

import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)

class __UpperCamelCase     (				nn.Module		):






					'''simple docstring'''



					def __init__(				self						):
						super().__init__()
						UpperCAmelCase__:	Union[str, Any]									= nn.Linear(3				,      4						)
						UpperCAmelCase__:	Dict									= nn.BatchNormad(4						)
						UpperCAmelCase__:	Dict									= nn.Linear(4				,      5						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__						)						)						)

class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	List[str]									= ModelForTest()
						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(lowerCamelCase__				,      model.state_dict()						)
							UpperCAmelCase__:	Any									= os.path.join(lowerCamelCase__				,      "index.json"						)
							self.assertTrue(os.path.isfile(lowerCamelCase__						)						)
							# TODO: add tests on what is inside the index

							for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
								UpperCAmelCase__:	List[Any]									= os.path.join(lowerCamelCase__				,      F"{key}.dat"						)
								self.assertTrue(os.path.isfile(lowerCamelCase__						)						)
								# TODO: add tests on the fact weights are properly loaded





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= [torch.floataa, torch.floataa, torch.bfloataa]

						for dtype in dtypes:
							UpperCAmelCase__:	str									= torch.randn(2				,      3				,      dtype=lowerCamelCase__						)
							with TemporaryDirectory() as tmp_dir:
								UpperCAmelCase__:	Dict									= offload_weight(lowerCamelCase__				,      "weight"				,      lowerCamelCase__				,      {}						)
								UpperCAmelCase__:	Tuple									= os.path.join(lowerCamelCase__				,      "weight.dat"						)
								self.assertTrue(os.path.isfile(lowerCamelCase__						)						)
								self.assertDictEqual(lowerCamelCase__				,      {"weight": {"shape": [2, 3], "dtype": str(lowerCamelCase__						).split("."						)[1]}}						)

								UpperCAmelCase__:	List[str]									= load_offloaded_weight(lowerCamelCase__				,      index["weight"]						)
								self.assertTrue(torch.equal(lowerCamelCase__				,      lowerCamelCase__						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Optional[Any]									= ModelForTest()
						UpperCAmelCase__:	int									= model.state_dict()
						UpperCAmelCase__:	List[Any]									= {k: v for k, v in state_dict.items() if "linear2" not in k}
						UpperCAmelCase__:	List[Any]									= {k: v for k, v in state_dict.items() if "linear2" in k}

						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(lowerCamelCase__				,      lowerCamelCase__						)
							UpperCAmelCase__:	Any									= OffloadedWeightsLoader(state_dict=lowerCamelCase__				,      save_folder=lowerCamelCase__						)

							# Every key is there with the right value
							self.assertEqual(sorted(lowerCamelCase__						)				,      sorted(state_dict.keys()						)						)
							for key, param in state_dict.items():
								self.assertTrue(torch.allclose(lowerCamelCase__				,      weight_map[key]						)						)

						UpperCAmelCase__:	Optional[Any]									= {k: v for k, v in state_dict.items() if "weight" in k}
						UpperCAmelCase__:	Optional[Any]									= {k: v for k, v in state_dict.items() if "weight" not in k}

						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(lowerCamelCase__				,      lowerCamelCase__						)
							UpperCAmelCase__:	Dict									= OffloadedWeightsLoader(state_dict=lowerCamelCase__				,      save_folder=lowerCamelCase__						)

							# Every key is there with the right value
							self.assertEqual(sorted(lowerCamelCase__						)				,      sorted(state_dict.keys()						)						)
							for key, param in state_dict.items():
								self.assertTrue(torch.allclose(lowerCamelCase__				,      weight_map[key]						)						)

						with TemporaryDirectory() as tmp_dir:
							offload_state_dict(lowerCamelCase__				,      lowerCamelCase__						)
							# Duplicates are removed
							UpperCAmelCase__:	Optional[Any]									= OffloadedWeightsLoader(state_dict=lowerCamelCase__				,      save_folder=lowerCamelCase__						)

							# Every key is there with the right value
							self.assertEqual(sorted(lowerCamelCase__						)				,      sorted(state_dict.keys()						)						)
							for key, param in state_dict.items():
								self.assertTrue(torch.allclose(lowerCamelCase__				,      weight_map[key]						)						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	int									= {"a.1": 0, "a.10": 1, "a.2": 2}
						UpperCAmelCase__:	List[str]									= extract_submodules_state_dict(lowerCamelCase__				,      ["a.1", "a.2"]						)
						self.assertDictEqual(lowerCamelCase__				,      {"a.1": 0, "a.2": 2}						)

						UpperCAmelCase__:	Optional[Any]									= {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
						UpperCAmelCase__:	Optional[int]									= extract_submodules_state_dict(lowerCamelCase__				,      ["a.1", "a.2"]						)
						self.assertDictEqual(lowerCamelCase__				,      {"a.1.a": 0, "a.2.a": 2}						)


import fire

from utils import calculate_rouge, save_json



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=None	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()]
	UpperCAmelCase__:	Dict									= [x.strip() for x in open(SCREAMING_SNAKE_CASE      ).readlines()][: len(SCREAMING_SNAKE_CASE      )]
	UpperCAmelCase__:	Any									= calculate_rouge(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      )
	if save_path is not None:
		save_json(SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,indent=SCREAMING_SNAKE_CASE      )
	return metrics  # these print nicely


if __name__ == "__main__":
					fire.Fire(calculate_rouge_path)