



"""simple docstring"""




import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling






def      __lowercase       (			a__       )    ->			Optional[Any]:
							__SCREAMING_SNAKE_CASE									= [2, 2, 6, 2] if 'tiny' in model_name else [2, 2, 18, 2]
							__SCREAMING_SNAKE_CASE									= True if 'large' in model_name or 'huge' in model_name else False
							__SCREAMING_SNAKE_CASE									= True if 'large' in model_name or 'huge' in model_name else False
							__SCREAMING_SNAKE_CASE									= True if 'large' in model_name or 'huge' in model_name else False

							if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
														if "fl3" in model_name:
																					__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]
																					__SCREAMING_SNAKE_CASE									= [5, 5, 5, 5]
														elif "fl4" in model_name:
																					__SCREAMING_SNAKE_CASE									= [4, 4, 4, 4]
																					__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]

							if "tiny" in model_name or "small" in model_name or "base" in model_name:
														__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]
														if "lrf" in model_name:
																					__SCREAMING_SNAKE_CASE									= [3, 3, 3, 3]
														else:
																					__SCREAMING_SNAKE_CASE									= [2, 2, 2, 2]

							if "tiny" in model_name:
														__SCREAMING_SNAKE_CASE									= 96
							elif "small" in model_name:
														__SCREAMING_SNAKE_CASE									= 96
							elif "base" in model_name:
														__SCREAMING_SNAKE_CASE									= 1_28
							elif "large" in model_name:
														__SCREAMING_SNAKE_CASE									= 1_92
							elif "xlarge" in model_name:
														__SCREAMING_SNAKE_CASE									= 2_56
							elif "huge" in model_name:
														__SCREAMING_SNAKE_CASE									= 3_52

							# set label information
							__SCREAMING_SNAKE_CASE									= 'huggingface/label-files'
							if "large" in model_name or "huge" in model_name:
														__SCREAMING_SNAKE_CASE									= 'imagenet-22k-id2label.json'
							else:
														__SCREAMING_SNAKE_CASE									= 'imagenet-1k-id2label.json'

							__SCREAMING_SNAKE_CASE									= json.load(open(hf_hub_download(a__							,       a__							,       repo_type='dataset'       )							,       'r'       )       )
							__SCREAMING_SNAKE_CASE									= {int(a__       ): v for k, v in idalabel.items()}
							__SCREAMING_SNAKE_CASE									= {v: k for k, v in idalabel.items()}

							__SCREAMING_SNAKE_CASE									= FocalNetConfig(
							    embed_dim=a__							,       depths=a__							,       focal_levels=a__							,       focal_windows=a__							,       use_conv_embed=a__							,       idalabel=a__							,       labelaid=a__							,       use_post_layernorm=a__							,       use_layerscale=a__							,       )

							return config






def      __lowercase       (			a__       )    ->			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 "encoder.layers" in name:
														__SCREAMING_SNAKE_CASE									= name.replace('encoder.layers'							,       'encoder.stages'       )
							if "downsample.proj" in name:
														__SCREAMING_SNAKE_CASE									= name.replace('downsample.proj'							,       'downsample.projection'       )
							if "blocks" in name:
														__SCREAMING_SNAKE_CASE									= name.replace('blocks'							,       'layers'       )
							if "modulation.f.weight" in name or "modulation.f.bias" in name:
														__SCREAMING_SNAKE_CASE									= name.replace('modulation.f'							,       'modulation.projection_in'       )
							if "modulation.h.weight" in name or "modulation.h.bias" in name:
														__SCREAMING_SNAKE_CASE									= name.replace('modulation.h'							,       'modulation.projection_context'       )
							if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
														__SCREAMING_SNAKE_CASE									= name.replace('modulation.proj'							,       'modulation.projection_out'       )

							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									= 'focalnet.' + name

							return name






def      __lowercase       (			a__							,       a__							,       a__=False       )    ->			Dict:
							# fmt: off
							__SCREAMING_SNAKE_CASE									= {
							    'focalnet-tiny': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth',
							    'focalnet-tiny-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth',
							    'focalnet-small': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth',
							    'focalnet-small-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth',
							    'focalnet-base': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth',
							    'focalnet-base-lrf': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth',
							    'focalnet-large-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth',
							    'focalnet-large-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth',
							    'focalnet-xlarge-lrf-fl3': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth',
							    'focalnet-xlarge-lrf-fl4': 'https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth',
							}
							# fmt: on

							__SCREAMING_SNAKE_CASE									= model_name_to_url[model_name]
							print('Checkpoint URL: '							,       a__       )
							__SCREAMING_SNAKE_CASE									= torch.hub.load_state_dict_from_url(a__							,       map_location='cpu'       )['model']

							# rename keys
							for key in state_dict.copy().keys():
														__SCREAMING_SNAKE_CASE									= state_dict.pop(a__       )
														__SCREAMING_SNAKE_CASE									= val

							__SCREAMING_SNAKE_CASE									= get_focalnet_config(a__       )
							__SCREAMING_SNAKE_CASE									= FocalNetForImageClassification(a__       )
							model.eval()

							# load state dict
							model.load_state_dict(a__       )

							# verify conversion
							__SCREAMING_SNAKE_CASE									= 'http://images.cocodataset.org/val2017/000000039769.jpg'

							__SCREAMING_SNAKE_CASE									= BitImageProcessor(
							    do_resize=a__							,       size={'shortest_edge': 2_56}							,       resample=PILImageResampling.BILINEAR							,       do_center_crop=a__							,       crop_size=2_24							,       do_normalize=a__							,       image_mean=a__							,       image_std=a__							,       )
							__SCREAMING_SNAKE_CASE									= Image.open(requests.get(a__							,       stream=a__       ).raw       )
							__SCREAMING_SNAKE_CASE									= processor(images=a__							,       return_tensors='pt'       )

							__SCREAMING_SNAKE_CASE									= transforms.Compose(
							    [
							        transforms.Resize(2_56       ),
							        transforms.CenterCrop(2_24       ),
							        transforms.ToTensor(),
							        transforms.Normalize(mean=[0.485, 0.456, 0.406]							,       std=[0.229, 0.224, 0.225]       ),
							    ]       )

							__SCREAMING_SNAKE_CASE									= image_transforms(a__       ).unsqueeze(0       )

							# verify pixel_values
							assert torch.allclose(inputs.pixel_values							,       a__							,       atol=1E-4       )

							__SCREAMING_SNAKE_CASE									= model(**a__       )

							__SCREAMING_SNAKE_CASE									= outputs.logits.argmax(-1       ).item()
							print('Predicted class:'							,       model.config.idalabel[predicted_class_idx]       )

							print('First values of logits:'							,       outputs.logits[0, :3]       )

							if model_name == "focalnet-tiny":
														__SCREAMING_SNAKE_CASE									= torch.tensor([0.2166, -0.4368, 0.2191]       )
							elif model_name == "focalnet-tiny-lrf":
														__SCREAMING_SNAKE_CASE									= torch.tensor([1.1669, 0.0125, -0.1695]       )
							elif model_name == "focalnet-small":
														__SCREAMING_SNAKE_CASE									= torch.tensor([0.4917, -0.0430, 0.1341]       )
							elif model_name == "focalnet-small-lrf":
														__SCREAMING_SNAKE_CASE									= torch.tensor([-0.2588, -0.5342, -0.2331]       )
							elif model_name == "focalnet-base":
														__SCREAMING_SNAKE_CASE									= torch.tensor([-0.1655, -0.4090, -0.1730]       )
							elif model_name == "focalnet-base-lrf":
														__SCREAMING_SNAKE_CASE									= torch.tensor([0.5306, -0.0483, -0.3928]       )
							assert torch.allclose(outputs.logits[0, :3]							,       a__							,       atol=1E-4       )
							print('Looks ok!'       )

							if pytorch_dump_folder_path is not None:
														print(f"""Saving model and processor of {model_name} to {pytorch_dump_folder_path}"""       )
														model.save_pretrained(a__       )
														processor.save_pretrained(a__       )

							if push_to_hub:
														print(f"""Pushing model and processor of {model_name} to the hub..."""       )
														model.push_to_hub(f"""{model_name}"""       )
														processor.push_to_hub(f"""{model_name}"""       )


if __name__ == "__main__":
			lowerCAmelCase__		:					int							=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default='''focalnet-tiny''',
			    type=str,
			    help='''Name of the FocalNet 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.'''
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    action='''store_true''',
			    help='''Whether to push the model and processor to the hub.''',
			)

			lowerCAmelCase__		:					List[Any]							=parser.parse_args()
			convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import multiprocessing
from typing import TYPE_CHECKING, Optional, Union

from .. import Dataset, Features, config
from ..formatting import query_table
from ..packaged_modules.sql.sql import Sql
from ..utils import logging
from .abc import AbstractDatasetInputStream


if TYPE_CHECKING:
			import sqlitea

			import sqlalchemy

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A = None				,							_A = None				,							_A = False				,							**_A				,							):







													'''simple docstring'''

													super().__init__(features=_A				,							cache_dir=_A				,							keep_in_memory=_A				,							**_A		)
													__SCREAMING_SNAKE_CASE									= Sql(
													    cache_dir=_A				,							features=_A				,							sql=_A				,							con=_A				,							**_A				,							)






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None
													__SCREAMING_SNAKE_CASE									= None

													self.builder.download_and_prepare(
													    download_config=_A				,							download_mode=_A				,							verification_mode=_A				,							base_path=_A				,							)

													# Build dataset for splits
													__SCREAMING_SNAKE_CASE									= self.builder.as_dataset(
													    split='train'				,							verification_mode=_A				,							in_memory=self.keep_in_memory		)
													return dataset







class        UpperCAmelCase_  :

						'''simple docstring'''




						def __init__(   self				,							_A				,							_A				,							_A				,							_A = None				,							_A = None				,							**_A				,							):







													'''simple docstring'''

													if num_proc is not None and num_proc <= 0:
																				raise ValueError(f"""num_proc {num_proc} must be an integer > 0."""		)

													__SCREAMING_SNAKE_CASE									= dataset
													__SCREAMING_SNAKE_CASE									= name
													__SCREAMING_SNAKE_CASE									= con
													__SCREAMING_SNAKE_CASE									= batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
													__SCREAMING_SNAKE_CASE									= num_proc
													__SCREAMING_SNAKE_CASE									= to_sql_kwargs






						def 	_A     (   self		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('sql'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('con'				,							_A		)
													__SCREAMING_SNAKE_CASE									= self.to_sql_kwargs.pop('index'				,							_A		)

													__SCREAMING_SNAKE_CASE									= self._write(index=_A				,							**self.to_sql_kwargs		)
													return written






						def 	_A     (   self				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= args
													__SCREAMING_SNAKE_CASE									= {**to_sql_kwargs, 'if_exists': 'append'} if offset > 0 else to_sql_kwargs
													__SCREAMING_SNAKE_CASE									= query_table(
													    table=self.dataset.data				,							key=slice(_A				,							offset + self.batch_size		)				,							indices=self.dataset._indices				,							)
													__SCREAMING_SNAKE_CASE									= batch.to_pandas()
													__SCREAMING_SNAKE_CASE									= df.to_sql(self.name				,							self.con				,							index=_A				,							**_A		)
													return num_rows or len(_A		)






						def 	_A     (   self				,							_A				,							**_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= 0

													if self.num_proc is None or self.num_proc == 1:
																				for offset in logging.tqdm(
																				    range(0				,							len(self.dataset		)				,							self.batch_size		)				,							unit='ba'				,							disable=not logging.is_progress_bar_enabled()				,							desc='Creating SQL from Arrow format'				,							):
																											written += self._batch_sql((offset, index, to_sql_kwargs)		)
													else:
																				__SCREAMING_SNAKE_CASE							,	__SCREAMING_SNAKE_CASE									= len(self.dataset		), self.batch_size
																				with multiprocessing.Pool(self.num_proc		) as pool:
																											for num_rows in logging.tqdm(
																											    pool.imap(
																											        self._batch_sql				,							[(offset, index, to_sql_kwargs) for offset in range(0				,							_A				,							_A		)]				,							)				,							total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size				,							unit='ba'				,							disable=not logging.is_progress_bar_enabled()				,							desc='Creating SQL from Arrow format'				,							):
																																		written += num_rows

													return written






