





'''simple docstring'''



import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__magic_name__   :						int    =			logging.get_logger()




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = True       ):







  '''simple docstring'''



  print(f'''Converting {name}...'''       )

  with torch.no_grad():
    if hidden_sizes == 1_28:
      if name[-1] == "S":
        _snake_case     		=				timm.create_model("levit_128s"					,	pretrained=SCREAMING_SNAKE_CASE__       )
      else:
        _snake_case     		=				timm.create_model("levit_128"					,	pretrained=SCREAMING_SNAKE_CASE__       )
    if hidden_sizes == 1_92:
      _snake_case     		=				timm.create_model("levit_192"					,	pretrained=SCREAMING_SNAKE_CASE__       )
    if hidden_sizes == 2_56:
      _snake_case     		=				timm.create_model("levit_256"					,	pretrained=SCREAMING_SNAKE_CASE__       )
    if hidden_sizes == 3_84:
      _snake_case     		=				timm.create_model("levit_384"					,	pretrained=SCREAMING_SNAKE_CASE__       )

    from_model.eval()
    _snake_case     		=				LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE__       ).eval()
    _snake_case     		=				OrderedDict()

    _snake_case     		=				from_model.state_dict()
    _snake_case     		=				list(from_model.state_dict().keys()       )
    _snake_case     		=				list(our_model.state_dict().keys()       )
    print(len(SCREAMING_SNAKE_CASE__       )					,	len(SCREAMING_SNAKE_CASE__       )       )
    for i in range(len(SCREAMING_SNAKE_CASE__       )       ):
      _snake_case     		=				weights[og_keys[i]]
    our_model.load_state_dict(SCREAMING_SNAKE_CASE__       )

    _snake_case     		=				torch.randn((2, 3, 2_24, 2_24)       )
    _snake_case     		=				from_model(SCREAMING_SNAKE_CASE__       )
    _snake_case     		=				our_model(SCREAMING_SNAKE_CASE__       ).logits

  assert torch.allclose(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       ), "The model logits don't match the original one."

  _snake_case     		=				name
  print(SCREAMING_SNAKE_CASE__       )

  if push_to_hub:
    our_model.save_pretrained(save_directory / checkpoint_name       )
    _snake_case     		=				LevitImageProcessor()
    image_processor.save_pretrained(save_directory / checkpoint_name       )

    print(f'''Pushed {checkpoint_name}'''       )




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__ = None					,	SCREAMING_SNAKE_CASE__ = True       ):







  '''simple docstring'''



  _snake_case     		=				"imagenet-1k-id2label.json"
  _snake_case     		=				10_00
  _snake_case     		=				(1, num_labels)

  _snake_case     		=				"huggingface/label-files"
  _snake_case     		=				num_labels
  _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     		=				idalabel
  _snake_case     		=				{v: k for k, v in idalabel.items()}

  _snake_case     		=				partial(SCREAMING_SNAKE_CASE__					,	num_labels=SCREAMING_SNAKE_CASE__					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__       )

  _snake_case     		=				{
      "levit-128S": 1_28,
      "levit-128": 1_28,
      "levit-192": 1_92,
      "levit-256": 2_56,
      "levit-384": 3_84,
  }

  _snake_case     		=				{
      "levit-128S": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]					,	num_attention_heads=[4, 6, 8]					,	depths=[2, 3, 4]					,	key_dim=[16, 16, 16]					,	drop_path_rate=0					,	),
      "levit-128": ImageNetPreTrainedConfig(
          hidden_sizes=[1_28, 2_56, 3_84]					,	num_attention_heads=[4, 8, 12]					,	depths=[4, 4, 4]					,	key_dim=[16, 16, 16]					,	drop_path_rate=0					,	),
      "levit-192": ImageNetPreTrainedConfig(
          hidden_sizes=[1_92, 2_88, 3_84]					,	num_attention_heads=[3, 5, 6]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0					,	),
      "levit-256": ImageNetPreTrainedConfig(
          hidden_sizes=[2_56, 3_84, 5_12]					,	num_attention_heads=[4, 6, 8]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0					,	),
      "levit-384": ImageNetPreTrainedConfig(
          hidden_sizes=[3_84, 5_12, 7_68]					,	num_attention_heads=[6, 9, 12]					,	depths=[4, 4, 4]					,	key_dim=[32, 32, 32]					,	drop_path_rate=0.1					,	),
  }

  if model_name:
    convert_weight_and_push(
        names_to_hidden_sizes[model_name]					,	SCREAMING_SNAKE_CASE__					,	names_to_config[model_name]					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__       )
  return config, expected_shape


if __name__ == "__main__":
 __magic_name__   :						List[Any]    =			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--model_name""",
     default=None,
     type=str,
     help="""The name of the model you wish to convert, it must be one of the supported Levit* architecture,""",
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""levit-dump-folder/""",
     type=Path,
     required=False,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")
 parser.add_argument(
     """--no-push_to_hub""",
     dest="""push_to_hub""",
     action="""store_false""",
     help="""Do not push model and image processor to the hub""",
 )

 __magic_name__   :						str    =			parser.parse_args()
 __magic_name__   :						Path    =			args.pytorch_dump_folder_path
 pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
 convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)









'''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_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for _ in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
  return lrs




def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=10       ):







  '''simple docstring'''



  _snake_case     		=				[]
  for step in range(SCREAMING_SNAKE_CASE__       ):
    lrs.append(scheduler.get_lr()[0]       )
    scheduler.step()
    if step == num_steps // 2:
      with tempfile.TemporaryDirectory() as tmpdirname:
        _snake_case     		=				os.path.join(SCREAMING_SNAKE_CASE__					,	"schedule.bin"       )
        torch.save(scheduler.state_dict()					,	SCREAMING_SNAKE_CASE__       )

        _snake_case     		=				torch.load(SCREAMING_SNAKE_CASE__       )
        scheduler.load_state_dict(SCREAMING_SNAKE_CASE__       )
  return lrs


@require_torch
class       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase	)

     def 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				AdamW(params=[w]						,   lr=2e-1						,   weight_decay=0.0	)
       for _ in range(100	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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 	UpperCamelCase(					self	):
       _snake_case     		=				torch.tensor([0.1, -0.2, -0.1]						,   requires_grad=lowerCamelCase	)
       _snake_case     		=				torch.tensor([0.4, 0.2, -0.5]	)
       _snake_case     		=				nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       _snake_case     		=				Adafactor(
           params=[w]						,   lr=1e-2						,   eps=(1e-30, 1e-3)						,   clip_threshold=1.0						,   decay_rate=-0.8						,   betaa=lowerCamelCase						,   weight_decay=0.0						,   relative_step=lowerCamelCase						,   scale_parameter=lowerCamelCase						,   warmup_init=lowerCamelCase						,   )
       for _ in range(1_000	):
         _snake_case     		=				criterion(lowerCamelCase						,   lowerCamelCase	)
         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       __SCREAMING_SNAKE_CASE	(						unittest.TestCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						Any      =   nn.Linear(50					,    50			) if is_torch_available() else None
     UpperCAmelCase__       :						str      =   AdamW(m.parameters()					,    lr=10.0			) if is_torch_available() else None
     UpperCAmelCase__       :						Optional[Any]      =   10

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase=None	):
       self.assertEqual(len(lowerCamelCase	)						,   len(lowerCamelCase	)	)
       for a, b in zip(lowerCamelCase						,   lowerCamelCase	):
         self.assertAlmostEqual(lowerCamelCase						,   lowerCamelCase						,   delta=lowerCamelCase						,   msg=lowerCamelCase	)






     def 	UpperCamelCase(					self	):
       _snake_case     		=				{"num_warmup_steps": 2, "num_training_steps": 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       _snake_case     		=				{
           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():
         _snake_case		,		_snake_case     		=				data

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

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







class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase	):
       _snake_case     		=				fn

     def __call__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       return self.fn(*lowerCamelCase						,   **lowerCamelCase	)






     @classmethod
     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				list(map(self						,   scheduler.lr_lambdas	)	)


