



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


A__	      =       {
    """albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
    """albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/config.json""",
    """albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/config.json""",
    """albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json""",
    """albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/config.json""",
    """albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/config.json""",
    """albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/config.json""",
    """albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json""",
}





class 					__lowerCAmelCase (     lowerCamelCase__       ):
				__lowerCamelCase =				'''albert'''




				def __init__(							self						,				_snake_case=30000						,				_snake_case=128						,				_snake_case=4096						,				_snake_case=12						,				_snake_case=1						,				_snake_case=64						,				_snake_case=16384						,				_snake_case=1						,				_snake_case="gelu_new"						,				_snake_case=0						,				_snake_case=0						,				_snake_case=512						,				_snake_case=2						,				_snake_case=0.02						,				_snake_case=1e-12						,				_snake_case=0.1						,				_snake_case="absolute"						,				_snake_case=0						,				_snake_case=2						,				_snake_case=3						,				**_snake_case						,				):



									"""simple docstring"""





									super().__init__(pad_token_id=_snake_case						,				bos_token_id=_snake_case						,				eos_token_id=_snake_case						,				**_snake_case		)

									_lowerCAmelCase						=    vocab_size
									_lowerCAmelCase						=    embedding_size
									_lowerCAmelCase						=    hidden_size
									_lowerCAmelCase						=    num_hidden_layers
									_lowerCAmelCase						=    num_hidden_groups
									_lowerCAmelCase						=    num_attention_heads
									_lowerCAmelCase						=    inner_group_num
									_lowerCAmelCase						=    hidden_act
									_lowerCAmelCase						=    intermediate_size
									_lowerCAmelCase						=    hidden_dropout_prob
									_lowerCAmelCase						=    attention_probs_dropout_prob
									_lowerCAmelCase						=    max_position_embeddings
									_lowerCAmelCase						=    type_vocab_size
									_lowerCAmelCase						=    initializer_range
									_lowerCAmelCase						=    layer_norm_eps
									_lowerCAmelCase						=    classifier_dropout_prob
									_lowerCAmelCase						=    position_embedding_type





class 					__lowerCAmelCase (     lowerCamelCase__       ):




				@property
				def 							snake_case   (							self		):



									"""simple docstring"""





									if self.task == "multiple-choice":
														_lowerCAmelCase						=    {0: """batch""", 1: """choice""", 2: """sequence"""}
									else:
														_lowerCAmelCase						=    {0: """batch""", 1: """sequence"""}
									return OrderedDict(
									    [
									        ("""input_ids""", dynamic_axis),
									        ("""attention_mask""", dynamic_axis),
									        ("""token_type_ids""", dynamic_axis),
									    ]		)









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 __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
						lowerCAmelCase_      			=  True if '''large''' in model_name or '''huge''' in model_name else False
						lowerCAmelCase_      			=  True if '''large''' in model_name or '''huge''' in model_name else False
						lowerCAmelCase_      			=  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:
																		lowerCAmelCase_      			=  [3, 3, 3, 3]
																		lowerCAmelCase_      			=  [5, 5, 5, 5]
												elif "fl4" in model_name:
																		lowerCAmelCase_      			=  [4, 4, 4, 4]
																		lowerCAmelCase_      			=  [3, 3, 3, 3]

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

						if "tiny" in model_name:
												lowerCAmelCase_      			=  96
						elif "small" in model_name:
												lowerCAmelCase_      			=  96
						elif "base" in model_name:
												lowerCAmelCase_      			=  128
						elif "large" in model_name:
												lowerCAmelCase_      			=  192
						elif "xlarge" in model_name:
												lowerCAmelCase_      			=  256
						elif "huge" in model_name:
												lowerCAmelCase_      			=  352

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

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

						lowerCAmelCase_      			=  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 __UpperCamelCase			(       _A  ):
						if "patch_embed.proj" in name:
												lowerCAmelCase_      			=  name.replace('''patch_embed.proj''' ,  '''embeddings.patch_embeddings.projection'''  )
						if "patch_embed.norm" in name:
												lowerCAmelCase_      			=  name.replace('''patch_embed.norm''' ,  '''embeddings.norm'''  )
						if "layers" in name:
												lowerCAmelCase_      			=  '''encoder.''' + name
						if "encoder.layers" in name:
												lowerCAmelCase_      			=  name.replace('''encoder.layers''' ,  '''encoder.stages'''  )
						if "downsample.proj" in name:
												lowerCAmelCase_      			=  name.replace('''downsample.proj''' ,  '''downsample.projection'''  )
						if "blocks" in name:
												lowerCAmelCase_      			=  name.replace('''blocks''' ,  '''layers'''  )
						if "modulation.f.weight" in name or "modulation.f.bias" in name:
												lowerCAmelCase_      			=  name.replace('''modulation.f''' ,  '''modulation.projection_in'''  )
						if "modulation.h.weight" in name or "modulation.h.bias" in name:
												lowerCAmelCase_      			=  name.replace('''modulation.h''' ,  '''modulation.projection_context'''  )
						if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
												lowerCAmelCase_      			=  name.replace('''modulation.proj''' ,  '''modulation.projection_out'''  )

						if name == "norm.weight":
												lowerCAmelCase_      			=  '''layernorm.weight'''
						if name == "norm.bias":
												lowerCAmelCase_      			=  '''layernorm.bias'''

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

						return name





def __UpperCamelCase			(       _A ,  _A ,  _A=False  ):
						# fmt: off
						lowerCAmelCase_      			=  {
						    '''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

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

						# rename keys
						for key in state_dict.copy().keys():
												lowerCAmelCase_      			=  state_dict.pop(_A  )
												lowerCAmelCase_      			=  val

						lowerCAmelCase_      			=  get_focalnet_config(_A  )
						lowerCAmelCase_      			=  FocalNetForImageClassification(_A  )
						model.eval()

						# load state dict
						model.load_state_dict(_A  )

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

						lowerCAmelCase_      			=  BitImageProcessor(
						    do_resize=_A ,  size={'''shortest_edge''': 256} ,  resample=PILImageResampling.BILINEAR ,  do_center_crop=_A ,  crop_size=224 ,  do_normalize=_A ,  image_mean=_A ,  image_std=_A ,  )
						lowerCAmelCase_      			=  Image.open(requests.get(_A ,  stream=_A  ).raw  )
						lowerCAmelCase_      			=  processor(images=_A ,  return_tensors='''pt'''  )

						lowerCAmelCase_      			=  transforms.Compose(
						    [
						        transforms.Resize(256  ),
						        transforms.CenterCrop(224  ),
						        transforms.ToTensor(),
						        transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6] ,  std=[0.2_2_9, 0.2_2_4, 0.2_2_5]  ),
						    ]  )

						lowerCAmelCase_      			=  image_transforms(_A  ).unsqueeze(0  )

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

						lowerCAmelCase_      			=  model(**_A  )

						lowerCAmelCase_      			=  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":
												lowerCAmelCase_      			=  torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]  )
						elif model_name == "focalnet-tiny-lrf":
												lowerCAmelCase_      			=  torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]  )
						elif model_name == "focalnet-small":
												lowerCAmelCase_      			=  torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]  )
						elif model_name == "focalnet-small-lrf":
												lowerCAmelCase_      			=  torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]  )
						elif model_name == "focalnet-base":
												lowerCAmelCase_      			=  torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]  )
						elif model_name == "focalnet-base-lrf":
												lowerCAmelCase_      			=  torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]  )
						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__":
						_A     =			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.''',
						)

						_A     =			parser.parse_args()
						convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




