

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

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

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

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

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

						snake_case_  =      FocalNetConfig(
						    embed_dim=SCREAMING_SNAKE_CASE__    ,						depths=SCREAMING_SNAKE_CASE__    ,						focal_levels=SCREAMING_SNAKE_CASE__    ,						focal_windows=SCREAMING_SNAKE_CASE__    ,						use_conv_embed=SCREAMING_SNAKE_CASE__    ,						idalabel=SCREAMING_SNAKE_CASE__    ,						labelaid=SCREAMING_SNAKE_CASE__    ,						use_post_layernorm=SCREAMING_SNAKE_CASE__    ,						use_layerscale=SCREAMING_SNAKE_CASE__    ,						)

						return config


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

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

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

						return name


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False			):
						# fmt: off
						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

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

						# rename keys
						for key in state_dict.copy().keys():
												snake_case_  =      state_dict.pop(SCREAMING_SNAKE_CASE__			)
												snake_case_  =      val

						snake_case_  =      get_focalnet_config(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      FocalNetForImageClassification(SCREAMING_SNAKE_CASE__			)
						model.eval()

						# load state dict
						model.load_state_dict(SCREAMING_SNAKE_CASE__			)

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

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

						snake_case_  =      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]			),
						    ]			)

						snake_case_  =      image_transforms(SCREAMING_SNAKE_CASE__			).unsqueeze(0			)

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

						snake_case_  =      model(**SCREAMING_SNAKE_CASE__			)

						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":
												snake_case_  =      torch.tensor([0.2166, -0.4368, 0.2191]			)
						elif model_name == "focalnet-tiny-lrf":
												snake_case_  =      torch.tensor([1.1669, 0.0125, -0.1695]			)
						elif model_name == "focalnet-small":
												snake_case_  =      torch.tensor([0.4917, -0.0430, 0.1341]			)
						elif model_name == "focalnet-small-lrf":
												snake_case_  =      torch.tensor([-0.2588, -0.5342, -0.2331]			)
						elif model_name == "focalnet-base":
												snake_case_  =      torch.tensor([-0.1655, -0.4090, -0.1730]			)
						elif model_name == "focalnet-base-lrf":
												snake_case_  =      torch.tensor([0.5306, -0.0483, -0.3928]			)
						assert torch.allclose(outputs.logits[0, :3]    ,						SCREAMING_SNAKE_CASE__    ,						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(SCREAMING_SNAKE_CASE__			)
												processor.save_pretrained(SCREAMING_SNAKE_CASE__			)

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






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import torch

from accelerate import PartialState
from accelerate.utils.operations import broadcast, gather, gather_object, pad_across_processes, reduce

def   __snake_case    (       _lowerCAmelCase			:			Optional[int]						)			->    Dict:
   return (torch.arange(state.num_processes						) + 1.0 + (state.num_processes * state.process_index)).to(state.device						)

def   __snake_case    (       _lowerCAmelCase			:			Tuple						)			->    Optional[Any]:
   A_       :					Optional[Any]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Dict        =      gather(_lowerCAmelCase						)
   assert gathered_tensor.tolist() == list(range(1					,					state.num_processes**2 + 1						)						)

def   __snake_case    (       _lowerCAmelCase			:			List[Any]						)			->    Any:
   A_       :					int        =      [state.process_index]
   A_       :					Union[str, Any]        =      gather_object(_lowerCAmelCase						)
   assert len(_lowerCAmelCase						) == state.num_processes, f"{gathered_obj}, {len(_lowerCAmelCase						)} != {state.num_processes}"
   assert gathered_obj == list(range(state.num_processes						)						), f"{gathered_obj} != {list(range(state.num_processes						)						)}"

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]						)			->    Optional[Any]:
   A_       :					List[str]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Optional[Any]        =      broadcast(_lowerCAmelCase						)
   assert broadcasted_tensor.shape == torch.Size([state.num_processes]						)
   assert broadcasted_tensor.tolist() == list(range(1					,					state.num_processes + 1						)						)

def   __snake_case    (       _lowerCAmelCase			:			Dict						)			->    str:
   # We need to pad the tensor with one more element if we are the main process
   # to ensure that we can pad
   if state.is_main_process:
      A_       :					Tuple        =      torch.arange(state.num_processes + 1						).to(state.device						)
   else:
      A_       :					List[str]        =      torch.arange(state.num_processes						).to(state.device						)
   A_       :					Any        =      pad_across_processes(_lowerCAmelCase						)
   assert padded_tensor.shape == torch.Size([state.num_processes + 1]						)
   if not state.is_main_process:
      assert padded_tensor.tolist() == list(range(0					,					state.num_processes						)						) + [0]

def   __snake_case    (       _lowerCAmelCase			:			List[Any]						)			->    Tuple:
   # For now runs on only two processes
   if state.num_processes != 2:
      return
   A_       :					str        =      create_tensor(_lowerCAmelCase						)
   A_       :					int        =      reduce(_lowerCAmelCase					,					"sum"						)
   A_       :					Optional[Any]        =      torch.tensor([4.0, 6]						).to(state.device						)
   assert torch.allclose(_lowerCAmelCase					,					_lowerCAmelCase						), f"{reduced_tensor} != {truth_tensor}"

def   __snake_case    (       _lowerCAmelCase			:			Union[str, Any]						)			->    Dict:
   # For now runs on only two processes
   if state.num_processes != 2:
      return
   A_       :					List[str]        =      create_tensor(_lowerCAmelCase						)
   A_       :					Tuple        =      reduce(_lowerCAmelCase					,					"mean"						)
   A_       :					List[str]        =      torch.tensor([2.0, 3]						).to(state.device						)
   assert torch.allclose(_lowerCAmelCase					,					_lowerCAmelCase						), f"{reduced_tensor} != {truth_tensor}"

def   __snake_case    (       _lowerCAmelCase			:			List[str]						)			->    Dict:
   # For xla_spawn (TPUs)
   main()

def   __snake_case    (       )			->    List[str]:
   A_       :					Tuple        =      PartialState()
   state.print(f"State: {state}"						)
   state.print("testing gather"						)
   test_gather(_lowerCAmelCase						)
   state.print("testing gather_object"						)
   test_gather_object(_lowerCAmelCase						)
   state.print("testing broadcast"						)
   test_broadcast(_lowerCAmelCase						)
   state.print("testing pad_across_processes"						)
   test_pad_across_processes(_lowerCAmelCase						)
   state.print("testing reduce_sum"						)
   test_reduce_sum(_lowerCAmelCase						)
   state.print("testing reduce_mean"						)
   test_reduce_mean(_lowerCAmelCase						)


if __name__ == "__main__":
 main()
