


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()
lowerCamelCase__							:						Dict							     =   logging.get_logger()







def SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase = True				)    ->				Optional[int]:
  print(F"""Converting {name}..."""				)

  with torch.no_grad():
    if hidden_sizes == 128:
      if name[-1] == "S":
        snake_case__						= timm.create_model('''levit_128s'''							,   pretrained=lowerCAmelCase__				)
      else:
        snake_case__						= timm.create_model('''levit_128'''							,   pretrained=lowerCAmelCase__				)
    if hidden_sizes == 192:
      snake_case__						= timm.create_model('''levit_192'''							,   pretrained=lowerCAmelCase__				)
    if hidden_sizes == 256:
      snake_case__						= timm.create_model('''levit_256'''							,   pretrained=lowerCAmelCase__				)
    if hidden_sizes == 384:
      snake_case__						= timm.create_model('''levit_384'''							,   pretrained=lowerCAmelCase__				)

    from_model.eval()
    snake_case__						= LevitForImageClassificationWithTeacher(lowerCAmelCase__				).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(lowerCAmelCase__				)							,   len(lowerCAmelCase__				)				)
    for i in range(len(lowerCAmelCase__				)				):
      snake_case__						= weights[og_keys[i]]
    our_model.load_state_dict(lowerCAmelCase__				)

    snake_case__						= torch.randn((2, 3, 224, 224)				)
    snake_case__						= from_model(lowerCAmelCase__				)
    snake_case__						= our_model(lowerCAmelCase__				).logits

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

  snake_case__						= name
  print(lowerCAmelCase__				)

  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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase							,   __lowerCAmelCase = None							,   __lowerCAmelCase = True				)    ->				Optional[int]:
  snake_case__						= '''imagenet-1k-id2label.json'''
  snake_case__						= 1000
  snake_case__						= (1, num_labels)

  snake_case__						= '''huggingface/label-files'''
  snake_case__						= num_labels
  snake_case__						= json.load(open(hf_hub_download(lowerCAmelCase__							,   lowerCAmelCase__							,   repo_type='''dataset'''				)							,   '''r'''				)				)
  snake_case__						= {int(lowerCAmelCase__				): v for k, v in idalabel.items()}

  snake_case__						= idalabel
  snake_case__						= {v: k for k, v in idalabel.items()}

  snake_case__						= partial(lowerCAmelCase__							,   num_labels=lowerCAmelCase__							,   idalabel=lowerCAmelCase__							,   labelaid=lowerCAmelCase__				)

  snake_case__						= {
      '''levit-128S''': 128,
      '''levit-128''': 128,
      '''levit-192''': 192,
      '''levit-256''': 256,
      '''levit-384''': 384,
  }

  snake_case__						= {
      '''levit-128S''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]							,   num_attention_heads=[4, 6, 8]							,   depths=[2, 3, 4]							,   key_dim=[16, 16, 16]							,   drop_path_rate=0							,   ),
      '''levit-128''': ImageNetPreTrainedConfig(
          hidden_sizes=[128, 256, 384]							,   num_attention_heads=[4, 8, 12]							,   depths=[4, 4, 4]							,   key_dim=[16, 16, 16]							,   drop_path_rate=0							,   ),
      '''levit-192''': ImageNetPreTrainedConfig(
          hidden_sizes=[192, 288, 384]							,   num_attention_heads=[3, 5, 6]							,   depths=[4, 4, 4]							,   key_dim=[32, 32, 32]							,   drop_path_rate=0							,   ),
      '''levit-256''': ImageNetPreTrainedConfig(
          hidden_sizes=[256, 384, 512]							,   num_attention_heads=[4, 6, 8]							,   depths=[4, 4, 4]							,   key_dim=[32, 32, 32]							,   drop_path_rate=0							,   ),
      '''levit-384''': ImageNetPreTrainedConfig(
          hidden_sizes=[384, 512, 768]							,   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]							,   lowerCAmelCase__							,   names_to_config[model_name]							,   lowerCAmelCase__							,   lowerCAmelCase__				)
  else:
    for model_name, config in names_to_config.items():
      convert_weight_and_push(names_to_hidden_sizes[model_name]							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__							,   lowerCAmelCase__				)
  return config, expected_shape


if __name__ == "__main__":
   lowerCamelCase__							:						str							     =   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""",
   )

   lowerCamelCase__							:						Optional[Any]							     =   parser.parse_args()
   lowerCamelCase__							:						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 importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			logging.get_logger(__name__)




def 							_UpperCamelCase				(   )      -> List[Any]:
							# Get the sagemaker specific mp parameters from smp_options variable.
							SCREAMING_SNAKE_CASE_				=				os.getenv('SM_HP_MP_PARAMETERS'		,'{}'   )
							try:
														# Parse it and check the field "partitions" is included, it is required for model parallel.
														SCREAMING_SNAKE_CASE_				=				json.loads(lowerCAmelCase__   )
														if "partitions" not in smp_options:
																					return False
							except json.JSONDecodeError:
														return False

							# Get the sagemaker specific framework parameters from mpi_options variable.
							SCREAMING_SNAKE_CASE_				=				os.getenv('SM_FRAMEWORK_PARAMS'		,'{}'   )
							try:
														# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
														SCREAMING_SNAKE_CASE_				=				json.loads(lowerCAmelCase__   )
														if not mpi_options.get('sagemaker_mpi_enabled'		,lowerCAmelCase__   ):
																					return False
							except json.JSONDecodeError:
														return False
							# Lastly, check if the `smdistributed` module is present.
							return importlib.util.find_spec('smdistributed'   ) is not None


if is_sagemaker_model_parallel_available():
							import smdistributed.modelparallel.torch as smp

							smp.init()






@dataclass
class    snake_case     (      lowercase_		):






				"""simple docstring"""
				_a    	=     field(
				    default="""""",						metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""},						)



				def   a__					(    self       )     ->							Union[str, Any]:
											super().__post_init__()
											warnings.warn(
											    '`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '
											    '`TrainingArguments` instead.',     _lowercase,     )



				@cached_property
				def   a__					(    self       )     ->							"torch.device":
											logger.info('PyTorch: setting up devices'       )
											if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
																		logger.warning(
																		    'torch.distributed process group is initialized, but local_rank == -1. '
																		    'In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'       )
											if self.no_cuda:
																		SCREAMING_SNAKE_CASE_				=				torch.device('cpu'       )
																		SCREAMING_SNAKE_CASE_				=				0
											elif is_sagemaker_model_parallel_available():
																		SCREAMING_SNAKE_CASE_				=				smp.local_rank()
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     _lowercase       )
																		SCREAMING_SNAKE_CASE_				=				1
											elif is_sagemaker_dp_enabled():
																		import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

																		torch.distributed.init_process_group(backend='smddp',     timeout=self.ddp_timeout_delta       )
																		SCREAMING_SNAKE_CASE_				=				int(os.getenv('SMDATAPARALLEL_LOCAL_RANK'       )       )
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     self.local_rank       )
																		SCREAMING_SNAKE_CASE_				=				1
											elif self.local_rank == -1:
																		# if n_gpu is > 1 we'll use nn.DataParallel.
																		# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
																		# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
																		# trigger an error that a device index is missing. Index 0 takes into account the
																		# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
																		# will use the first GPU in that env, i.e. GPU#1
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda:0' if torch.cuda.is_available() else 'cpu'       )
																		# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
																		# the default value.
																		SCREAMING_SNAKE_CASE_				=				torch.cuda.device_count()
											else:
																		# Here, we'll use torch.distributed.
																		# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
																		if not torch.distributed.is_initialized():
																									torch.distributed.init_process_group(backend='nccl',     timeout=self.ddp_timeout_delta       )
																		SCREAMING_SNAKE_CASE_				=				torch.device('cuda',     self.local_rank       )
																		SCREAMING_SNAKE_CASE_				=				1

											if device.type == "cuda":
																		torch.cuda.set_device(_lowercase       )

											return device



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											if is_sagemaker_model_parallel_available():
																		return smp.dp_size()

											return super().world_size



				@property
				def   a__					(    self       )     ->							Optional[Any]:
											return not is_sagemaker_model_parallel_available()





				@property
				def   a__					(    self       )     ->							Tuple:
											return False





