






import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


lowercase  							= get_logger(__name__)

# Datasets
lowercase  							= "https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"
lowercase  							= "https://cdn-datasets.huggingface.co/datasets/datasets"
lowercase  							= "https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"

# Metrics
lowercase  							= "https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"
lowercase  							= "https://cdn-datasets.huggingface.co/datasets/metric"
lowercase  							= "https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"

# Hub
lowercase  							= os.environ.get("HF_ENDPOINT", "https://huggingface.co")
lowercase  							= HF_ENDPOINT + "/datasets/{repo_id}/resolve/{revision}/{path}"
lowercase  							= "main"

lowercase  							= version.parse(platform.python_version())

if PY_VERSION < version.parse("3.8"):
	import importlib_metadata
else:
	import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
lowercase  							= {"1", "ON", "YES", "TRUE"}
lowercase  							= ENV_VARS_TRUE_VALUES.union({"AUTO"})


# Imports
lowercase  							= version.parse(importlib_metadata.version("dill"))
lowercase  							= version.parse(importlib_metadata.version("pandas"))
lowercase  							= version.parse(importlib_metadata.version("pyarrow"))

lowercase  							= os.environ.get("USE_TF", "AUTO").upper()
lowercase  							= os.environ.get("USE_TORCH", "AUTO").upper()
lowercase  							= os.environ.get("USE_JAX", "AUTO").upper()

lowercase  							= "N/A"
lowercase  							= False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
	lowercase  							= importlib.util.find_spec("torch") is not None
	if TORCH_AVAILABLE:
		try:
			lowercase  							= version.parse(importlib_metadata.version("torch"))
			logger.info(f'PyTorch version {TORCH_VERSION} available.')
		except importlib_metadata.PackageNotFoundError:
			pass
else:
	logger.info("Disabling PyTorch because USE_TF is set")

lowercase  							= "N/A"
lowercase  							= False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
	lowercase  							= importlib.util.find_spec("tensorflow") is not None
	if TF_AVAILABLE:
		# For the metadata, we have to look for both tensorflow and tensorflow-cpu
		for package in [
		    "tensorflow",
		    "tensorflow-cpu",
		    "tensorflow-gpu",
		    "tf-nightly",
		    "tf-nightly-cpu",
		    "tf-nightly-gpu",
		    "intel-tensorflow",
		    "tensorflow-rocm",
		    "tensorflow-macos",
		]:
			try:
				lowercase  							= version.parse(importlib_metadata.version(package))
			except importlib_metadata.PackageNotFoundError:
				continue
			else:
				break
		else:
			lowercase  							= False
	if TF_AVAILABLE:
		if TF_VERSION.major < 2:
			logger.info(f'TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.')
			lowercase  							= False
		else:
			logger.info(f'TensorFlow version {TF_VERSION} available.')
else:
	logger.info("Disabling Tensorflow because USE_TORCH is set")


lowercase  							= "N/A"
lowercase  							= False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
	lowercase  							= importlib.util.find_spec("jax") is not None and importlib.util.find_spec("jaxlib") is not None
	if JAX_AVAILABLE:
		try:
			lowercase  							= version.parse(importlib_metadata.version("jax"))
			logger.info(f'JAX version {JAX_VERSION} available.')
		except importlib_metadata.PackageNotFoundError:
			pass
else:
	logger.info("Disabling JAX because USE_JAX is set to False")


lowercase  							= os.environ.get("USE_BEAM", "AUTO").upper()
lowercase  							= "N/A"
lowercase  							= False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
	try:
		lowercase  							= version.parse(importlib_metadata.version("apache_beam"))
		lowercase  							= True
		logger.info(f'Apache Beam version {BEAM_VERSION} available.')
	except importlib_metadata.PackageNotFoundError:
		pass
else:
	logger.info("Disabling Apache Beam because USE_BEAM is set to False")


# Optional tools for data loading
lowercase  							= importlib.util.find_spec("sqlalchemy") is not None

# Optional tools for feature decoding
lowercase  							= importlib.util.find_spec("PIL") is not None
lowercase  							= importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.0.31")
lowercase  							= importlib.util.find_spec("soundfile") is not None and version.parse(
    importlib.import_module("soundfile").__libsndfile_version__
) >= version.parse("1.1.0")

# Optional compression tools
lowercase  							= importlib.util.find_spec("rarfile") is not None
lowercase  							= importlib.util.find_spec("zstandard") is not None
lowercase  							= importlib.util.find_spec("lz4") is not None
lowercase  							= importlib.util.find_spec("py7zr") is not None


# Cache location
lowercase  							= "~/.cache"
lowercase  							= os.getenv("XDG_CACHE_HOME", DEFAULT_XDG_CACHE_HOME)
lowercase  							= os.path.join(XDG_CACHE_HOME, "huggingface")
lowercase  							= os.path.expanduser(os.getenv("HF_HOME", DEFAULT_HF_CACHE_HOME))

lowercase  							= os.path.join(HF_CACHE_HOME, "datasets")
lowercase  							= Path(os.getenv("HF_DATASETS_CACHE", DEFAULT_HF_DATASETS_CACHE))

lowercase  							= os.path.join(HF_CACHE_HOME, "metrics")
lowercase  							= Path(os.getenv("HF_METRICS_CACHE", DEFAULT_HF_METRICS_CACHE))

lowercase  							= os.path.join(HF_CACHE_HOME, "modules")
lowercase  							= Path(os.getenv("HF_MODULES_CACHE", DEFAULT_HF_MODULES_CACHE))

lowercase  							= "downloads"
lowercase  							= os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
lowercase  							= Path(os.getenv("HF_DATASETS_DOWNLOADED_DATASETS_PATH", DEFAULT_DOWNLOADED_DATASETS_PATH))

lowercase  							= "extracted"
lowercase  							= os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
lowercase  							= Path(os.getenv("HF_DATASETS_EXTRACTED_DATASETS_PATH", DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
lowercase  							= (
    os.environ.get("HF_UPDATE_DOWNLOAD_COUNTS", "AUTO").upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
lowercase  							= 1000

# Size of the preloaded record batch in `Dataset.__iter__`
lowercase  							= 10

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
lowercase  							= 4 << 30

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
lowercase  							= "500MB"

# Parquet configuration
lowercase  							= 100
lowercase  							= 100
lowercase  							= 100

# Offline mode
lowercase  							= os.environ.get("HF_DATASETS_OFFLINE", "AUTO").upper() in ENV_VARS_TRUE_VALUES

# In-memory
lowercase  							= 0  # Disabled
lowercase  							= float(os.environ.get("HF_DATASETS_IN_MEMORY_MAX_SIZE", DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
lowercase  							= "dataset.arrow"
lowercase  							= "indices.arrow"
lowercase  							= "state.json"
lowercase  							= "dataset_info.json"
lowercase  							= "dataset_infos.json"
lowercase  							= "LICENSE"
lowercase  							= "metric_info.json"
lowercase  							= "dataset_dict.json"

lowercase  							= "datasets_modules"

lowercase  							= 255

# Streaming
lowercase  							= 20
lowercase  							= 5

# Datasets without script
lowercase  							= 200
lowercase  							= 10
lowercase  							= 200

# Progress bars
lowercase  							= 0.05  # 20 progress updates per sec













import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 			UpperCamelCase_				(      utils.Parser    ):



	'''simple docstring'''




	lowerCAmelCase													=			"halfcheetah-medium-expert-v2"
	lowerCAmelCase													=			"config.offline"






def 						__UpperCAmelCase      (					a_							,					a_							,					a_							,					a_							,					a_):
							snake_case_      ,		snake_case_           =							utils.load_model(a_							,					a_							,					a_							,					epoch=a_							,					device=a_)
							snake_case_           =							TrajectoryTransformerModel(gpt.config)

							trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict())
							snake_case_           =							gpt.pos_emb
							trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict())
							trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict())
							trajectory_transformer.head.load_state_dict(gpt.head.state_dict())

							for i, block in enumerate(gpt.blocks):
														trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict())
														trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict())
														trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict())

														trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict())
														trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict())
														trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict())
														trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict())

							torch.save(trajectory_transformer.state_dict()							,					'pytorch_model.bin')


if __name__ == "__main__":
	lowercase  							= Parser().parse_args("plan")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)





