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        lowercase_   (					nn.Module		):



							def __init__(						self    ):
													super().__init__()
													_snake_case						:					int    =   nn.Linear(3				,    4    )
													_snake_case						:					int    =   nn.BatchNormad(4    )
													_snake_case						:					int    =   nn.Linear(4				,    5    )



							def 	UpperCamelCase	(						self				,    lowercase_    ):
													return self.lineara(self.batchnorm(self.lineara(lowercase_    )    )    )




class        lowercase_   (					unittest.TestCase		):



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

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   [torch.floataa, torch.floataa, torch.bfloataa]

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

																									_snake_case						:					Dict    =   load_offloaded_weight(lowercase_				,    index["weight"]    )
																									self.assertTrue(torch.equal(lowercase_				,    lowercase_    )    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Dict    =   ModelForTest()
													_snake_case						:					int    =   model.state_dict()
													_snake_case						:					int    =   {k: v for k, v in state_dict.items() if "linear2" not in k}
													_snake_case						:					Tuple    =   {k: v for k, v in state_dict.items() if "linear2" in k}

													with TemporaryDirectory() as tmp_dir:
																			offload_state_dict(lowercase_				,    lowercase_    )
																			_snake_case						:					Tuple    =   OffloadedWeightsLoader(state_dict=lowercase_				,    save_folder=lowercase_    )

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

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

													with TemporaryDirectory() as tmp_dir:
																			offload_state_dict(lowercase_				,    lowercase_    )
																			_snake_case						:					Any    =   OffloadedWeightsLoader(state_dict=lowercase_				,    save_folder=lowercase_    )

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

													with TemporaryDirectory() as tmp_dir:
																			offload_state_dict(lowercase_				,    lowercase_    )
																			# Duplicates are removed
																			_snake_case						:					int    =   OffloadedWeightsLoader(state_dict=lowercase_				,    save_folder=lowercase_    )

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



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Any    =   {"a.1": 0, "a.10": 1, "a.2": 2}
													_snake_case						:					Optional[Any]    =   extract_submodules_state_dict(lowercase_				,    ["a.1", "a.2"]    )
													self.assertDictEqual(lowercase_				,    {"a.1": 0, "a.2": 2}    )

													_snake_case						:					Tuple    =   {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
													_snake_case						:					str    =   extract_submodules_state_dict(lowercase_				,    ["a.1", "a.2"]    )
													self.assertDictEqual(lowercase_				,    {"a.1.a": 0, "a.2.a": 2}    )
from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
				import tensorflow as tf


__SCREAMING_SNAKE_CASE						:   str         =			logging.get_logger(__name__)



@dataclass
class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		[
							    'no_inference',
							    'no_cuda',
							    'no_tpu',
							    'no_speed',
							    'no_memory',
							    'no_env_print',
							    'no_multi_process',
							]



							def __init__(						self				,    **lowercase_    ):
													for deprecated_arg in self.deprecated_args:
																			if deprecated_arg in kwargs:
																									_snake_case						:					List[str]    =   deprecated_arg[3:]
																									_snake_case						:					Optional[int]    =   not kwargs.pop(lowercase_    )
																									logger.warning(
																									    f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
																									    f""" {positive_arg}={kwargs[positive_arg]}"""    )
													_snake_case						:					Tuple    =   kwargs.pop("tpu_name"				,    self.tpu_name    )
													_snake_case						:					Any    =   kwargs.pop("device_idx"				,    self.device_idx    )
													_snake_case						:					List[str]    =   kwargs.pop("eager_mode"				,    self.eager_mode    )
													_snake_case						:					List[str]    =   kwargs.pop("use_xla"				,    self.use_xla    )
													super().__init__(**lowercase_    )

							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={'help': 'Name of TPU'}						,			)
							_lowerCamelCase						=		field(
							    default=0						,			metadata={'help': 'CPU / GPU device index. Defaults to 0.'}						,			)
							_lowerCamelCase						=		field(default=__snake_case						,			metadata={'help': 'Benchmark models in eager model.'}		)
							_lowerCamelCase						=		field(
							    default=__snake_case						,			metadata={
							        'help': 'Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'
							    }						,			)



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													_snake_case						:					str    =   None
													if self.tpu:
																			try:
																									if self.tpu_name:
																															_snake_case						:					Optional[Any]    =   tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name    )
																									else:
																															_snake_case						:					List[str]    =   tf.distribute.cluster_resolver.TPUClusterResolver()
																			except ValueError:
																									_snake_case						:					Union[str, Any]    =   None
													return tpu



							@cached_property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.is_tpu:
																			tf.config.experimental_connect_to_cluster(self._setup_tpu    )
																			tf.tpu.experimental.initialize_tpu_system(self._setup_tpu    )

																			_snake_case						:					List[str]    =   tf.distribute.TPUStrategy(self._setup_tpu    )
													else:
																			# currently no multi gpu is allowed
																			if self.is_gpu:
																									# TODO: Currently only single GPU is supported
																									tf.config.set_visible_devices(self.gpu_list[self.device_idx]				,    "GPU"    )
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""    )
																			else:
																									tf.config.set_visible_devices([]				,    "GPU"    )  # disable GPU
																									_snake_case						:					Any    =   tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""    )

													return strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_tpu is not None



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return self._setup_strategy



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													return tf.config.list_physical_devices("GPU"    )



							@property
							def 	UpperCamelCase	(						self    ):
													requires_backends(self				,    ["tf"]    )
													if self.cuda:
																			return len(self.gpu_list    )
													return 0



							@property
							def 	UpperCamelCase	(						self    ):
													return self.n_gpu > 0