
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 					lowercase__:



	"""simple docstring"""




	def __init__(			self    :     str			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Tuple=1_3			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=7			,				SCREAMING_SNAKE_CASE_    :     Dict=6			,				SCREAMING_SNAKE_CASE_    :     int=1_7			,				SCREAMING_SNAKE_CASE_    :     str=2_3			,				SCREAMING_SNAKE_CASE_    :     Dict=1_1			,				SCREAMING_SNAKE_CASE_    :     List[str]=True			,				)			->	Any:
					lowercase_	    =   parent
					lowercase_	    =   batch_size
					lowercase_	    =   seq_length
					lowercase_	    =   act_dim
					lowercase_	    =   state_dim
					lowercase_	    =   hidden_size
					lowercase_	    =   max_length
					lowercase_	    =   is_training
	def 		_lowercase    (			self    :     str			)			->	Optional[Any]:
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, self.state_dim)			)
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, self.act_dim)			)
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, 1)			)
					lowercase_	    =   floats_tensor((self.batch_size, self.seq_length, 1)			)
					lowercase_	    =   ids_tensor((self.batch_size, self.seq_length)			,				vocab_size=1_0_0_0			)
					lowercase_	    =   random_attention_mask((self.batch_size, self.seq_length)			)

					lowercase_	    =   self.get_config()

					return (
					    config,
					    states,
					    actions,
					    rewards,
					    returns_to_go,
					    timesteps,
					    attention_mask,
					)
	def 		_lowercase    (			self    :     int			)			->	List[Any]:
					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 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     List[Any]			,				SCREAMING_SNAKE_CASE_    :     Tuple			,				SCREAMING_SNAKE_CASE_    :     Any			,				SCREAMING_SNAKE_CASE_    :     int			,				SCREAMING_SNAKE_CASE_    :     List[str]			,				)			->	Dict:
					lowercase_	    =   DecisionTransformerModel(config=SCREAMING_SNAKE_CASE_			)
					model.to(SCREAMING_SNAKE_CASE_			)
					model.eval()
					lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

					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 		_lowercase    (			self    :     Union[str, Any]			)			->	Dict:
					lowercase_	    =   self.prepare_config_and_inputs()
					(
					    (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      (
					    lowercase_
					)    ,      
					)	    =   config_and_inputs
					lowercase_	    =   {
					    '''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 					lowercase__(							UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Dict       	=      (DecisionTransformerModel,) if is_torch_available() else ()
	a     :int       	=      ()
	a     :Optional[Any]       	=      {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

	# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
	a     :Optional[int]       	=      False
	a     :Optional[int]       	=      False
	a     :Union[str, Any]       	=      False
	a     :Optional[Any]       	=      False
	a     :Optional[int]       	=      False
	a     :Optional[Any]       	=      False
	a     :Any       	=      False
	a     :Union[str, Any]       	=      False
	a     :Tuple       	=      False
	def 		_lowercase    (			self    :     Union[str, Any]			)			->	Optional[int]:
					lowercase_	    =   DecisionTransformerModelTester(self			)
					lowercase_	    =   ConfigTester(self			,				config_class=SCREAMING_SNAKE_CASE_			,				hidden_size=3_7			)
	def 		_lowercase    (			self    :     Any			)			->	str:
					self.config_tester.run_common_tests()
	def 		_lowercase    (			self    :     Dict			)			->	int:
					lowercase_	    =   self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)
	@slow
	def 		_lowercase    (			self    :     List[str]			)			->	Union[str, Any]:
					for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowercase_	    =   DecisionTransformerModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)




	def 		_lowercase    (			self    :     Optional[Any]			)			->	int:
					lowercase_    ,      lowercase_	    =   self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowercase_	    =   model_class(SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   inspect.signature(model.forward			)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									lowercase_	    =   [*signature.parameters.keys()]

									lowercase_	    =   [
									    '''states''',
									    '''actions''',
									    '''rewards''',
									    '''returns_to_go''',
									    '''timesteps''',
									    '''attention_mask''',
									]

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

@require_torch
class 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




	@slow
	def 		_lowercase    (			self    :     Optional[Any]			)			->	str:
					lowercase_	    =   2  # number of steps of autoregressive prediction we will perform
					lowercase_	    =   1_0  # defined by the RL environment, may be normalized
					lowercase_	    =   DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''			)
					lowercase_	    =   model.to(SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   model.config
					torch.manual_seed(0			)
					lowercase_	    =   torch.randn(1			,				1			,				config.state_dim			).to(device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			)  # env.reset()

					lowercase_	    =   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=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   torch.tensor(SCREAMING_SNAKE_CASE_			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			).reshape(1			,				1			,				1			)
					lowercase_	    =   state
					lowercase_	    =   torch.zeros(1			,				0			,				config.act_dim			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			)
					lowercase_	    =   torch.zeros(1			,				0			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			)
					lowercase_	    =   torch.tensor(0			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.long			).reshape(1			,				1			)

					for step in range(SCREAMING_SNAKE_CASE_			):
									lowercase_	    =   torch.cat([actions, torch.zeros(1			,				1			,				config.act_dim			,				device=SCREAMING_SNAKE_CASE_			)]			,				dim=1			)
									lowercase_	    =   torch.cat([rewards, torch.zeros(1			,				1			,				device=SCREAMING_SNAKE_CASE_			)]			,				dim=1			)

									lowercase_	    =   torch.ones(1			,				states.shape[1]			).to(dtype=torch.long			,				device=states.device			)

									with torch.no_grad():
													lowercase_    ,      lowercase_    ,      lowercase_	    =   model(
													    states=SCREAMING_SNAKE_CASE_			,				actions=SCREAMING_SNAKE_CASE_			,				rewards=SCREAMING_SNAKE_CASE_			,				returns_to_go=SCREAMING_SNAKE_CASE_			,				timesteps=SCREAMING_SNAKE_CASE_			,				attention_mask=SCREAMING_SNAKE_CASE_			,				return_dict=SCREAMING_SNAKE_CASE_			,				)

									self.assertEqual(action_pred.shape			,				actions.shape			)
									self.assertTrue(torch.allclose(action_pred[0, -1]			,				expected_outputs[step]			,				atol=1e-4			)			)
									lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   (  # env.step(action)
									    torch.randn(1			,				1			,				config.state_dim			).to(device=SCREAMING_SNAKE_CASE_			,				dtype=torch.floataa			),
									    1.0,
									    False,
									    {},
									)

									lowercase_	    =   action_pred[0, -1]
									lowercase_	    =   torch.cat([states, state]			,				dim=1			)
									lowercase_	    =   returns_to_go[0, -1] - reward
									lowercase_	    =   torch.cat([returns_to_go, pred_return.reshape(1			,				1			,				1			)]			,				dim=1			)
									lowercase_	    =   torch.cat(
									    [timesteps, torch.ones((1, 1)			,				device=SCREAMING_SNAKE_CASE_			,				dtype=torch.long			) * (step + 1)]			,				dim=1			)






import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				if "cls_token" in name:
								lowercase_	    =   name.replace('''cls_token'''		,  '''vit.embeddings.cls_token'''   )
				if "mask_token" in name:
								lowercase_	    =   name.replace('''mask_token'''		,  '''decoder.mask_token'''   )
				if "decoder_pos_embed" in name:
								lowercase_	    =   name.replace('''decoder_pos_embed'''		,  '''decoder.decoder_pos_embed'''   )
				if "pos_embed" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''pos_embed'''		,  '''vit.embeddings.position_embeddings'''   )
				if "patch_embed.proj" in name:
								lowercase_	    =   name.replace('''patch_embed.proj'''		,  '''vit.embeddings.patch_embeddings.projection'''   )
				if "patch_embed.norm" in name:
								lowercase_	    =   name.replace('''patch_embed.norm'''		,  '''vit.embeddings.norm'''   )
				if "decoder_blocks" in name:
								lowercase_	    =   name.replace('''decoder_blocks'''		,  '''decoder.decoder_layers'''   )
				if "blocks" in name:
								lowercase_	    =   name.replace('''blocks'''		,  '''vit.encoder.layer'''   )
				if "attn.proj" in name:
								lowercase_	    =   name.replace('''attn.proj'''		,  '''attention.output.dense'''   )
				if "attn" in name:
								lowercase_	    =   name.replace('''attn'''		,  '''attention.self'''   )
				if "norm1" in name:
								lowercase_	    =   name.replace('''norm1'''		,  '''layernorm_before'''   )
				if "norm2" in name:
								lowercase_	    =   name.replace('''norm2'''		,  '''layernorm_after'''   )
				if "mlp.fc1" in name:
								lowercase_	    =   name.replace('''mlp.fc1'''		,  '''intermediate.dense'''   )
				if "mlp.fc2" in name:
								lowercase_	    =   name.replace('''mlp.fc2'''		,  '''output.dense'''   )
				if "decoder_embed" in name:
								lowercase_	    =   name.replace('''decoder_embed'''		,  '''decoder.decoder_embed'''   )
				if "decoder_norm" in name:
								lowercase_	    =   name.replace('''decoder_norm'''		,  '''decoder.decoder_norm'''   )
				if "decoder_pred" in name:
								lowercase_	    =   name.replace('''decoder_pred'''		,  '''decoder.decoder_pred'''   )
				if "norm.weight" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.weight'''		,  '''vit.layernorm.weight'''   )
				if "norm.bias" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.bias'''		,  '''vit.layernorm.bias'''   )

				return name




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(snake_case__   )

								if "qkv" in key:
												lowercase_	    =   key.split('''.'''   )
												lowercase_	    =   int(key_split[1]   )
												if "decoder_blocks" in key:
																lowercase_	    =   config.decoder_hidden_size
																lowercase_	    =   '''decoder.decoder_layers.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]
												else:
																lowercase_	    =   config.hidden_size
																lowercase_	    =   '''vit.encoder.layer.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]

								else:
												lowercase_	    =   val

				return orig_state_dict




def 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

				lowercase_	    =   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   )
				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )
				lowercase_	    =   image_processor(images=snake_case__		,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

				# verify logits
				assert torch.allclose(logits[0, :3, :3]		,  snake_case__		,  atol=1e-4   )

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

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


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
						    type=str,
						    help='URL of the checkpoint 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         =   parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




