





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

    return model, optimizer, scheduler, train_dl, valid_dl





def 		UpperCamelCase							(				_lowerCamelCase   :   int			):
    return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def 		UpperCamelCase							(				_lowerCamelCase   :   Optional[Any]			):
    A__							=     torch.nn.Linear(*tuple(model.weight.T.shape			)			).state_dict()
    model.load_state_dict(_lowerCamelCase			)






class   UpperCAmelCase      (			UpperCamelCase__							):
 @require_cuda
 def 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[Any]:
     A__							=     Accelerator()
     assert PartialState._shared_state["_cpu"] is False
     assert PartialState._shared_state["device"].type == "cuda"
     with self.assertRaises(lowercase_				):
         A__							=     Accelerator(cpu=lowercase_				)
 def 	UpperCAmelCase_				(							self :List[Any]				)->				int:
     A__							=     Accelerator()
     A__							=     GradientState()
     assert state.num_steps == 1
     A__							=     4
     assert state.num_steps == 4

     assert state.sync_gradients is True
     A__							=     False
     assert state.sync_gradients is False
     GradientState._reset_state()
 def 	UpperCAmelCase_				(							self :List[str]				)->				Optional[int]:
     A__							=     Accelerator()
     A__,      A__,      A__,      A__,      A__							=     create_components()

     (
         (
         A__
     ),      (
         A__
     ),      (
         A__
     ),      (
         A__
     ),      (
         A__
     ),      
     )							=     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 	UpperCAmelCase_				(							self :List[Any]				)->				Optional[Any]:
     A__							=     Accelerator()
     A__,      A__,      A__,      A__,      A__							=     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 	UpperCAmelCase_				(							self :Tuple				)->				Optional[Any]:
     PartialState._reset_state()

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

     with patch("torch.cuda.set_device"		,			lowercase_				), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"				):
         A__							=     Accelerator()
         self.assertEqual(str(accelerator.state.device				)		,			"cuda:64"				)
 def 	UpperCAmelCase_				(							self :Dict				)->				List[str]:
     A__							=     Accelerator()
     A__,      A__,      A__,      A__,      A__							=     create_components()
     accelerator.prepare(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_				)

     A__							=     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 	UpperCAmelCase_				(							self :Dict				)->				Union[str, Any]:
     A__							=     Accelerator()
     A__,      A__,      A__,      A__,      A__							=     create_components()
     accelerator.prepare(lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_				)

     A__							=     get_signature(lowercase_				)

     # saving hook
     def save_config(lowercase_ :Optional[Any]		,			lowercase_ :Dict		,			lowercase_ :Dict				):
         A__							=     {"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[int]		,			lowercase_ :Any				):
         with open(os.path.join(lowercase_		,			"data.json"				)		,			"r"				) as f:
             A__							=     json.load(lowercase_				)

         A__							=     config["class_name"]

     A__							=     accelerator.register_save_state_pre_hook(lowercase_				)
     A__							=     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
         A__							=     "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
         A__							=     "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 	UpperCAmelCase_				(							self :Optional[int]				)->				Union[str, Any]:
     A__							=     Accelerator()
     A__,      A__,      A__,      A__,      A__							=     create_components()
     A__							=     None

     # This should work
     A__,      A__,      A__,      A__,      A__,      A__							=     accelerator.prepare(
         lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_		,			lowercase_				)
     self.assertTrue(dummy_obj is None				)
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				List[str]:
     A__							=     Accelerator()
     A__,      A__,      A__,      A__,      A__							=     create_components()
     A__							=     [1, 2, 3]

     # This should work
     A__,      A__,      A__,      A__,      A__,      A__							=     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 	UpperCAmelCase_				(							self :List[str]				)->				str:
     from transformers import AutoModelForCausalLM

     A__							=     AutoModelForCausalLM.from_pretrained(
         "EleutherAI/gpt-neo-125m"		,			load_in_abit=lowercase_		,			device_map={"": 0}		,			)
     A__							=     Accelerator()

     # This should work
     A__							=     accelerator.prepare(lowercase_				)
 @slow
 @require_bnb
 def 	UpperCAmelCase_				(							self :int				)->				Dict:
     from transformers import AutoModelForCausalLM

     A__							=     Accelerator()

     with init_empty_weights():
         A__							=     AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"		,			)
         model.tie_weights()
         A__							=     infer_auto_device_map(lowercase_				)
         A__							=     "cpu"

     A__							=     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_				):
         A__							=     accelerator.prepare(lowercase_				)
 @slow
 @require_bnb
 @require_multi_gpu
 def 	UpperCAmelCase_				(							self :Optional[Any]				)->				Optional[int]:
     from transformers import AutoModelForCausalLM

     A__							=     {"distributed_type": DistributedType.MULTI_GPU}

     with init_empty_weights():
         A__							=     AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"		,			)
         model.tie_weights()
         A__							=     infer_auto_device_map(lowercase_				)
         A__							=     1

     A__							=     AutoModelForCausalLM.from_pretrained(
         "EleutherAI/gpt-neo-125m"		,			load_in_abit=lowercase_		,			device_map=lowercase_		,			)
     A__							=     Accelerator()

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

     PartialState._reset_state()
 @slow
 @require_bnb
 @require_multi_gpu
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[Any]:
     from transformers import AutoModelForCausalLM

     with init_empty_weights():
         A__							=     AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"		,			)
         A__							=     infer_auto_device_map(lowercase_				)
         A__							=     1

     A__							=     AutoModelForCausalLM.from_pretrained(
         "EleutherAI/gpt-neo-125m"		,			load_in_abit=lowercase_		,			device_map=lowercase_		,			)
     A__							=     Accelerator()

     # This should work
     A__							=     accelerator.prepare(lowercase_				)



 @require_cuda
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[int]:
     A__							=     torch.nn.Linear(10		,			10				)
     A__							=     torch.optim.SGD(model.parameters()		,			lr=0.0_1				)
     A__							=     Accelerator(cpu=lowercase_				)
     A__							=     accelerator.prepare(lowercase_				)










'''simple docstring'''

import string
from math import logaa





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     document.translate(
        str.maketrans(""		,			""		,			string.punctuation			)			).replace("\n"		,			""			)
    A__							=     document_without_punctuation.split(" "			)  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     corpus.lower().translate(
        str.maketrans(""		,			""		,			string.punctuation			)			)  # strip all punctuation and replace it with ''
    A__							=     corpus_without_punctuation.split("\n"			)
    A__							=     term.lower()
    return (len([doc for doc in docs if term in doc]			), len(_lowerCamelCase			))





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple=False			):
    if smoothing:
        if n == 0:
            raise ValueError("log10(0) is undefined."			)
        return round(1 + logaa(n / (1 + df)			)		,			3			)

    if df == 0:
        raise ZeroDivisionError("df must be > 0"			)
    elif n == 0:
        raise ValueError("log10(0) is undefined."			)
    return round(logaa(n / df			)		,			3			)





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return round(tf * idf		,			3			)



