






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 			SCREAMING_SNAKE_CASE      :
     '''simple docstring'''


     def __init__(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Dict       ,			UpperCAmelCase_							:						Union[str, Any]=13       ,			UpperCAmelCase_							:						Any=7       ,			UpperCAmelCase_							:						Union[str, Any]=6       ,			UpperCAmelCase_							:						Optional[Any]=17       ,			UpperCAmelCase_							:						Optional[Any]=23       ,			UpperCAmelCase_							:						Optional[int]=11       ,			UpperCAmelCase_							:						Any=True       ,			):
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     parent
       SCREAMING_SNAKE_CASE							:   Dict						    =     batch_size
       SCREAMING_SNAKE_CASE							:   Tuple						    =     seq_length
       SCREAMING_SNAKE_CASE							:   List[str]						    =     act_dim
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     state_dim
       SCREAMING_SNAKE_CASE							:   Optional[Any]						    =     hidden_size
       SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     max_length
       SCREAMING_SNAKE_CASE							:   Optional[int]						    =     is_training


     def 					_A							(					self							:						List[str]   ):
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((self.batch_size, self.seq_length, self.state_dim)   )
       SCREAMING_SNAKE_CASE							:   List[Any]						    =     floats_tensor((self.batch_size, self.seq_length, self.act_dim)   )
       SCREAMING_SNAKE_CASE							:   int						    =     floats_tensor((self.batch_size, self.seq_length, 1)   )
       SCREAMING_SNAKE_CASE							:   Any						    =     floats_tensor((self.batch_size, self.seq_length, 1)   )
       SCREAMING_SNAKE_CASE							:   List[str]						    =     ids_tensor((self.batch_size, self.seq_length)       ,			vocab_size=1000   )
       SCREAMING_SNAKE_CASE							:   str						    =     random_attention_mask((self.batch_size, self.seq_length)   )

       SCREAMING_SNAKE_CASE							:   str						    =     self.get_config()

       return (
           config,
           states,
           actions,
           rewards,
           returns_to_go,
           timesteps,
           attention_mask,
       )


     def 					_A							(					self							:						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 					_A							(					self							:						Union[str, Any]       ,			UpperCAmelCase_							:						Any       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Union[str, Any]       ,			UpperCAmelCase_							:						str       ,			UpperCAmelCase_							:						Tuple       ,			UpperCAmelCase_							:						List[str]       ,			):
       SCREAMING_SNAKE_CASE							:   Dict						    =     DecisionTransformerModel(config=UpperCAmelCase_   )
       model.to(UpperCAmelCase_   )
       model.eval()
       SCREAMING_SNAKE_CASE							:   Any						    =     model(UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_       ,			UpperCAmelCase_   )

       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 					_A							(					self							:						Optional[Any]   ):
       SCREAMING_SNAKE_CASE							:   Any						    =     self.prepare_config_and_inputs()
       (
           (
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	(
           SCREAMING_SNAKE_CASE
       )    ,	
       )							:   Union[str, Any]						    =     config_and_inputs
       SCREAMING_SNAKE_CASE							:   Any						    =     {
           "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 			SCREAMING_SNAKE_CASE      (      lowerCAmelCase   ,		lowerCAmelCase   ,		lowerCAmelCase   ,		unittest.TestCase       ):
     '''simple docstring'''


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

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

     # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
     UpperCamelCase_				:		Tuple								=						False
     UpperCamelCase_				:		str								=						False
     UpperCamelCase_				:		Union[str, Any]								=						False
     UpperCamelCase_				:		Optional[Any]								=						False
     UpperCamelCase_				:		Tuple								=						False
     UpperCamelCase_				:		Union[str, Any]								=						False
     UpperCamelCase_				:		Optional[Any]								=						False
     UpperCamelCase_				:		Dict								=						False
     UpperCamelCase_				:		Optional[Any]								=						False


     def 					_A							(					self							:						List[Any]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     DecisionTransformerModelTester(self   )
       SCREAMING_SNAKE_CASE							:   Dict						    =     ConfigTester(self       ,			config_class=UpperCAmelCase_       ,			hidden_size=37   )


     def 					_A							(					self							:						Optional[int]   ):
       self.config_tester.run_common_tests()


     def 					_A							(					self							:						Optional[int]   ):
       SCREAMING_SNAKE_CASE							:   str						    =     self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*UpperCAmelCase_   )


     @slow
     def 					_A							(					self							:						str   ):
       for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         SCREAMING_SNAKE_CASE							:   int						    =     DecisionTransformerModel.from_pretrained(UpperCAmelCase_   )
         self.assertIsNotNone(UpperCAmelCase_   )






     def 					_A							(					self							:						str   ):
       SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE							:   Union[str, Any]						    =     [
             "states",
             "actions",
             "rewards",
             "returns_to_go",
             "timesteps",
             "attention_mask",
         ]

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





@require_torch
class 			SCREAMING_SNAKE_CASE      (      unittest.TestCase       ):
     '''simple docstring'''


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

       SCREAMING_SNAKE_CASE							:   List[str]						    =     torch.tensor(
           [[0.242_793, -0.28_693_074, 0.8_742_613], [0.67_815_274, -0.08_101_085, -0.12_952_147]]       ,			device=UpperCAmelCase_   )

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

       for step in range(UpperCAmelCase_   ):
         SCREAMING_SNAKE_CASE							:   str						    =     torch.cat([actions, torch.zeros(1       ,			1       ,			config.act_dim       ,			device=UpperCAmelCase_   )]       ,			dim=1   )
         SCREAMING_SNAKE_CASE							:   List[Any]						    =     torch.cat([rewards, torch.zeros(1       ,			1       ,			device=UpperCAmelCase_   )]       ,			dim=1   )

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

         with torch.no_grad():
           SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE    ,	SCREAMING_SNAKE_CASE							:   Tuple						    =     model(
               states=UpperCAmelCase_       ,			actions=UpperCAmelCase_       ,			rewards=UpperCAmelCase_       ,			returns_to_go=UpperCAmelCase_       ,			timesteps=UpperCAmelCase_       ,			attention_mask=UpperCAmelCase_       ,			return_dict=UpperCAmelCase_       ,			)

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

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













import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD


torch.set_grad_enabled(False)





def      _UpperCamelCase  (a__ :Tuple ,      a__ :List[str]=False							):






							"""simple docstring"""





							UpperCamelCase__					  =      []
							for i in range(config.num_hidden_layers							):
														# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
														rename_keys.append((f"""module.blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")							)
														rename_keys.append(
														    (f"""module.blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")							)
														rename_keys.append((f"""module.blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")							)
														rename_keys.append((f"""module.blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")							)

							# projection layer + position embeddings
							rename_keys.extend(
							    [
							        ("""module.cls_token""", """vit.embeddings.cls_token"""),
							        ("""module.patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight"""),
							        ("""module.patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias"""),
							        ("""module.pos_embed""", """vit.embeddings.position_embeddings"""),
							    ]							)

							if base_model:
														# layernorm + pooler
														rename_keys.extend(
														    [
														        ("""module.norm.weight""", """layernorm.weight"""),
														        ("""module.norm.bias""", """layernorm.bias"""),
														    ]							)

														# if just the base model, we should remove "vit" from all keys that start with "vit"
														UpperCamelCase__					  =      [(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""							) else pair for pair in rename_keys]
							else:
														# layernorm + classification head
														rename_keys.extend(
														    [
														        ("""norm.weight""", """vit.layernorm.weight"""),
														        ("""norm.bias""", """vit.layernorm.bias"""),
														        ("""head.weight""", """classifier.weight"""),
														        ("""head.bias""", """classifier.bias"""),
														    ]							)

							return rename_keys





def      _UpperCamelCase  (a__ :int ,      a__ :Tuple ,      a__ :Union[str, Any]=False							):






							"""simple docstring"""





							for i in range(config.num_hidden_layers							):
														if base_model:
																					UpperCamelCase__					  =      """"""
														else:
																					UpperCamelCase__					  =      """vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														UpperCamelCase__					  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.weight"""							)
														UpperCamelCase__					  =      state_dict.pop(f"""module.blocks.{i}.attn.qkv.bias"""							)
														# next, add query, keys and values (in that order) to the state dict
														UpperCamelCase__					  =      in_proj_weight[
														    : config.hidden_size, :
														]
														UpperCamelCase__					  =      in_proj_bias[: config.hidden_size]
														UpperCamelCase__					  =      in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														UpperCamelCase__					  =      in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														UpperCamelCase__					  =      in_proj_weight[
														    -config.hidden_size :, :
														]
														UpperCamelCase__					  =      in_proj_bias[-config.hidden_size :]





def      _UpperCamelCase  (a__ :int							):






							"""simple docstring"""





							UpperCamelCase__					  =      ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(a__ ,      a__							)





def      _UpperCamelCase  (a__ :Optional[int]							):






							"""simple docstring"""





							UpperCamelCase__					  =      [
							    """module.fc.fc1.weight""",
							    """module.fc.fc1.bias""",
							    """module.fc.bn1.weight""",
							    """module.fc.bn1.bias""",
							    """module.fc.bn1.running_mean""",
							    """module.fc.bn1.running_var""",
							    """module.fc.bn1.num_batches_tracked""",
							    """module.fc.fc2.weight""",
							    """module.fc.fc2.bias""",
							    """module.fc.bn2.weight""",
							    """module.fc.bn2.bias""",
							    """module.fc.bn2.running_mean""",
							    """module.fc.bn2.running_var""",
							    """module.fc.bn2.num_batches_tracked""",
							    """module.fc.fc3.weight""",
							    """module.fc.fc3.bias""",
							]
							for k in ignore_keys:
														state_dict.pop(a__ ,      a__							)





def      _UpperCamelCase  (a__ :List[str] ,      a__ :Any ,      a__ :str							):






							"""simple docstring"""





							UpperCamelCase__					  =      dct.pop(a__							)
							UpperCamelCase__					  =      val





def      _UpperCamelCase  (a__ :Dict ,      a__ :Dict							):






							"""simple docstring"""





							UpperCamelCase__					  =      ViTMSNConfig()
							UpperCamelCase__					  =      1000

							UpperCamelCase__					  =      """datasets/huggingface/label-files"""
							UpperCamelCase__					  =      """imagenet-1k-id2label.json"""
							UpperCamelCase__					  =      json.load(open(hf_hub_download(a__ ,      a__							) ,      """r"""							)							)
							UpperCamelCase__					  =      {int(a__							): v for k, v in idalabel.items()}
							UpperCamelCase__					  =      idalabel
							UpperCamelCase__					  =      {v: k for k, v in idalabel.items()}

							if "s16" in checkpoint_url:
														UpperCamelCase__					  =      384
														UpperCamelCase__					  =      1536
														UpperCamelCase__					  =      6
							elif "l16" in checkpoint_url:
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      4096
														UpperCamelCase__					  =      24
														UpperCamelCase__					  =      16
														UpperCamelCase__					  =      0.1
							elif "b4" in checkpoint_url:
														UpperCamelCase__					  =      4
							elif "l7" in checkpoint_url:
														UpperCamelCase__					  =      7
														UpperCamelCase__					  =      1024
														UpperCamelCase__					  =      4096
														UpperCamelCase__					  =      24
														UpperCamelCase__					  =      16
														UpperCamelCase__					  =      0.1

							UpperCamelCase__					  =      ViTMSNModel(a__							)

							UpperCamelCase__					  =      torch.hub.load_state_dict_from_url(a__ ,      map_location="""cpu"""							)["""target_encoder"""]

							UpperCamelCase__					  =      ViTImageProcessor(size=config.image_size							)

							remove_projection_head(a__							)
							UpperCamelCase__					  =      create_rename_keys(a__ ,      base_model=a__							)

							for src, dest in rename_keys:
														rename_key(a__ ,      a__ ,      a__							)
							read_in_q_k_v(a__ ,      a__ ,      base_model=a__							)

							model.load_state_dict(a__							)
							model.eval()

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

							UpperCamelCase__					  =      Image.open(requests.get(a__ ,      stream=a__							).raw							)
							UpperCamelCase__					  =      ViTImageProcessor(
							    size=config.image_size ,      image_mean=a__ ,      image_std=a__							)
							UpperCamelCase__					  =      image_processor(images=a__ ,      return_tensors="""pt"""							)

							# forward pass
							torch.manual_seed(2							)
							UpperCamelCase__					  =      model(**a__							)
							UpperCamelCase__					  =      outputs.last_hidden_state

							# The following Colab Notebook was used to generate these outputs:
							# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
							if "s16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[-1.0915, -1.4876, -1.1809]]							)
							elif "b16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[14.2889, -18.9045, 11.7281]]							)
							elif "l16" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[41.5028, -22.8681, 45.6475]]							)
							elif "b4" in checkpoint_url:
														UpperCamelCase__					  =      torch.tensor([[-4.3868, 5.2932, -0.4137]]							)
							else:
														UpperCamelCase__					  =      torch.tensor([[-0.1792, -0.6465, 2.4263]]							)

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

							print(f"""Saving model to {pytorch_dump_folder_path}"""							)
							model.save_pretrained(a__							)

							print(f"""Saving image processor to {pytorch_dump_folder_path}"""							)
							image_processor.save_pretrained(a__							)


if __name__ == "__main__":
					UpperCamelCase__           =			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--checkpoint_url",
					    default="https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar",
					    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."
					)

					UpperCamelCase__           =			parser.parse_args()
					convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




