
import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment


def       A       (					)   ->  Optional[int]:
		'''simple docstring'''

		UpperCamelCase		=     torch.nn.Linear(2    ,       4 )
		UpperCamelCase		=     torch.optim.AdamW(model.parameters()    ,       lr=1.0 )
		UpperCamelCase		=     torch.optim.lr_scheduler.OneCycleLR(lowercase    ,       max_lr=0.0_1    ,       steps_per_epoch=2    ,       epochs=1 )
		UpperCamelCase		=     DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
		UpperCamelCase		=     DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )

		return model, optimizer, scheduler, train_dl, valid_dl


def       A       (					lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

		return (model.weight.abs().sum() + model.bias.abs().sum()).item()


def       A       (					lowercase )   ->  Union[str, Any]:
		'''simple docstring'''

		UpperCamelCase		=     torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
		model.load_state_dict(lowercase )


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):



		@require_cuda
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				assert PartialState._shared_state["_cpu"] is False
				assert PartialState._shared_state["device"].type == "cuda"
				with self.assertRaises(A_       ):
						UpperCamelCase		=     Accelerator(cpu=A_       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				UpperCamelCase		=     GradientState()
				assert state.num_steps == 1
				UpperCamelCase		=     4
				assert state.num_steps == 4

				assert state.sync_gradients is True
				UpperCamelCase		=     False
				assert state.sync_gradients is False
				GradientState._reset_state()



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     create_components()

				(
				    (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  (
				    UpperCamelCase
				)	,  
				)		=     accelerator.prepare(A_				,      A_				,      A_				,      A_				,      A_       )

				self.assertTrue(prepared_model in accelerator._models       )
				self.assertTrue(prepared_optimizer in accelerator._optimizers       )
				self.assertTrue(prepared_scheduler in accelerator._schedulers       )
				self.assertTrue(prepared_train_dl in accelerator._dataloaders       )
				self.assertTrue(prepared_valid_dl in accelerator._dataloaders       )



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     create_components()
				accelerator.prepare(A_				,      A_				,      A_				,      A_				,      A_       )
				accelerator.free_memory()

				self.assertTrue(len(accelerator._models       ) == 0       )
				self.assertTrue(len(accelerator._optimizers       ) == 0       )
				self.assertTrue(len(accelerator._schedulers       ) == 0       )
				self.assertTrue(len(accelerator._dataloaders       ) == 0       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				PartialState._reset_state()

				# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
				def noop(*A_				,      **A_       ):
						pass

				with patch('torch.cuda.set_device'				,      A_       ), patch_environment(ACCELERATE_TORCH_DEVICE='cuda:64'       ):
						UpperCamelCase		=     Accelerator()
						self.assertEqual(str(accelerator.state.device       )				,      'cuda:64'       )



		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     create_components()
				accelerator.prepare(A_				,      A_				,      A_				,      A_				,      A_       )

				UpperCamelCase		=     get_signature(A_       )

				with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(A_       )

						# make sure random weights don't match
						load_random_weights(A_       )
						self.assertTrue(abs(model_signature - get_signature(A_       )       ) > 1e-3       )

						# make sure loaded weights match
						accelerator.load_state(A_       )
						self.assertTrue(abs(model_signature - get_signature(A_       )       ) < 1e-3       )



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     create_components()
				accelerator.prepare(A_				,      A_				,      A_				,      A_				,      A_       )

				UpperCamelCase		=     get_signature(A_       )

				# saving hook
				def save_config(A_				,      A_				,      A_       ):
						UpperCamelCase		=     {'class_name': models[0].__class__.__name__}

						with open(os.path.join(A_				,      'data.json'       )				,      'w'       ) as f:
								json.dump(A_				,      A_       )

        # loading hook
				def load_config(A_				,      A_       ):
						with open(os.path.join(A_				,      'data.json'       )				,      'r'       ) as f:
								UpperCamelCase		=     json.load(A_       )

						UpperCamelCase		=     config['class_name']

				UpperCamelCase		=     accelerator.register_save_state_pre_hook(A_       )
				UpperCamelCase		=     accelerator.register_load_state_pre_hook(A_       )

				with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(A_       )

						# make sure random weights don't match with hooks
						load_random_weights(A_       )
						self.assertTrue(abs(model_signature - get_signature(A_       )       ) > 1e-3       )

						# random class name to verify correct one is loaded
						UpperCamelCase		=     'random'

						# make sure loaded weights match with hooks
						accelerator.load_state(A_       )
						self.assertTrue(abs(model_signature - get_signature(A_       )       ) < 1e-3       )

						# mode.class_name is loaded from config
						self.assertTrue(model.class_name == model.__class__.__name__       )

				# remove hooks
				save_hook.remove()
				load_hook.remove()

				with tempfile.TemporaryDirectory() as tmpdirname:
						accelerator.save_state(A_       )

						# make sure random weights don't match with hooks removed
						load_random_weights(A_       )
						self.assertTrue(abs(model_signature - get_signature(A_       )       ) > 1e-3       )

						# random class name to verify correct one is loaded
						UpperCamelCase		=     'random'

						# make sure loaded weights match with hooks removed
						accelerator.load_state(A_       )
						self.assertTrue(abs(model_signature - get_signature(A_       )       ) < 1e-3       )

						# mode.class_name is NOT loaded from config
						self.assertTrue(model.class_name != model.__class__.__name__       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     create_components()
				UpperCamelCase		=     None

				# This should work
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     accelerator.prepare(
				    A_				,      A_				,      A_				,      A_				,      A_				,      A_       )
				self.assertTrue(dummy_obj is None       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     Accelerator()
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     create_components()
				UpperCamelCase		=     [1, 2, 3]

				# This should work
				UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase	,  UpperCamelCase		=     accelerator.prepare(
				    A_				,      A_				,      A_				,      A_				,      A_				,      A_       )
				self.assertEqual(
				    getattr(A_				,      '_is_accelerate_prepared'				,      A_       )				,      A_				,      'Dummy object should have `_is_accelerate_prepared` set to `True`'				,      )
				self.assertEqual(
				    getattr(A_				,      '_is_accelerate_prepared'				,      A_       )				,      A_				,      'Model is missing `_is_accelerator_prepared` or is set to `False`'				,      )
				self.assertEqual(
				    getattr(A_				,      '_is_accelerate_prepared'				,      A_       )				,      A_				,      'Optimizer is missing `_is_accelerator_prepared` or is set to `False`'				,      )
				self.assertEqual(
				    getattr(A_				,      '_is_accelerate_prepared'				,      A_       )				,      A_				,      'Scheduler is missing `_is_accelerator_prepared` or is set to `False`'				,      )
				self.assertEqual(
				    getattr(A_				,      '_is_accelerate_prepared'				,      A_       )				,      A_				,      'Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'				,      )
				self.assertEqual(
				    getattr(A_				,      '_is_accelerate_prepared'				,      A_       )				,      A_				,      'Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'				,      )



		@slow
		@require_bnb
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				from transformers import AutoModelForCausalLM

				UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(
				    'EleutherAI/gpt-neo-125m'				,      load_in_abit=A_				,      device_map={'': 0}				,      )
				UpperCamelCase		=     Accelerator()

				# This should work
				UpperCamelCase		=     accelerator.prepare(A_       )



		@slow
		@require_bnb
		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				from transformers import AutoModelForCausalLM

				UpperCamelCase		=     Accelerator()

				with init_empty_weights():
						UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(
						    'EleutherAI/gpt-neo-125m'				,      )
						model.tie_weights()
						UpperCamelCase		=     infer_auto_device_map(A_       )
						UpperCamelCase		=     'cpu'

				UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(
				    'EleutherAI/gpt-neo-125m'				,      device_map=A_				,      load_in_abit=A_				,      llm_inta_enable_fpaa_cpu_offload=A_       )

				# This should not work and get value error
				with self.assertRaises(A_       ):
						UpperCamelCase		=     accelerator.prepare(A_       )



		@slow
		@require_bnb
		@require_multi_gpu
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				from transformers import AutoModelForCausalLM

				UpperCamelCase		=     {'distributed_type': DistributedType.MULTI_GPU}

				with init_empty_weights():
						UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(
						    'EleutherAI/gpt-neo-125m'				,      )
						model.tie_weights()
						UpperCamelCase		=     infer_auto_device_map(A_       )
						UpperCamelCase		=     1

				UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(
				    'EleutherAI/gpt-neo-125m'				,      load_in_abit=A_				,      device_map=A_				,      )
				UpperCamelCase		=     Accelerator()

				# This should not work and get value error
				with self.assertRaises(A_       ):
						UpperCamelCase		=     accelerator.prepare(A_       )

				PartialState._reset_state()



		@slow
		@require_bnb
		@require_multi_gpu
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				from transformers import AutoModelForCausalLM

				with init_empty_weights():
						UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(
						    'EleutherAI/gpt-neo-125m'				,      )
						UpperCamelCase		=     infer_auto_device_map(A_       )
						UpperCamelCase		=     1

				UpperCamelCase		=     AutoModelForCausalLM.from_pretrained(
				    'EleutherAI/gpt-neo-125m'				,      load_in_abit=A_				,      device_map=A_				,      )
				UpperCamelCase		=     Accelerator()

				# This should work
				UpperCamelCase		=     accelerator.prepare(A_       )



		@require_cuda
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				UpperCamelCase		=     torch.nn.Linear(10				,      10       )
				UpperCamelCase		=     torch.optim.SGD(model.parameters()				,      lr=0.01       )
				UpperCamelCase		=     Accelerator(cpu=A_       )
				UpperCamelCase		=     accelerator.prepare(A_       )


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase					: Union[str, Any]      =       {
    "configuration_instructblip": [
        "INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "InstructBlipConfig",
        "InstructBlipQFormerConfig",
        "InstructBlipVisionConfig",
    ],
    "processing_instructblip": ["InstructBlipProcessor"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase					: List[Any]      =       [
					    "INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "InstructBlipQFormerModel",
					    "InstructBlipPreTrainedModel",
					    "InstructBlipForConditionalGeneration",
					    "InstructBlipVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_instructblip import (
					    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    InstructBlipConfig,
					    InstructBlipQFormerConfig,
					    InstructBlipVisionConfig,
					)
					from .processing_instructblip import InstructBlipProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_instructblip import (
										    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    InstructBlipForConditionalGeneration,
										    InstructBlipPreTrainedModel,
										    InstructBlipQFormerModel,
										    InstructBlipVisionModel,
										)

else:
					import sys

					_UpperCAmelCase					: Union[str, Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
