

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():





	'''simple docstring'''
	snake_case_      		= torch.nn.Linear(2			, 4	)
	snake_case_      		= torch.optim.AdamW(model.parameters()			, lr=1.0	)
	snake_case_      		= torch.optim.lr_scheduler.OneCycleLR(__A			, max_lr=0.01			, steps_per_epoch=2			, epochs=1	)
	snake_case_      		= DataLoader(TensorDataset(torch.tensor([1, 2, 3]	)	)	)
	snake_case_      		= DataLoader(TensorDataset(torch.tensor([4, 5, 6]	)	)	)

	return model, optimizer, scheduler, train_dl, valid_dl





def a(lowercase__	):





	'''simple docstring'''
	return (model.weight.abs().sum() + model.bias.abs().sum()).item()





def a(lowercase__	):





	'''simple docstring'''
	snake_case_      		= torch.nn.Linear(*tuple(model.weight.T.shape	)	).state_dict()
	model.load_state_dict(__A	)






class SCREAMING_SNAKE_CASE  (		__lowerCAmelCase							):






			"""simple docstring"""




			@require_cuda
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				assert PartialState._shared_state["_cpu"] is False
				assert PartialState._shared_state["device"].type == "cuda"
				with self.assertRaises(_UpperCamelCase							):
					snake_case_      		= Accelerator(cpu=_UpperCamelCase							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				snake_case_      		= GradientState()
				assert state.num_steps == 1
				snake_case_      		= 4
				assert state.num_steps == 4

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				snake_case_      		= create_components()

				(
				    snake_case_							
				)      		= accelerator.prepare(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase							)

				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							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				snake_case_      		= create_components()
				accelerator.prepare(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase							)
				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							):

				"""simple docstring"""


				PartialState._reset_state()

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

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				snake_case_      		= create_components()
				accelerator.prepare(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase							)

				snake_case_      		= get_signature(_UpperCamelCase							)

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

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

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				snake_case_      		= create_components()
				accelerator.prepare(_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase							)

				snake_case_      		= get_signature(_UpperCamelCase							)

				# saving hook
				def save_config(__UpperCamelCase ,				__UpperCamelCase ,				__UpperCamelCase							):
					snake_case_      		= {"""class_name""": models[0].__class__.__name__}

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

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

					snake_case_      		= config["""class_name"""]

				snake_case_      		= accelerator.register_save_state_pre_hook(_UpperCamelCase							)
				snake_case_      		= accelerator.register_load_state_pre_hook(_UpperCamelCase							)

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

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

					# random class name to verify correct one is loaded
					snake_case_      		= """random"""

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

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

					# random class name to verify correct one is loaded
					snake_case_      		= """random"""

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

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				snake_case_      		= create_components()
				snake_case_      		= None

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


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= Accelerator()
				snake_case_      		= create_components()
				snake_case_      		= [1, 2, 3]

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


			@slow
			@require_bnb
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				from transformers import AutoModelForCausalLM

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

				# This should work
				snake_case_      		= accelerator.prepare(_UpperCamelCase							)


			@slow
			@require_bnb
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				from transformers import AutoModelForCausalLM

				snake_case_      		= Accelerator()

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

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

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


			@slow
			@require_bnb
			@require_multi_gpu
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				from transformers import AutoModelForCausalLM

				snake_case_      		= {"""distributed_type""": DistributedType.MULTI_GPU}

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

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

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

				PartialState._reset_state()


			@slow
			@require_bnb
			@require_multi_gpu
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				from transformers import AutoModelForCausalLM

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

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

				# This should work
				snake_case_      		= accelerator.prepare(_UpperCamelCase							)







			@require_cuda
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				snake_case_      		= torch.nn.Linear(10 ,				10							)
				snake_case_      		= torch.optim.SGD(model.parameters() ,				lr=0.01							)
				snake_case_      		= Accelerator(cpu=_UpperCamelCase							)
				snake_case_      		= accelerator.prepare(_UpperCamelCase							)



from collections import defaultdict





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= first_str.lower().strip()
	snake_case_      		= second_str.lower().strip()

	# Remove whitespace
	snake_case_      		= first_str.replace(' '			, ''	)
	snake_case_      		= second_str.replace(' '			, ''	)

	# Strings of different lengths are not anagrams
	if len(lowercase__	) != len(lowercase__	):
		return False

	# Default values for count should be 0
	snake_case_      		= defaultdict(lowercase__	)

	# For each character in input strings,
	# increment count in the corresponding
	for i in range(len(lowercase__	)	):
		count[first_str[i]] += 1
		count[second_str[i]] -= 1

	return all(_count == 0 for _count in count.values()	)


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		A											=     input('Enter the first string ').strip()
		A											=     input('Enter the second string ').strip()

		A											=     check_anagrams(input_a, input_b)
		print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
