






"""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()
__SCREAMING_SNAKE_CASE   =   logging.get_logger()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__     ,      a__ = True ):





						'''simple docstring'''






						print(F"""Converting {name}...""" )

						with torch.no_grad():
												if hidden_sizes == 1_28:
																		if name[-1] == "S":
																								lowerCAmelCase   :Union[str, Any]						=			timm.create_model('levit_128s'     ,      pretrained=a_ )
																		else:
																								lowerCAmelCase   :Any						=			timm.create_model('levit_128'     ,      pretrained=a_ )
												if hidden_sizes == 1_92:
																		lowerCAmelCase   :List[Any]						=			timm.create_model('levit_192'     ,      pretrained=a_ )
												if hidden_sizes == 2_56:
																		lowerCAmelCase   :str						=			timm.create_model('levit_256'     ,      pretrained=a_ )
												if hidden_sizes == 3_84:
																		lowerCAmelCase   :int						=			timm.create_model('levit_384'     ,      pretrained=a_ )

												from_model.eval()
												lowerCAmelCase   :List[str]						=			LevitForImageClassificationWithTeacher(a_ ).eval()
												lowerCAmelCase   :str						=			OrderedDict()

												lowerCAmelCase   :Union[str, Any]						=			from_model.state_dict()
												lowerCAmelCase   :str						=			list(from_model.state_dict().keys() )
												lowerCAmelCase   :List[Any]						=			list(our_model.state_dict().keys() )
												print(len(a_ )     ,      len(a_ ) )
												for i in range(len(a_ ) ):
																		lowerCAmelCase   :Any						=			weights[og_keys[i]]
												our_model.load_state_dict(a_ )

												lowerCAmelCase   :List[Any]						=			torch.randn((2, 3, 2_24, 2_24) )
												lowerCAmelCase   :Union[str, Any]						=			from_model(a_ )
												lowerCAmelCase   :Optional[Any]						=			our_model(a_ ).logits

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

						lowerCAmelCase   :List[str]						=			name
						print(a_ )

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

												print(F"""Pushed {checkpoint_name}""" )


def        UpperCAmelCase      (					a__     ,      a__ = None     ,      a__ = True ):





						'''simple docstring'''






						lowerCAmelCase   :Dict						=			"""imagenet-1k-id2label.json"""
						lowerCAmelCase   :str						=			10_00
						lowerCAmelCase   :Optional[Any]						=			(1, num_labels)

						lowerCAmelCase   :Optional[int]						=			"""huggingface/label-files"""
						lowerCAmelCase   :Dict						=			num_labels
						lowerCAmelCase   :List[str]						=			json.load(open(hf_hub_download(a_     ,      a_     ,      repo_type='dataset' )     ,      'r' ) )
						lowerCAmelCase   :Optional[int]						=			{int(a_ ): v for k, v in idalabel.items()}

						lowerCAmelCase   :List[Any]						=			idalabel
						lowerCAmelCase   :int						=			{v: k for k, v in idalabel.items()}

						lowerCAmelCase   :Optional[Any]						=			partial(a_     ,      num_labels=a_     ,      idalabel=a_     ,      labelaid=a_ )

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

						lowerCAmelCase   :Optional[int]						=			{
						    """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]     ,      a_     ,      names_to_config[model_name]     ,      a_     ,      a_ )
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(names_to_hidden_sizes[model_name]     ,      a_     ,      a_     ,      a_     ,      a_ )
						return config, expected_shape


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   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',
		)

		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		__SCREAMING_SNAKE_CASE   =   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 inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import DecisionTransformerModel
      from transformers.models.decision_transformer.modeling_decision_transformer import (
          DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )




class      A     :






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase=13      ,  __lowerCAmelCase=7      ,  __lowerCAmelCase=6      ,  __lowerCAmelCase=17      ,  __lowerCAmelCase=23      ,  __lowerCAmelCase=11      ,  __lowerCAmelCase=True      ,  ):
       UpperCamelCase_					: Tuple            =				parent
       UpperCamelCase_					: List[Any]            =				batch_size
       UpperCamelCase_					: List[str]            =				seq_length
       UpperCamelCase_					: Dict            =				act_dim
       UpperCamelCase_					: Optional[int]            =				state_dim
       UpperCamelCase_					: Dict            =				hidden_size
       UpperCamelCase_					: str            =				max_length
       UpperCamelCase_					: int            =				is_training
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
       UpperCamelCase_					: Dict            =				floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
       UpperCamelCase_					: Union[str, Any]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: List[str]            =				floats_tensor((self.batch_size, self.seq_length, 1)			)
       UpperCamelCase_					: Optional[Any]            =				ids_tensor((self.batch_size, self.seq_length)      ,  vocab_size=10_00			)
       UpperCamelCase_					: Optional[Any]            =				random_attention_mask((self.batch_size, self.seq_length)			)

       UpperCamelCase_					: Tuple            =				self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )
 def 	_UpperCAmelCase							(				self			):
       return DecisionTransformerConfig(
           batch_size=self.batch_size      ,  seq_length=self.seq_length      ,  act_dim=self.act_dim      ,  state_dim=self.state_dim      ,  hidden_size=self.hidden_size      ,  max_length=self.max_length      ,  )
 def 	_UpperCAmelCase							(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  ):
       UpperCamelCase_					: Tuple            =				DecisionTransformerModel(config=__lowerCAmelCase			)
       model.to(__lowerCAmelCase			)
       model.eval()
       UpperCamelCase_					: str            =				model(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)

       self.parent.assertEqual(result.state_preds.shape      ,  states.shape			)
       self.parent.assertEqual(result.action_preds.shape      ,  actions.shape			)
       self.parent.assertEqual(result.return_preds.shape      ,  returns_to_go.shape			)
       self.parent.assertEqual(
           result.last_hidden_state.shape      ,  (self.batch_size, self.seq_length * 3, self.hidden_size)			)  # seq length *3 as there are 3 modelities: states, returns and actions





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				self.prepare_config_and_inputs()
       (
           (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      (
           UpperCamelCase_
       )     ,      
       )					: str            =				config_and_inputs
       UpperCamelCase_					: int            =				{
           """states""": states,
           """actions""": actions,
           """rewards""": rewards,
           """returns_to_go""": returns_to_go,
           """timesteps""": timesteps,
           """attention_mask""": attention_mask,
       }
       return config, inputs_dict






@require_torch
class      A     (   SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, unittest.TestCase    ):






 """simple docstring"""





 __a      :    Tuple			  =    (DecisionTransformerModel,) if is_torch_available() else ()
 __a      :    Union[str, Any]			  =    ()
 __a      :    int			  =    {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

 # Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
 __a      :    Optional[Any]			  =    False

 # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
 __a      :    int			  =    False
 __a      :    Tuple			  =    False
 __a      :    Dict			  =    False
 __a      :    Union[str, Any]			  =    False
 __a      :    int			  =    False
 __a      :    List[Any]			  =    False
 __a      :    List[str]			  =    False
 __a      :    Dict			  =    False
 __a      :    Optional[Any]			  =    False
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Dict            =				DecisionTransformerModelTester(self			)
       UpperCamelCase_					: Dict            =				ConfigTester(self      ,  config_class=__lowerCAmelCase      ,  hidden_size=37			)
 def 	_UpperCAmelCase							(				self			):
       self.config_tester.run_common_tests()
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Optional[int]            =				self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowerCAmelCase			)
 @slow
 def 	_UpperCAmelCase							(				self			):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCamelCase_					: Union[str, Any]            =				DecisionTransformerModel.from_pretrained(__lowerCAmelCase			)
             self.assertIsNotNone(__lowerCAmelCase			)





 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_     ,      UpperCamelCase_					: str            =				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
             UpperCamelCase_					: Union[str, Any]            =				model_class(__lowerCAmelCase			)
             UpperCamelCase_					: Optional[int]            =				inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             UpperCamelCase_					: Any            =				[*signature.parameters.keys()]

             UpperCamelCase_					: List[str]            =				[
                 """states""",
                 """actions""",
                 """rewards""",
                 """returns_to_go""",
                 """timesteps""",
                 """attention_mask""",
             ]

             self.assertListEqual(arg_names[: len(__lowerCAmelCase			)]      ,  __lowerCAmelCase			)






@require_torch
class      A     (   unittest.TestCase    ):






 """simple docstring"""





 @slow
 def 	_UpperCAmelCase							(				self			):
       UpperCamelCase_					: Tuple            =				2  # number of steps of autoregressive prediction we will perform
       UpperCamelCase_					: Dict            =				10  # defined by the RL environment, may be normalized
       UpperCamelCase_					: Any            =				DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""			)
       UpperCamelCase_					: Tuple            =				model.to(__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				model.config
       torch.manual_seed(0			)
       UpperCamelCase_					: str            =				torch.randn(1      ,  1      ,  config.state_dim			).to(device=__lowerCAmelCase      ,  dtype=torch.floataa			)  # env.reset()

       UpperCamelCase_					: List[str]            =				torch.tensor(
           [[0.24_27_93, -0.28_69_30_74, 0.8_74_26_13], [0.67_81_52_74, -0.08_10_10_85, -0.12_95_21_47]]      ,  device=__lowerCAmelCase			)

       UpperCamelCase_					: Tuple            =				torch.tensor(__lowerCAmelCase      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			).reshape(1      ,  1      ,  1			)
       UpperCamelCase_					: Optional[int]            =				state
       UpperCamelCase_					: int            =				torch.zeros(1      ,  0      ,  config.act_dim      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[int]            =				torch.zeros(1      ,  0      ,  device=__lowerCAmelCase      ,  dtype=torch.floataa			)
       UpperCamelCase_					: Optional[Any]            =				torch.tensor(0      ,  device=__lowerCAmelCase      ,  dtype=torch.long			).reshape(1      ,  1			)

       for step in range(__lowerCAmelCase			):
             UpperCamelCase_					: List[Any]            =				torch.cat([actions, torch.zeros(1      ,  1      ,  config.act_dim      ,  device=__lowerCAmelCase			)]      ,  dim=1			)
             UpperCamelCase_					: Optional[int]            =				torch.cat([rewards, torch.zeros(1      ,  1      ,  device=__lowerCAmelCase			)]      ,  dim=1			)

             UpperCamelCase_					: Union[str, Any]            =				torch.ones(1      ,  states.shape[1]			).to(dtype=torch.long      ,  device=states.device			)

             with torch.no_grad():
                   UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: List[Any]            =				model(
                       states=__lowerCAmelCase      ,  actions=__lowerCAmelCase      ,  rewards=__lowerCAmelCase      ,  returns_to_go=__lowerCAmelCase      ,  timesteps=__lowerCAmelCase      ,  attention_mask=__lowerCAmelCase      ,  return_dict=__lowerCAmelCase      ,  )

             self.assertEqual(action_pred.shape      ,  actions.shape			)
             self.assertTrue(torch.allclose(action_pred[0, -1]      ,  expected_outputs[step]      ,  atol=1E-4			)			)
             UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_     ,      UpperCamelCase_					: Union[str, Any]            =				(  # env.step(action)
                 torch.randn(1      ,  1      ,  config.state_dim			).to(device=__lowerCAmelCase      ,  dtype=torch.floataa			),
                 1.0,
                 False,
                 {},
             )

             UpperCamelCase_					: Any            =				action_pred[0, -1]
             UpperCamelCase_					: int            =				torch.cat([states, state]      ,  dim=1			)
             UpperCamelCase_					: str            =				returns_to_go[0, -1] - reward
             UpperCamelCase_					: int            =				torch.cat([returns_to_go, pred_return.reshape(1      ,  1      ,  1			)]      ,  dim=1			)
             UpperCamelCase_					: int            =				torch.cat(
                 [timesteps, torch.ones((1, 1)      ,  device=__lowerCAmelCase      ,  dtype=torch.long			) * (step + 1)]      ,  dim=1			)






