"""simple docstring"""




import torch
from torch import nn

class    a_   (  nn.Module      ):




					def __init__(						self    :  Dict	,    snake_case__    :  str	,    snake_case__    :  List[Any]	,    snake_case__    :  Any	,    snake_case__    :  Optional[Any]	,    snake_case__    :  int=1	,    snake_case__    :  str=False  ):
							super().__init__()

							lowerCAmelCase__   = n_token
							lowerCAmelCase__   = d_embed
							lowerCAmelCase__   = d_proj

							lowerCAmelCase__   = cutoffs + [n_token]
							lowerCAmelCase__   = [0] + self.cutoffs
							lowerCAmelCase__   = div_val

							lowerCAmelCase__   = self.cutoffs[0]
							lowerCAmelCase__   = len(self.cutoffs  ) - 1
							lowerCAmelCase__   = self.shortlist_size + self.n_clusters

							if self.n_clusters > 0:
									lowerCAmelCase__   = nn.Parameter(torch.zeros(self.n_clusters	,    self.d_embed  )  )
									lowerCAmelCase__   = nn.Parameter(torch.zeros(self.n_clusters  )  )

							lowerCAmelCase__   = nn.ModuleList()
							lowerCAmelCase__   = nn.ParameterList()

							if div_val == 1:
									for i in range(len(self.cutoffs  )  ):
											if d_proj != d_embed:
													self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case__	,    snake_case__  )  )  )
											else:
													self.out_projs.append(snake_case__  )

									self.out_layers.append(nn.Linear(snake_case__	,    snake_case__  )  )
							else:
									for i in range(len(self.cutoffs  )  ):
											lowerCAmelCase__ ,       lowerCAmelCase__   = self.cutoff_ends[i], self.cutoff_ends[i + 1]
											lowerCAmelCase__   = d_embed // (div_val**i)

											self.out_projs.append(nn.Parameter(torch.FloatTensor(snake_case__	,    snake_case__  )  )  )

											self.out_layers.append(nn.Linear(snake_case__	,    r_idx - l_idx  )  )

							lowerCAmelCase__   = keep_order




					def     _SCREAMING_SNAKE_CASE    (						self    :  int	,    snake_case__    :  Optional[Any]	,    snake_case__    :  Dict	,    snake_case__    :  Optional[int]	,    snake_case__    :  List[Any]  ):
							if proj is None:
									lowerCAmelCase__   = nn.functional.linear(snake_case__	,    snake_case__	,    bias=snake_case__  )
							else:
									# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
									lowerCAmelCase__   = nn.functional.linear(snake_case__	,    proj.t().contiguous()  )
									lowerCAmelCase__   = nn.functional.linear(snake_case__	,    snake_case__	,    bias=snake_case__  )
									# else:
									#     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
									#     if bias is not None:
									#         logit = logit + bias

							return logit




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  Optional[Any]	,    snake_case__    :  Union[str, Any]=None	,    snake_case__    :  Dict=False  ):

							if labels is not None:
									# Shift so that tokens < n predict n
									lowerCAmelCase__   = hidden[..., :-1, :].contiguous()
									lowerCAmelCase__   = labels[..., 1:].contiguous()
									lowerCAmelCase__   = hidden.view(-1	,    hidden.size(-1  )  )
									lowerCAmelCase__   = labels.view(-1  )
									if hidden.size(0  ) != labels.size(0  ):
											raise RuntimeError("""Input and labels should have the same size in the batch dimension."""  )
							else:
									lowerCAmelCase__   = hidden.view(-1	,    hidden.size(-1  )  )

							if self.n_clusters == 0:
									lowerCAmelCase__   = self._compute_logit(snake_case__	,    self.out_layers[0].weight	,    self.out_layers[0].bias	,    self.out_projs[0]  )
									if labels is not None:
											lowerCAmelCase__   = labels != -100
											lowerCAmelCase__   = torch.zeros_like(snake_case__	,    dtype=hidden.dtype	,    device=hidden.device  )
											lowerCAmelCase__   = (
											    -nn.functional.log_softmax(snake_case__	,    dim=-1  )[mask].gather(1	,    labels[mask].unsqueeze(1  )  ).squeeze(1  )
											)
									else:
											lowerCAmelCase__   = nn.functional.log_softmax(snake_case__	,    dim=-1  )
							else:
									# construct weights and biases
									lowerCAmelCase__ ,       lowerCAmelCase__   = [], []
									for i in range(len(self.cutoffs  )  ):
											if self.div_val == 1:
													lowerCAmelCase__ ,       lowerCAmelCase__   = self.cutoff_ends[i], self.cutoff_ends[i + 1]
													lowerCAmelCase__   = self.out_layers[0].weight[l_idx:r_idx]
													lowerCAmelCase__   = self.out_layers[0].bias[l_idx:r_idx]
											else:
													lowerCAmelCase__   = self.out_layers[i].weight
													lowerCAmelCase__   = self.out_layers[i].bias

											if i == 0:
													lowerCAmelCase__   = torch.cat([weight_i, self.cluster_weight]	,    dim=0  )
													lowerCAmelCase__   = torch.cat([bias_i, self.cluster_bias]	,    dim=0  )

											weights.append(snake_case__  )
											biases.append(snake_case__  )

									lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = weights[0], biases[0], self.out_projs[0]

									lowerCAmelCase__   = self._compute_logit(snake_case__	,    snake_case__	,    snake_case__	,    snake_case__  )
									lowerCAmelCase__   = nn.functional.log_softmax(snake_case__	,    dim=1  )

									if labels is None:
											lowerCAmelCase__   = hidden.new_empty((head_logit.size(0  ), self.n_token)  )
									else:
											lowerCAmelCase__   = torch.zeros_like(snake_case__	,    dtype=hidden.dtype	,    device=hidden.device  )

									lowerCAmelCase__   = 0
									lowerCAmelCase__   = [0] + self.cutoffs
									for i in range(len(snake_case__  ) - 1  ):
											lowerCAmelCase__ ,       lowerCAmelCase__   = cutoff_values[i], cutoff_values[i + 1]

											if labels is not None:
													lowerCAmelCase__   = (labels >= l_idx) & (labels < r_idx)
													lowerCAmelCase__   = mask_i.nonzero().squeeze()

													if indices_i.numel() == 0:
															continue

													lowerCAmelCase__   = labels.index_select(0	,    snake_case__  ) - l_idx
													lowerCAmelCase__   = head_logprob.index_select(0	,    snake_case__  )
													lowerCAmelCase__   = hidden.index_select(0	,    snake_case__  )
											else:
													lowerCAmelCase__   = hidden

											if i == 0:
													if labels is not None:
															lowerCAmelCase__   = head_logprob_i.gather(1	,    target_i[:, None]  ).squeeze(1  )
													else:
															lowerCAmelCase__   = head_logprob[:, : self.cutoffs[0]]
											else:
													lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = weights[i], biases[i], self.out_projs[i]

													lowerCAmelCase__   = self._compute_logit(snake_case__	,    snake_case__	,    snake_case__	,    snake_case__  )
													lowerCAmelCase__   = nn.functional.log_softmax(snake_case__	,    dim=1  )
													lowerCAmelCase__   = self.cutoffs[0] + i - 1  # No probability for the head cluster
													if labels is not None:
															lowerCAmelCase__   = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
															    1	,    target_i[:, None]  ).squeeze(1  )
													else:
															lowerCAmelCase__   = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
															lowerCAmelCase__   = logprob_i

											if labels is not None:
													if (hasattr(self	,    """keep_order"""  ) and self.keep_order) or keep_order:
															out.index_copy_(0	,    snake_case__	,    -logprob_i  )
													else:
															out[offset : offset + logprob_i.size(0  )].copy_(-logprob_i  )
													offset += logprob_i.size(0  )

							return out




					def     _SCREAMING_SNAKE_CASE    (						self    :  str	,    snake_case__    :  int  ):
							if self.n_clusters == 0:
									lowerCAmelCase__   = self._compute_logit(snake_case__	,    self.out_layers[0].weight	,    self.out_layers[0].bias	,    self.out_projs[0]  )
									return nn.functional.log_softmax(snake_case__	,    dim=-1  )
							else:
									# construct weights and biases
									lowerCAmelCase__ ,       lowerCAmelCase__   = [], []
									for i in range(len(self.cutoffs  )  ):
											if self.div_val == 1:
													lowerCAmelCase__ ,       lowerCAmelCase__   = self.cutoff_ends[i], self.cutoff_ends[i + 1]
													lowerCAmelCase__   = self.out_layers[0].weight[l_idx:r_idx]
													lowerCAmelCase__   = self.out_layers[0].bias[l_idx:r_idx]
											else:
													lowerCAmelCase__   = self.out_layers[i].weight
													lowerCAmelCase__   = self.out_layers[i].bias

											if i == 0:
													lowerCAmelCase__   = torch.cat([weight_i, self.cluster_weight]	,    dim=0  )
													lowerCAmelCase__   = torch.cat([bias_i, self.cluster_bias]	,    dim=0  )

											weights.append(snake_case__  )
											biases.append(snake_case__  )

									lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = weights[0], biases[0], self.out_projs[0]
									lowerCAmelCase__   = self._compute_logit(snake_case__	,    snake_case__	,    snake_case__	,    snake_case__  )

									lowerCAmelCase__   = hidden.new_empty((head_logit.size(0  ), self.n_token)  )
									lowerCAmelCase__   = nn.functional.log_softmax(snake_case__	,    dim=1  )

									lowerCAmelCase__   = [0] + self.cutoffs
									for i in range(len(snake_case__  ) - 1  ):
											lowerCAmelCase__ ,       lowerCAmelCase__   = cutoff_values[i], cutoff_values[i + 1]

											if i == 0:
													lowerCAmelCase__   = head_logprob[:, : self.cutoffs[0]]
											else:
													lowerCAmelCase__ ,       lowerCAmelCase__ ,       lowerCAmelCase__   = weights[i], biases[i], self.out_projs[i]

													lowerCAmelCase__   = self._compute_logit(snake_case__	,    snake_case__	,    snake_case__	,    snake_case__  )
													lowerCAmelCase__   = nn.functional.log_softmax(snake_case__	,    dim=1  )

													lowerCAmelCase__   = head_logprob[:, -i] + tail_logprob_i
													lowerCAmelCase__   = logprob_i

									return out






"""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						(	lowerCAmelCase__						):
			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(lowerCAmelCase__	,lowerCAmelCase__	,repo_type='''dataset'''						)	,'''r'''						)						)
			lowerCamelCase_					=  {int(lowerCAmelCase__						): v for k, v in idalabel.items()}
			lowerCamelCase_					=  {v: k for k, v in idalabel.items()}

			lowerCamelCase_					=  FocalNetConfig(
			    embed_dim=lowerCAmelCase__	,depths=lowerCAmelCase__	,focal_levels=lowerCAmelCase__	,focal_windows=lowerCAmelCase__	,use_conv_embed=lowerCAmelCase__	,idalabel=lowerCAmelCase__	,labelaid=lowerCAmelCase__	,use_post_layernorm=lowerCAmelCase__	,use_layerscale=lowerCAmelCase__	,)

			return config






def  lowercase						(	lowerCAmelCase__						):
			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  lowercase						(	lowerCAmelCase__	,lowerCAmelCase__	,lowerCAmelCase__=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: '''	,lowerCAmelCase__						)
			lowerCamelCase_					=  torch.hub.load_state_dict_from_url(lowerCAmelCase__	,map_location='''cpu'''						)['''model''']

			# rename keys
			for key in state_dict.copy().keys():
						lowerCamelCase_					=  state_dict.pop(lowerCAmelCase__						)
						lowerCamelCase_					=  val

			lowerCamelCase_					=  get_focalnet_config(lowerCAmelCase__						)
			lowerCamelCase_					=  FocalNetForImageClassification(lowerCAmelCase__						)
			model.eval()

			# load state dict
			model.load_state_dict(lowerCAmelCase__						)

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

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

			lowerCamelCase_					=  transforms.Compose(
			    [
			        transforms.Resize(256						),
			        transforms.CenterCrop(224						),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.485, 0.456, 0.406]	,std=[0.229, 0.224, 0.225]						),
			    ]						)

			lowerCamelCase_					=  image_transforms(lowerCAmelCase__						).unsqueeze(0						)

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

			lowerCamelCase_					=  model(**lowerCAmelCase__						)

			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_166, -0.4_368, 0.2_191]						)
			elif model_name == "focalnet-tiny-lrf":
						lowerCamelCase_					=  torch.tensor([1.1_669, 0.0_125, -0.1_695]						)
			elif model_name == "focalnet-small":
						lowerCamelCase_					=  torch.tensor([0.4_917, -0.0_430, 0.1_341]						)
			elif model_name == "focalnet-small-lrf":
						lowerCamelCase_					=  torch.tensor([-0.2_588, -0.5_342, -0.2_331]						)
			elif model_name == "focalnet-base":
						lowerCamelCase_					=  torch.tensor([-0.1_655, -0.4_090, -0.1_730]						)
			elif model_name == "focalnet-base-lrf":
						lowerCamelCase_					=  torch.tensor([0.5_306, -0.0_483, -0.3_928]						)
			assert torch.allclose(outputs.logits[0, :3]	,lowerCAmelCase__	,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(lowerCAmelCase__						)
						processor.save_pretrained(lowerCAmelCase__						)

			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)



