






import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
A__	:    List[Any]			 = logging.get_logger(__name__)

def 			_snake_case    (					lowerCamelCase__     :   int  )    ->     Union[str, Any]:
							lowerCamelCase_	:							int									=MobileNetVaConfig(layer_norm_eps=0.001  )

							if "_quant" in model_name:
														raise ValueError("Quantized models are not supported."  )

							lowerCamelCase_	:							List[Any]									=re.match(r"^mobilenet_v1_([^_]*)_([^_]*)$"    ,						__lowerCamelCase  )
							if matches:
														lowerCamelCase_	:							List[Any]									=float(matches[1]  )
														lowerCamelCase_	:							Any									=int(matches[2]  )

							# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
							# the usual 1000. The first class (index 0) is "background".
							lowerCamelCase_	:							Dict									=1_001
							lowerCamelCase_	:							Any									="imagenet-1k-id2label.json"
							lowerCamelCase_	:							Optional[Any]									="huggingface/label-files"
							lowerCamelCase_	:							Any									=json.load(open(hf_hub_download(__lowerCamelCase    ,						__lowerCamelCase    ,						repo_type="dataset"  )    ,						"r"  )  )
							lowerCamelCase_	:							int									={int(__lowerCamelCase  ) + 1: v for k, v in idalabel.items()}
							lowerCamelCase_	:							Tuple									="background"
							lowerCamelCase_	:							Optional[int]									=idalabel
							lowerCamelCase_	:							Dict									={v: k for k, v in idalabel.items()}

							return config

def 			_snake_case    (					)    ->     List[Any]:
							lowerCamelCase_	:							Dict									="http://images.cocodataset.org/val2017/000000039769.jpg"
							lowerCamelCase_	:							Union[str, Any]									=Image.open(requests.get(__lowerCamelCase    ,						stream=__lowerCamelCase  ).raw  )
							return im

@torch.no_grad()
def 			_snake_case    (					lowerCamelCase__     :   List[Any]    ,						lowerCamelCase__     :   Any    ,						lowerCamelCase__     :   int    ,						lowerCamelCase__     :   int=False  )    ->     List[str]:
							lowerCamelCase_	:							Optional[Any]									=get_mobilenet_va_config(__lowerCamelCase  )

							# Load 🤗 model
							lowerCamelCase_	:							Optional[Any]									=MobileNetVaForImageClassification(__lowerCamelCase  ).eval()

							# Load weights from TensorFlow checkpoint
							load_tf_weights_in_mobilenet_va(__lowerCamelCase    ,						__lowerCamelCase    ,						__lowerCamelCase  )

							# Check outputs on an image, prepared by MobileNetV1ImageProcessor
							lowerCamelCase_	:							Any									=MobileNetVaImageProcessor(
							    crop_size={"width": config.image_size, "height": config.image_size}    ,						size={"shortest_edge": config.image_size + 32}    ,						)
							lowerCamelCase_	:							Dict									=image_processor(images=prepare_img()    ,						return_tensors="pt"  )
							lowerCamelCase_	:							str									=model(**__lowerCamelCase  )
							lowerCamelCase_	:							Dict									=outputs.logits

							assert logits.shape == (1, 1_001)

							if model_name == "mobilenet_v1_1.0_224":
														lowerCamelCase_	:							Union[str, Any]									=torch.tensor([-4.1739, -1.1233, 3.1205]  )
							elif model_name == "mobilenet_v1_0.75_192":
														lowerCamelCase_	:							Optional[int]									=torch.tensor([-3.9440, -2.3141, -0.3333]  )
							else:
														lowerCamelCase_	:							Optional[Any]									=None

							if expected_logits is not None:
														assert torch.allclose(logits[0, :3]    ,						__lowerCamelCase    ,						atol=1e-4  )

							Path(__lowerCamelCase  ).mkdir(exist_ok=__lowerCamelCase  )
							print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""  )
							model.save_pretrained(__lowerCamelCase  )
							print(F"""Saving image processor to {pytorch_dump_folder_path}"""  )
							image_processor.save_pretrained(__lowerCamelCase  )

							if push_to_hub:
														print("Pushing to the hub..."  )
														lowerCamelCase_	:							str									="google/" + model_name
														image_processor.push_to_hub(__lowerCamelCase  )
														model.push_to_hub(__lowerCamelCase  )


if __name__ == "__main__":
						A__	:    Union[str, Any]			 = argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='mobilenet_v1_1.0_224',
						    type=str,
						    help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
						)
						parser.add_argument(
						    '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						A__	:    Optional[int]			 = parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, 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 )



