






"""simple docstring"""



import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD

def 			_snake_case    (					)    ->     str:
							lowerCamelCase_	:							Optional[int]									="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"
							lowerCamelCase_	:							str									=Image.open(requests.get(lowerCamelCase__    ,						stream=lowerCamelCase__  ).raw  ).convert("RGB"  )

							return image

def 			_snake_case    (					lowerCamelCase__     :   Optional[int]  )    ->     Any:
							lowerCamelCase_	:							str									=[]
							# fmt: off

							# vision encoder
							rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding")  )
							rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding")  )
							rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight")  )
							rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias")  )
							rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight")  )
							rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias")  )

							for i in range(config.vision_config.num_hidden_layers  ):
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.weight""", F"""vision_model.encoder.layers.{i}.layer_norm1.weight""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm1.bias""", F"""vision_model.encoder.layers.{i}.layer_norm1.bias""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.weight""", F"""vision_model.encoder.layers.{i}.layer_norm2.weight""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.norm2.bias""", F"""vision_model.encoder.layers.{i}.layer_norm2.bias""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.attn.qkv.weight""", F"""vision_model.encoder.layers.{i}.self_attn.qkv.weight""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.weight""", F"""vision_model.encoder.layers.{i}.self_attn.projection.weight""",)  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.attn.proj.bias""", F"""vision_model.encoder.layers.{i}.self_attn.projection.bias""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc1.weight""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc1.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc1.bias""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.weight""", F"""vision_model.encoder.layers.{i}.mlp.fc2.weight""")  )
														rename_keys.append((F"""visual_encoder.blocks.{i}.mlp.fc2.bias""", F"""vision_model.encoder.layers.{i}.mlp.fc2.bias""")  )

							# QFormer
							rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.layernorm.weight")  )
							rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.layernorm.bias")  )

							# fmt: on
							return rename_keys

def 			_snake_case    (					lowerCamelCase__     :   Dict    ,						lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   Tuple  )    ->     Optional[Any]:
							lowerCamelCase_	:							str									=dct.pop(lowerCamelCase__  )
							lowerCamelCase_	:							Optional[Any]									=val

def 			_snake_case    (					lowerCamelCase__     :   str    ,						lowerCamelCase__     :   Optional[Any]  )    ->     str:
							for i in range(config.vision_config.num_hidden_layers  ):
														# read in original q and v biases
														lowerCamelCase_	:							Optional[Any]									=state_dict.pop(F"""visual_encoder.blocks.{i}.attn.q_bias"""  )
														lowerCamelCase_	:							int									=state_dict.pop(F"""visual_encoder.blocks.{i}.attn.v_bias"""  )

														# next, set bias in the state dict
														lowerCamelCase_	:							Optional[int]									=torch.cat((q_bias, torch.zeros_like(lowerCamelCase__    ,						requires_grad=lowerCamelCase__  ), v_bias)  )
														lowerCamelCase_	:							Any									=qkv_bias

def 			_snake_case    (					lowerCamelCase__     :   Optional[Any]    ,						lowerCamelCase__     :   List[str]  )    ->     Any:
							lowerCamelCase_	:							List[Any]									=364 if "coco" in model_name else 224
							lowerCamelCase_	:							Optional[Any]									=BlipaVisionConfig(image_size=lowerCamelCase__  ).to_dict()

							# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
							# seems like flan-T5 models don't have bos_token_id properly set?
							if "opt-2.7b" in model_name:
														lowerCamelCase_	:							Tuple									=OPTConfig.from_pretrained("facebook/opt-2.7b"    ,						eos_token_id=lowerCamelCase__  ).to_dict()
							elif "opt-6.7b" in model_name:
														lowerCamelCase_	:							Any									=OPTConfig.from_pretrained("facebook/opt-6.7b"    ,						eos_token_id=lowerCamelCase__  ).to_dict()
							elif "t5-xl" in model_name:
														lowerCamelCase_	:							Union[str, Any]									=TaConfig.from_pretrained("google/flan-t5-xl"    ,						dense_act_fn="gelu"    ,						bos_token_id=1  ).to_dict()
							elif "t5-xxl" in model_name:
														lowerCamelCase_	:							Any									=TaConfig.from_pretrained("google/flan-t5-xxl"    ,						dense_act_fn="gelu"    ,						bos_token_id=1  ).to_dict()

							lowerCamelCase_	:							Optional[int]									=BlipaConfig(vision_config=lowerCamelCase__    ,						text_config=lowerCamelCase__  )

							return config, image_size

@torch.no_grad()
def 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   Any=None    ,						lowerCamelCase__     :   Optional[int]=False  )    ->     int:
							lowerCamelCase_	:							int									=(
							    AutoTokenizer.from_pretrained("facebook/opt-2.7b"  )
							    if "opt" in model_name
							    else AutoTokenizer.from_pretrained("google/flan-t5-xl"  )
							)
							lowerCamelCase_	:							Dict									=tokenizer("\n"    ,						add_special_tokens=lowerCamelCase__  ).input_ids[0]
							lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=get_blipa_config(lowerCamelCase__    ,						eos_token_id=lowerCamelCase__  )

							lowerCamelCase_	:							Union[str, Any]									=BlipaForConditionalGeneration(lowerCamelCase__  ).eval()

							lowerCamelCase_	:							Union[str, Any]									={
							    "blip2-opt-2.7b": ("blip2_opt", "pretrain_opt2.7b"),
							    "blip2-opt-6.7b": ("blip2_opt", "pretrain_opt6.7b"),
							    "blip2-opt-2.7b-coco": ("blip2_opt", "caption_coco_opt2.7b"),
							    "blip2-opt-6.7b-coco": ("blip2_opt", "caption_coco_opt6.7b"),
							    "blip2-flan-t5-xl": ("blip2_t5", "pretrain_flant5xl"),
							    "blip2-flan-t5-xl-coco": ("blip2_t5", "caption_coco_flant5xl"),
							    "blip2-flan-t5-xxl": ("blip2_t5", "pretrain_flant5xxl"),
							}

							lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=model_name_to_original[model_name]

							# load original model
							print("Loading original model..."  )
							lowerCamelCase_	:							int									="cuda" if torch.cuda.is_available() else "cpu"
							lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Optional[int]									=load_model_and_preprocess(
							    name=lowerCamelCase__    ,						model_type=lowerCamelCase__    ,						is_eval=lowerCamelCase__    ,						device=lowerCamelCase__  )
							original_model.eval()
							print("Done!"  )

							# update state dict keys
							lowerCamelCase_	:							int									=original_model.state_dict()
							lowerCamelCase_	:							Optional[int]									=create_rename_keys(lowerCamelCase__  )
							for src, dest in rename_keys:
														rename_key(lowerCamelCase__    ,						lowerCamelCase__    ,						lowerCamelCase__  )

							# some keys can be renamed efficiently
							for key, val in state_dict.copy().items():
														lowerCamelCase_	:							Optional[Any]									=state_dict.pop(lowerCamelCase__  )
														if key.startswith("Qformer.bert"  ):
																					lowerCamelCase_	:							List[Any]									=key.replace("Qformer.bert"    ,						"qformer"  )
														if "attention.self" in key:
																					lowerCamelCase_	:							Dict									=key.replace("self"    ,						"attention"  )
														if "opt_proj" in key:
																					lowerCamelCase_	:							Tuple									=key.replace("opt_proj"    ,						"language_projection"  )
														if "t5_proj" in key:
																					lowerCamelCase_	:							Any									=key.replace("t5_proj"    ,						"language_projection"  )
														if key.startswith("opt"  ):
																					lowerCamelCase_	:							List[str]									=key.replace("opt"    ,						"language"  )
														if key.startswith("t5"  ):
																					lowerCamelCase_	:							str									=key.replace("t5"    ,						"language"  )
														lowerCamelCase_	:							str									=val

							# read in qv biases
							read_in_q_v_bias(lowerCamelCase__    ,						lowerCamelCase__  )

							lowerCamelCase_							,						lowerCamelCase_	:							List[str]									=hf_model.load_state_dict(lowerCamelCase__    ,						strict=lowerCamelCase__  )
							assert len(lowerCamelCase__  ) == 0
							assert unexpected_keys == ["qformer.embeddings.position_ids"]

							lowerCamelCase_	:							List[str]									=load_demo_image()
							lowerCamelCase_	:							Union[str, Any]									=vis_processors["eval"](lowerCamelCase__  ).unsqueeze(0  ).to(lowerCamelCase__  )
							lowerCamelCase_	:							List[str]									=tokenizer(["\n"]    ,						return_tensors="pt"  ).input_ids.to(lowerCamelCase__  )

							# create processor
							lowerCamelCase_	:							int									=BlipImageProcessor(
							    size={"height": image_size, "width": image_size}    ,						image_mean=lowerCamelCase__    ,						image_std=lowerCamelCase__  )
							lowerCamelCase_	:							List[str]									=BlipaProcessor(image_processor=lowerCamelCase__    ,						tokenizer=lowerCamelCase__  )
							lowerCamelCase_	:							Any									=processor(images=lowerCamelCase__    ,						return_tensors="pt"  ).pixel_values.to(lowerCamelCase__  )

							# make sure processor creates exact same pixel values
							assert torch.allclose(lowerCamelCase__    ,						lowerCamelCase__  )

							original_model.to(lowerCamelCase__  )
							hf_model.to(lowerCamelCase__  )
							with torch.no_grad():
														if "opt" in model_name:
																					lowerCamelCase_	:							List[Any]									=original_model({"image": original_pixel_values, "text_input": [""]}  ).logits
																					lowerCamelCase_	:							Optional[int]									=hf_model(lowerCamelCase__    ,						lowerCamelCase__  ).logits
														else:
																					lowerCamelCase_	:							str									=original_model(
																					    {"image": original_pixel_values, "text_input": ["\n"], "text_output": ["\n"]}  ).logits
																					lowerCamelCase_	:							List[Any]									=input_ids.masked_fill(input_ids == tokenizer.pad_token_id    ,						-100  )
																					lowerCamelCase_	:							List[str]									=hf_model(lowerCamelCase__    ,						lowerCamelCase__    ,						labels=lowerCamelCase__  ).logits

							assert original_logits.shape == logits.shape
							print("First values of original logits:"    ,						original_logits[0, :3, :3]  )
							print("First values of HF logits:"    ,						logits[0, :3, :3]  )

							# assert values
							if model_name == "blip2-flan-t5-xl":
														lowerCamelCase_	:							Tuple									=torch.tensor(
														    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]    ,						device=lowerCamelCase__  )
														assert torch.allclose(logits[0, :3, :3]    ,						lowerCamelCase__    ,						atol=1e-4  )
							elif model_name == "blip2-flan-t5-xl-coco":
														lowerCamelCase_	:							List[str]									=torch.tensor(
														    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]    ,						device=lowerCamelCase__  )
							else:
														# cast to same type
														lowerCamelCase_	:							List[Any]									=logits.dtype
														assert torch.allclose(original_logits.to(lowerCamelCase__  )    ,						lowerCamelCase__    ,						atol=1e-2  )
							print("Looks ok!"  )

							print("Generating a caption..."  )
							lowerCamelCase_	:							Optional[int]									=""
							lowerCamelCase_	:							List[str]									=tokenizer(lowerCamelCase__    ,						return_tensors="pt"  ).input_ids.to(lowerCamelCase__  )

							lowerCamelCase_	:							List[Any]									=original_model.generate({"image": original_pixel_values}  )
							lowerCamelCase_	:							List[Any]									=hf_model.generate(
							    lowerCamelCase__    ,						lowerCamelCase__    ,						do_sample=lowerCamelCase__    ,						num_beams=5    ,						max_length=30    ,						min_length=1    ,						top_p=0.9    ,						repetition_penalty=1.0    ,						length_penalty=1.0    ,						temperature=1    ,						)
							print("Original generation:"    ,						lowerCamelCase__  )
							lowerCamelCase_	:							Tuple									=input_ids.shape[1]
							lowerCamelCase_	:							int									=processor.batch_decode(outputs[:, prompt_length:]    ,						skip_special_tokens=lowerCamelCase__  )
							lowerCamelCase_	:							Optional[Any]									=[text.strip() for text in output_text]
							print("HF generation:"    ,						lowerCamelCase__  )

							if pytorch_dump_folder_path is not None:
														processor.save_pretrained(lowerCamelCase__  )
														hf_model.save_pretrained(lowerCamelCase__  )

							if push_to_hub:
														processor.push_to_hub(F"""nielsr/{model_name}"""  )
														hf_model.push_to_hub(F"""nielsr/{model_name}"""  )


if __name__ == "__main__":
						A__	:    Optional[int]			 = argparse.ArgumentParser()
						A__	:    Tuple			 = [
						    'blip2-opt-2.7b',
						    'blip2-opt-6.7b',
						    'blip2-opt-2.7b-coco',
						    'blip2-opt-6.7b-coco',
						    'blip2-flan-t5-xl',
						    'blip2-flan-t5-xl-coco',
						    'blip2-flan-t5-xxl',
						]
						parser.add_argument(
						    '--model_name',
						    default='blip2-opt-2.7b',
						    choices=choices,
						    type=str,
						    help='Path to hf config.json of model to convert',
						)
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model and processor to the hub after converting',
						)

						A__	:    Union[str, Any]			 = parser.parse_args()

						convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, 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        lowercase__      :


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


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
										lowerCamelCase_	:							List[Any]									=floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase_	:							Optional[Any]									=floats_tensor((self.batch_size, self.seq_length, 1) )
										lowerCamelCase_	:							List[Any]									=ids_tensor((self.batch_size, self.seq_length)       ,	vocab_size=1000 )
										lowerCamelCase_	:							Optional[int]									=random_attention_mask((self.batch_size, self.seq_length) )

										lowerCamelCase_	:							List[str]									=self.get_config()

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


			def      UpperCAmelCase__						(				self					:  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      UpperCAmelCase__						(				self					:  Optional[Any]       ,	snake_case__					:  Tuple       ,	snake_case__					:  Dict       ,	snake_case__					:  int       ,	snake_case__					:  Dict       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  List[str]       ,	snake_case__					:  List[str]       ,	):
										lowerCamelCase_	:							Tuple									=DecisionTransformerModel(config=snake_case__ )
										model.to(snake_case__ )
										model.eval()
										lowerCamelCase_	:							str									=model(snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	snake_case__       ,	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      UpperCAmelCase__						(				self					:  List[str] ):
										lowerCamelCase_	:							List[str]									=self.prepare_config_and_inputs()
										(
										    (
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						(
										    lowerCamelCase_
										)							,						
										)	:							Optional[int]									=config_and_inputs
										lowerCamelCase_	:							Optional[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        lowercase__      (							snake_case__,       snake_case__,       snake_case__,       unittest.TestCase       ):
			_UpperCAmelCase		:Optional[Any]             =			(DecisionTransformerModel,) if is_torch_available() else ()
			_UpperCAmelCase		:int             =			()
			_UpperCAmelCase		: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		:Union[str, Any]             =			False

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


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Dict									=DecisionTransformerModelTester(self )
										lowerCamelCase_	:							str									=ConfigTester(self       ,	config_class=snake_case__       ,	hidden_size=37 )


			def      UpperCAmelCase__						(				self					:  Union[str, Any] ):
										self.config_tester.run_common_tests()


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Tuple									=self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*snake_case__ )


			@slow
			def      UpperCAmelCase__						(				self					:  List[str] ):
										for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCamelCase_	:							str									=DecisionTransformerModel.from_pretrained(snake_case__ )
																	self.assertIsNotNone(snake_case__ )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_							,						lowerCamelCase_	:							Dict									=self.model_tester.prepare_config_and_inputs_for_common()

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

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

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







@require_torch
class        lowercase__      (							unittest.TestCase       ):


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

										lowerCamelCase_	:							Optional[Any]									=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=snake_case__ )

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

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

																	lowerCamelCase_	:							Optional[int]									=torch.ones(1       ,	states.shape[1] ).to(dtype=torch.long       ,	device=states.device )

																	with torch.no_grad():
																								lowerCamelCase_							,						lowerCamelCase_							,						lowerCamelCase_	:							Dict									=model(
																								    states=snake_case__       ,	actions=snake_case__       ,	rewards=snake_case__       ,	returns_to_go=snake_case__       ,	timesteps=snake_case__       ,	attention_mask=snake_case__       ,	return_dict=snake_case__       ,	)

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

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



