


'''simple docstring'''

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 __snake_case      (   UpperCAmelCase_		:		Dict	):
 lowerCamelCase_  				=						SwinvaConfig()
 lowerCamelCase_  				=						swinva_name.split("_"	)

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

 if model_size == "tiny":
  lowerCamelCase_  				=						96
  lowerCamelCase_  				=						(2, 2, 6, 2)
  lowerCamelCase_  				=						(3, 6, 12, 24)
 elif model_size == "small":
  lowerCamelCase_  				=						96
  lowerCamelCase_  				=						(2, 2, 18, 2)
  lowerCamelCase_  				=						(3, 6, 12, 24)
 elif model_size == "base":
  lowerCamelCase_  				=						128
  lowerCamelCase_  				=						(2, 2, 18, 2)
  lowerCamelCase_  				=						(4, 8, 16, 32)
 else:
  lowerCamelCase_  				=						192
  lowerCamelCase_  				=						(2, 2, 18, 2)
  lowerCamelCase_  				=						(6, 12, 24, 48)

 if "to" in swinva_name:
  lowerCamelCase_  				=						(12, 12, 12, 6)

 if ("22k" in swinva_name) and ("to" not in swinva_name):
  lowerCamelCase_  				=						21841
  lowerCamelCase_  				=						"huggingface/label-files"
  lowerCamelCase_  				=						"imagenet-22k-id2label.json"
  lowerCamelCase_  				=						json.load(open(hf_hub_download(UpperCAmelCase_      ,       UpperCAmelCase_      ,       repo_type="dataset"	)      ,       "r"	)	)
  lowerCamelCase_  				=						{int(UpperCAmelCase_	): v for k, v in idalabel.items()}
  lowerCamelCase_  				=						idalabel
  lowerCamelCase_  				=						{v: k for k, v in idalabel.items()}

 else:
  lowerCamelCase_  				=						1000
  lowerCamelCase_  				=						"huggingface/label-files"
  lowerCamelCase_  				=						"imagenet-1k-id2label.json"
  lowerCamelCase_  				=						json.load(open(hf_hub_download(UpperCAmelCase_      ,       UpperCAmelCase_      ,       repo_type="dataset"	)      ,       "r"	)	)
  lowerCamelCase_  				=						{int(UpperCAmelCase_	): v for k, v in idalabel.items()}
  lowerCamelCase_  				=						idalabel
  lowerCamelCase_  				=						{v: k for k, v in idalabel.items()}

 lowerCamelCase_  				=						img_size
 lowerCamelCase_  				=						num_classes
 lowerCamelCase_  				=						embed_dim
 lowerCamelCase_  				=						depths
 lowerCamelCase_  				=						num_heads
 lowerCamelCase_  				=						window_size

 return config





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]	):
 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 "attn.proj" in name:
  lowerCamelCase_  				=						name.replace("attn.proj"      ,       "attention.output.dense"	)
 if "attn" in name:
  lowerCamelCase_  				=						name.replace("attn"      ,       "attention.self"	)
 if "norm1" in name:
  lowerCamelCase_  				=						name.replace("norm1"      ,       "layernorm_before"	)
 if "norm2" in name:
  lowerCamelCase_  				=						name.replace("norm2"      ,       "layernorm_after"	)
 if "mlp.fc1" in name:
  lowerCamelCase_  				=						name.replace("mlp.fc1"      ,       "intermediate.dense"	)
 if "mlp.fc2" in name:
  lowerCamelCase_  				=						name.replace("mlp.fc2"      ,       "output.dense"	)
 if "q_bias" in name:
  lowerCamelCase_  				=						name.replace("q_bias"      ,       "query.bias"	)
 if "k_bias" in name:
  lowerCamelCase_  				=						name.replace("k_bias"      ,       "key.bias"	)
 if "v_bias" in name:
  lowerCamelCase_  				=						name.replace("v_bias"      ,       "value.bias"	)
 if "cpb_mlp" in name:
  lowerCamelCase_  				=						name.replace("cpb_mlp"      ,       "continuous_position_bias_mlp"	)
 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_  				=						"swinv2." + name

 return name





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		List[Any]	):
 for key in orig_state_dict.copy().keys():
  lowerCamelCase_  				=						orig_state_dict.pop(UpperCAmelCase_	)

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

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

 return orig_state_dict





def __snake_case      (   UpperCAmelCase_		:		Union[str, Any]      ,       UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						timm.create_model(UpperCAmelCase_      ,       pretrained=UpperCAmelCase_	)
 timm_model.eval()

 lowerCamelCase_  				=						get_swinva_config(UpperCAmelCase_	)
 lowerCamelCase_  				=						SwinvaForImageClassification(UpperCAmelCase_	)
 model.eval()

 lowerCamelCase_  				=						convert_state_dict(timm_model.state_dict()      ,       UpperCAmelCase_	)
 model.load_state_dict(UpperCAmelCase_	)

 lowerCamelCase_  				=						"http://images.cocodataset.org/val2017/000000039769.jpg"

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

 lowerCamelCase_  				=						timm_model(inputs["pixel_values"]	)
 lowerCamelCase_  				=						model(**UpperCAmelCase_	).logits

 assert torch.allclose(UpperCAmelCase_      ,       UpperCAmelCase_      ,       atol=1E-3	)

 print(F'''Saving model {swinva_name} to {pytorch_dump_folder_path}'''	)
 model.save_pretrained(UpperCAmelCase_	)

 print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
 image_processor.save_pretrained(UpperCAmelCase_	)

 model.push_to_hub(
     repo_path_or_name=Path(UpperCAmelCase_      ,       UpperCAmelCase_	)      ,       organization="nandwalritik"      ,       commit_message="Add model"      ,       )


if __name__ == "__main__":
     a_						:       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."""
     )

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







'''simple docstring'''

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]=10	):
 lowerCamelCase_  				=						[]
 for _ in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
 return lrs





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[Any]=10	):
 lowerCamelCase_  				=						[]
 for step in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "schedule.bin"	)
    torch.save(scheduler.state_dict()      ,       UpperCAmelCase_	)

    lowerCamelCase_  				=						torch.load(UpperCAmelCase_	)
    scheduler.load_state_dict(UpperCAmelCase_	)
 return lrs





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						AdamW(params=[w]    ,						lr=2e-1    ,						weight_decay=0.0  )
   for _ in range(100  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						Adafactor(
       params=[w]    ,						lr=1e-2    ,						eps=(1e-30, 1e-3)    ,						clip_threshold=1.0    ,						decay_rate=-0.8    ,						betaa=UpperCamelCase    ,						weight_decay=0.0    ,						relative_step=UpperCamelCase    ,						scale_parameter=UpperCamelCase    ,						warmup_init=UpperCamelCase    ,						)
   for _ in range(1000  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						nn.Linear(50    ,		50    ) if is_torch_available() else None
  _lowerCamelCase						=						AdamW(m.parameters()    ,		lr=10.0    ) if is_torch_available() else None
  _lowerCamelCase						=						10






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase    ,						msg=UpperCamelCase  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{"num_warmup_steps": 2, "num_training_steps": 10}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   lowerCamelCase_  				=						{
       get_constant_schedule: ({}, [10.0] * self.num_steps),
       get_constant_schedule_with_warmup: (
           {"num_warmup_steps": 4},
           [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
       ),
       get_linear_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
       ),
       get_cosine_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
       ),
       get_cosine_with_hard_restarts_schedule_with_warmup: (
           {**common_kwargs, "num_cycles": 2},
           [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
       ),
       get_polynomial_decay_schedule_with_warmup: (
           {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
           [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
       ),
       get_inverse_sqrt_schedule: (
           {"num_warmup_steps": 2},
           [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
       ),
   }

   for scheduler_func, data in scheds.items():
    lowerCamelCase_				,lowerCamelCase_  				=						data

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    self.assertEqual(len([scheduler.get_lr()[0]]  )    ,						1  )
    lowerCamelCase_  				=						unwrap_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListAlmostEqual(
        UpperCamelCase    ,						UpperCamelCase    ,						tol=1e-2    ,						msg=f'''failed for {scheduler_func} in normal scheduler'''    ,						)

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    if scheduler_func.__name__ != "get_constant_schedule":
     LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase  )  # wrap to test picklability of the schedule
    lowerCamelCase_  				=						unwrap_and_save_reload_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListEqual(UpperCamelCase    ,						UpperCamelCase    ,						msg=f'''failed for {scheduler_func} in save and reload'''  )





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						fn






  def __call__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.fn(*UpperCamelCase    ,						**UpperCamelCase  )







  @classmethod
  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						list(map(self    ,						scheduler.lr_lambdas  )  )



