size
							a__             =     num_classes
							a__             =     embed_dim
							a__             =     depths
							a__             =     num_heads
							a__             =     window_size

							return config






def 							__lowercase   ( __lowerCAmelCase							:				Any   ):
							if "patch_embed.proj" in name:
														a__             =     name.replace('patch_embed.proj'					,    'embeddings.patch_embeddings.projection'   )
							if "patch_embed.norm" in name:
														a__             =     name.replace('patch_embed.norm'					,    'embeddings.norm'   )
							if "layers" in name:
														a__             =     'encoder.' + name
							if "attn.proj" in name:
														a__             =     name.replace('attn.proj'					,    'attention.output.dense'   )
							if "attn" in name:
														a__             =     name.replace('attn'					,    'attention.self'   )
							if "norm1" in name:
														a__             =     name.replace('norm1'					,    'layernorm_before'   )
							if "norm2" in name:
														a__             =     name.replace('norm2'					,    'layernorm_after'   )
							if "mlp.fc1" in name:
														a__             =     name.replace('mlp.fc1'					,    'intermediate.dense'   )
							if "mlp.fc2" in name:
														a__             =     name.replace('mlp.fc2'					,    'output.dense'   )
							if "q_bias" in name:
														a__             =     name.replace('q_bias'					,    'query.bias'   )
							if "k_bias" in name:
														a__             =     name.replace('k_bias'					,    'key.bias'   )
							if "v_bias" in name:
														a__             =     name.replace('v_bias'					,    'value.bias'   )
							if "cpb_mlp" in name:
														a__             =     name.replace('cpb_mlp'					,    'continuous_position_bias_mlp'   )
							if name == "norm.weight":
														a__             =     'layernorm.weight'
							if name == "norm.bias":
														a__             =     'layernorm.bias'

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

							return name






def 							__lowercase   ( __lowerCAmelCase							:				Union[str, Any]					,    __lowerCAmelCase							:				Union[str, Any]   ):
							for key in orig_state_dict.copy().keys():
														a__             =     orig_state_dict.pop(__lowerCAmelCase   )

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

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

							return orig_state_dict






def 							__lowercase   ( __lowerCAmelCase							:				List[Any]					,    __lowerCAmelCase							:				Optional[int]   ):
							a__             =     timm.create_model(__lowerCAmelCase					,    pretrained=__lowerCAmelCase   )
							timm_model.eval()

							a__             =     get_swinva_config(__lowerCAmelCase   )
							a__             =     SwinvaForImageClassification(__lowerCAmelCase   )
							model.eval()

							a__             =     convert_state_dict(timm_model.state_dict()					,    __lowerCAmelCase   )
							model.load_state_dict(__lowerCAmelCase   )

							a__             =     'http://images.cocodataset.org/val2017/000000039769.jpg'

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

							a__             =     timm_model(inputs['pixel_values']   )
							a__             =     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__":
						snake_case							:       Dict     			=      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.'''
						)

						snake_case							:       Optional[Any]     			=      parser.parse_args()
						convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




