

"""simple docstring"""





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   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						List[str]  				=	torch.nn.Linear(2						,     4 )
	lowerCAmelCase__	:						List[str]  				=	torch.optim.AdamW(model.parameters()						,     lr=1.0 )
	lowerCAmelCase__	:						Tuple  				=	torch.optim.lr_scheduler.OneCycleLR(A_						,     max_lr=0.01						,     steps_per_epoch=2						,     epochs=1 )
	lowerCAmelCase__	:						Optional[Any]  				=	DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
	lowerCAmelCase__	:						int  				=	DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )

	return model, optimizer, scheduler, train_dl, valid_dl

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	return (model.weight.abs().sum() + model.bias.abs().sum()).item()

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Union[str, Any]  				=	torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
	model.load_state_dict(A_ )







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					@require_cuda
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						List[str]  				=	Accelerator()
						assert PartialState._shared_state["_cpu"] is False
						assert PartialState._shared_state["device"].type == "cuda"
						with self.assertRaises(lowercase_		):
							lowerCAmelCase__	:						Dict  				=	Accelerator(cpu=lowercase_		)







					def        __lowerCAmelCase (       self			:				str		):
						lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator()
						lowerCAmelCase__	:						Optional[Any]  				=	GradientState()
						assert state.num_steps == 1
						lowerCAmelCase__	:						int  				=	4
						assert state.num_steps == 4

						assert state.sync_gradients is True
						lowerCAmelCase__	:						str  				=	False
						assert state.sync_gradients is False
						GradientState._reset_state()







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Dict  				=	Accelerator()
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	create_components()

						(
						    (
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,(
						    lowerCAmelCase__
						)				,
						)	:						int  				=	accelerator.prepare(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		)

						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        __lowerCAmelCase (       self			:				int		):
						lowerCAmelCase__	:						str  				=	Accelerator()
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Optional[int]  				=	create_components()
						accelerator.prepare(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		)
						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        __lowerCAmelCase (       self			:				Optional[Any]		):
						PartialState._reset_state()

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

						with patch('''torch.cuda.set_device'''   ,lowercase_		), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''		):
							lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator()
							self.assertEqual(str(accelerator.state.device		)   ,'''cuda:64'''		)







					def        __lowerCAmelCase (       self			:				Dict		):
						lowerCAmelCase__	:						str  				=	Accelerator()
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						List[Any]  				=	create_components()
						accelerator.prepare(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		)

						lowerCAmelCase__	:						int  				=	get_signature(lowercase_		)

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

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

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







					def        __lowerCAmelCase (       self			:				List[Any]		):
						lowerCAmelCase__	:						Tuple  				=	Accelerator()
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	create_components()
						accelerator.prepare(lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		)

						lowerCAmelCase__	:						Any  				=	get_signature(lowercase_		)

						# saving hook
						def save_config(lowercase_			:				str   ,lowercase_			:				List[Any]   ,lowercase_			:				Optional[int]		):
							lowerCAmelCase__	:						str  				=	{'''class_name''': models[0].__class__.__name__}

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

        # loading hook
						def load_config(lowercase_			:				Optional[Any]   ,lowercase_			:				str		):
							with open(os.path.join(lowercase_   ,'''data.json'''		)   ,'''r'''		) as f:
								lowerCAmelCase__	:						Dict  				=	json.load(lowercase_		)

							lowerCAmelCase__	:						Any  				=	config['''class_name''']

						lowerCAmelCase__	:						Optional[int]  				=	accelerator.register_save_state_pre_hook(lowercase_		)
						lowerCAmelCase__	:						Optional[Any]  				=	accelerator.register_load_state_pre_hook(lowercase_		)

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

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

							# random class name to verify correct one is loaded
							lowerCAmelCase__	:						List[str]  				=	'''random'''

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

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

							# random class name to verify correct one is loaded
							lowerCAmelCase__	:						str  				=	'''random'''

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

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







					def        __lowerCAmelCase (       self			:				Union[str, Any]		):
						lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator()
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Any  				=	create_components()
						lowerCAmelCase__	:						List[str]  				=	None

						# This should work
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	accelerator.prepare(
						    lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_   ,lowercase_		)
						self.assertTrue(dummy_obj is None		)







					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						lowerCAmelCase__	:						Optional[Any]  				=	Accelerator()
						lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__				,lowerCAmelCase__	:						Dict  				=	create_components()
						lowerCAmelCase__	:						Optional[Any]  				=	[1, 2, 3]

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







					@slow
					@require_bnb
					def        __lowerCAmelCase (       self			:				List[Any]		):
						from transformers import AutoModelForCausalLM

						lowerCAmelCase__	:						List[str]  				=	AutoModelForCausalLM.from_pretrained(
						    '''EleutherAI/gpt-neo-125m'''   ,load_in_abit=lowercase_   ,device_map={'''''': 0}   ,)
						lowerCAmelCase__	:						Union[str, Any]  				=	Accelerator()

						# This should work
						lowerCAmelCase__	:						Optional[int]  				=	accelerator.prepare(lowercase_		)







					@slow
					@require_bnb
					def        __lowerCAmelCase (       self			:				int		):
						from transformers import AutoModelForCausalLM

						lowerCAmelCase__	:						Tuple  				=	Accelerator()

						with init_empty_weights():
							lowerCAmelCase__	:						List[str]  				=	AutoModelForCausalLM.from_pretrained(
							    '''EleutherAI/gpt-neo-125m'''   ,)
							model.tie_weights()
							lowerCAmelCase__	:						List[Any]  				=	infer_auto_device_map(lowercase_		)
							lowerCAmelCase__	:						int  				=	'''cpu'''

						lowerCAmelCase__	:						Any  				=	AutoModelForCausalLM.from_pretrained(
						    '''EleutherAI/gpt-neo-125m'''   ,device_map=lowercase_   ,load_in_abit=lowercase_   ,llm_inta_enable_fpaa_cpu_offload=lowercase_		)

						# This should not work and get value error
						with self.assertRaises(lowercase_		):
							lowerCAmelCase__	:						Any  				=	accelerator.prepare(lowercase_		)







					@slow
					@require_bnb
					@require_multi_gpu
					def        __lowerCAmelCase (       self			:				Optional[Any]		):
						from transformers import AutoModelForCausalLM

						lowerCAmelCase__	:						List[Any]  				=	{'''distributed_type''': DistributedType.MULTI_GPU}

						with init_empty_weights():
							lowerCAmelCase__	:						Tuple  				=	AutoModelForCausalLM.from_pretrained(
							    '''EleutherAI/gpt-neo-125m'''   ,)
							model.tie_weights()
							lowerCAmelCase__	:						Any  				=	infer_auto_device_map(lowercase_		)
							lowerCAmelCase__	:						Any  				=	1

						lowerCAmelCase__	:						Tuple  				=	AutoModelForCausalLM.from_pretrained(
						    '''EleutherAI/gpt-neo-125m'''   ,load_in_abit=lowercase_   ,device_map=lowercase_   ,)
						lowerCAmelCase__	:						Tuple  				=	Accelerator()

						# This should not work and get value error
						with self.assertRaises(lowercase_		):
							lowerCAmelCase__	:						List[str]  				=	accelerator.prepare(lowercase_		)

						PartialState._reset_state()







					@slow
					@require_bnb
					@require_multi_gpu
					def        __lowerCAmelCase (       self			:				Tuple		):
						from transformers import AutoModelForCausalLM

						with init_empty_weights():
							lowerCAmelCase__	:						List[str]  				=	AutoModelForCausalLM.from_pretrained(
							    '''EleutherAI/gpt-neo-125m'''   ,)
							lowerCAmelCase__	:						Optional[int]  				=	infer_auto_device_map(lowercase_		)
							lowerCAmelCase__	:						int  				=	1

						lowerCAmelCase__	:						str  				=	AutoModelForCausalLM.from_pretrained(
						    '''EleutherAI/gpt-neo-125m'''   ,load_in_abit=lowercase_   ,device_map=lowercase_   ,)
						lowerCAmelCase__	:						List[Any]  				=	Accelerator()

						# This should work
						lowerCAmelCase__	:						List[str]  				=	accelerator.prepare(lowercase_		)







					@require_cuda
					def        __lowerCAmelCase (       self			:				List[str]		):
						lowerCAmelCase__	:						Dict  				=	torch.nn.Linear(1_0   ,1_0		)
						lowerCAmelCase__	:						List[Any]  				=	torch.optim.SGD(model.parameters()   ,lr=0.01		)
						lowerCAmelCase__	:						str  				=	Accelerator(cpu=lowercase_		)
						lowerCAmelCase__	:						Optional[int]  				=	accelerator.prepare(lowercase_		)







"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__UpperCamelCase  :      Dict							      =		{
    '''configuration_clip''': [
        '''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''CLIPConfig''',
        '''CLIPOnnxConfig''',
        '''CLIPTextConfig''',
        '''CLIPVisionConfig''',
    ],
    '''processing_clip''': ['''CLIPProcessor'''],
    '''tokenization_clip''': ['''CLIPTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      int							      =		['''CLIPTokenizerFast''']

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		['''CLIPFeatureExtractor''']
							__UpperCamelCase  :      Optional[Any]							      =		['''CLIPImageProcessor''']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Tuple							      =		[
							    '''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''CLIPModel''',
							    '''CLIPPreTrainedModel''',
							    '''CLIPTextModel''',
							    '''CLIPTextModelWithProjection''',
							    '''CLIPVisionModel''',
							    '''CLIPVisionModelWithProjection''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[int]							      =		[
							    '''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFCLIPModel''',
							    '''TFCLIPPreTrainedModel''',
							    '''TFCLIPTextModel''',
							    '''TFCLIPVisionModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase  :      Optional[Any]							      =		[
							    '''FlaxCLIPModel''',
							    '''FlaxCLIPPreTrainedModel''',
							    '''FlaxCLIPTextModel''',
							    '''FlaxCLIPTextPreTrainedModel''',
							    '''FlaxCLIPVisionModel''',
							    '''FlaxCLIPVisionPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_clip import (
							    CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    CLIPConfig,
							    CLIPOnnxConfig,
							    CLIPTextConfig,
							    CLIPVisionConfig,
							)
							from .processing_clip import CLIPProcessor
							from .tokenization_clip import CLIPTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_clip_fast import CLIPTokenizerFast

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .feature_extraction_clip import CLIPFeatureExtractor
														from .image_processing_clip import CLIPImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_clip import (
														    CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    CLIPModel,
														    CLIPPreTrainedModel,
														    CLIPTextModel,
														    CLIPTextModelWithProjection,
														    CLIPVisionModel,
														    CLIPVisionModelWithProjection,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_clip import (
														    TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFCLIPModel,
														    TFCLIPPreTrainedModel,
														    TFCLIPTextModel,
														    TFCLIPVisionModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_clip import (
														    FlaxCLIPModel,
														    FlaxCLIPPreTrainedModel,
														    FlaxCLIPTextModel,
														    FlaxCLIPTextPreTrainedModel,
														    FlaxCLIPVisionModel,
														    FlaxCLIPVisionPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase  :      Tuple							      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




