



import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases

def      A_ (		__a       :			Optional[int]	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				assert isinstance(__a	,						__a						)
				assert dataset.num_rows == 4
				assert dataset.num_columns == 3
				assert dataset.column_names == ["col_1", "col_2", "col_3"]
				for feature, expected_dtype in expected_features.items():
								assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""	,						[False, True]						)
def      A_ (		__a       :			Optional[int]	,						__a       :			Tuple	,						__a       :			int						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				a__			 =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								a__			 =						CsvDatasetReader(__a	,						cache_dir=__a	,						keep_in_memory=__a						).read()
				_check_csv_dataset(__a	,						__a						)

@pytest.mark.parametrize(
    """features"""	,						[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,						)
def      A_ (		__a       :			str	,						__a       :			Tuple	,						__a       :			List[Any]						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				a__			 =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				a__			 =						features.copy() if features else default_expected_features
				a__			 =						(
				    Features({feature: Value(__a						) for feature, dtype in features.items()}						) if features is not None else None
				)
				a__			 =						CsvDatasetReader(__a	,						features=__a	,						cache_dir=__a						).read()
				_check_csv_dataset(__a	,						__a						)

@pytest.mark.parametrize("""split"""	,						[None, NamedSplit("""train"""						), """train""", """test"""]						)
def      A_ (		__a       :			List[str]	,						__a       :			Optional[int]	,						__a       :			List[str]						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				a__			 =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				a__			 =						CsvDatasetReader(__a	,						cache_dir=__a	,						split=__a						).read()
				_check_csv_dataset(__a	,						__a						)
				assert dataset.split == split if split else "train"

@pytest.mark.parametrize("""path_type"""	,						[str, list]						)
def      A_ (		__a       :			Any	,						__a       :			str	,						__a       :			Dict						):

				"""simple docstring"""
				if issubclass(__a	,						__a						):
								a__			 =						csv_path
				elif issubclass(__a	,						__a						):
								a__			 =						[csv_path]
				a__			 =						tmp_path / """cache"""
				a__			 =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				a__			 =						CsvDatasetReader(__a	,						cache_dir=__a						).read()
				_check_csv_dataset(__a	,						__a						)

def      A_ (		__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			str=("train",)						):

				"""simple docstring"""
				assert isinstance(__a	,						__a						)
				for split in splits:
								a__			 =						dataset_dict[split]
								assert dataset.num_rows == 4
								assert dataset.num_columns == 3
								assert dataset.column_names == ["col_1", "col_2", "col_3"]
								for feature, expected_dtype in expected_features.items():
												assert dataset.features[feature].dtype == expected_dtype

@pytest.mark.parametrize("""keep_in_memory"""	,						[False, True]						)
def      A_ (		__a       :			Optional[int]	,						__a       :			List[str]	,						__a       :			Dict						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				a__			 =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
								a__			 =						CsvDatasetReader({"""train""": csv_path}	,						cache_dir=__a	,						keep_in_memory=__a						).read()
				_check_csv_datasetdict(__a	,						__a						)

@pytest.mark.parametrize(
    """features"""	,						[
        None,
        {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
        {"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
        {"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
        {"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
    ]	,						)
def      A_ (		__a       :			str	,						__a       :			Dict	,						__a       :			Tuple						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
				a__			 =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				a__			 =						features.copy() if features else default_expected_features
				a__			 =						(
				    Features({feature: Value(__a						) for feature, dtype in features.items()}						) if features is not None else None
				)
				a__			 =						CsvDatasetReader({"""train""": csv_path}	,						features=__a	,						cache_dir=__a						).read()
				_check_csv_datasetdict(__a	,						__a						)

@pytest.mark.parametrize("""split"""	,						[None, NamedSplit("""train"""						), """train""", """test"""]						)
def      A_ (		__a       :			List[Any]	,						__a       :			int	,						__a       :			Tuple						):

				"""simple docstring"""
				if split:
								a__			 =						{split: csv_path}
				else:
								a__			 =						{"""train""": csv_path, """test""": csv_path}
				a__			 =						tmp_path / """cache"""
				a__			 =						{"""col_1""": """int64""", """col_2""": """int64""", """col_3""": """float64"""}
				a__			 =						CsvDatasetReader(__a	,						cache_dir=__a						).read()
				_check_csv_datasetdict(__a	,						__a	,						splits=list(path.keys()						)						)
				assert all(dataset[split].split == split for split in path.keys()						)

def      A_ (		__a       :			Union[str, Any]						):

				"""simple docstring"""
				with open(__a	,						encoding="""utf-8"""						) as csvfile:
								yield from csv.reader(__a						)

def      A_ (		__a       :			Tuple	,						__a       :			int						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				a__			 =						os.path.join(__a	,						"""tmp.csv"""						)
				a__			 =						CsvDatasetReader({"""train""": csv_path}	,						cache_dir=__a						).read()
				CsvDatasetWriter(dataset["""train"""]	,						__a	,						num_proc=1						).write()

				a__			 =						iter_csv_file(__a						)
				a__			 =						iter_csv_file(__a						)

				for rowa, rowa in zip(__a	,						__a						):
								assert rowa == rowa

def      A_ (		__a       :			str	,						__a       :			Any						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				a__			 =						os.path.join(__a	,						"""tmp.csv"""						)
				a__			 =						CsvDatasetReader({"""train""": csv_path}	,						cache_dir=__a						).read()
				CsvDatasetWriter(dataset["""train"""]	,						__a	,						num_proc=2						).write()

				a__			 =						iter_csv_file(__a						)
				a__			 =						iter_csv_file(__a						)

				for rowa, rowa in zip(__a	,						__a						):
								assert rowa == rowa




def      A_ (		__a       :			Optional[int]	,						__a       :			Dict						):

				"""simple docstring"""
				a__			 =						tmp_path / """cache"""
				a__			 =						os.path.join(__a	,						"""tmp.csv"""						)
				a__			 =						CsvDatasetReader({"""train""": csv_path}	,						cache_dir=__a						).read()
				with pytest.raises(__a						):
								CsvDatasetWriter(dataset["""train"""]	,						__a	,						num_proc=0						)






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel

class 	__snake_case (				utils.Parser):







							'''simple docstring'''






							UpperCamelCase__			: str             =   "halfcheetah-medium-expert-v2"
							UpperCamelCase__			: str             =   "config.offline"




def      A_ (		__a       :			List[Any]	,						__a       :			Union[str, Any]	,						__a       :			Optional[Any]	,						__a       :			Dict	,						__a       :			Union[str, Any]						):

				"""simple docstring"""
				a__ ,     a__			 =						utils.load_model(__a	,						__a	,						__a	,						epoch=__a	,						device=__a						)
				a__			 =						TrajectoryTransformerModel(gpt.config						)

				trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()						)
				a__			 =						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__":
	UpperCAmelCase								=   Parser().parse_args("""plan""")
	convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
	    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
	)

