
from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      str			,				UpperCAmelCase__				:      Optional[Any]       )  ->    List[Any]:
							for param, grad_param in zip(model_a.parameters()			,				model_b.parameters()       ):
														if not param.requires_grad:
																					continue
														if not did_step:
																					# Grads should not be in sync
																					assert (
																					    torch.allclose(param.grad			,				grad_param.grad       ) is False
																					), F'''Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'''
														else:
																					# Grads should be in sync
																					assert (
																					    torch.allclose(param.grad			,				grad_param.grad       ) is True
																					), F'''Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'''



def      __lowerCAmelCase						(							UpperCAmelCase__				:      int			,				UpperCAmelCase__				:      Tuple			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      List[Any]			,				UpperCAmelCase__				:      Union[str, Any]=True       )  ->    Tuple:
							model.train()
							lowerCamelCase_							  =					model(UpperCAmelCase__       )
							lowerCamelCase_							  =					F.mse_loss(UpperCAmelCase__			,				target.to(output.device       )       )
							if not do_backward:
														loss /= accelerator.gradient_accumulation_steps
														loss.backward()
							else:
														accelerator.backward(UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any			,				UpperCAmelCase__				:      int=False       )  ->    Optional[Any]:
							set_seed(4_2       )
							lowerCamelCase_							  =					RegressionModel()
							lowerCamelCase_							  =					deepcopy(UpperCAmelCase__       )
							lowerCamelCase_							  =					RegressionDataset(length=8_0       )
							lowerCamelCase_							  =					DataLoader(UpperCAmelCase__			,				batch_size=1_6       )
							model.to(accelerator.device       )
							if sched:
														lowerCamelCase_							  =					AdamW(params=model.parameters()			,				lr=1e-3       )
														lowerCamelCase_							  =					AdamW(params=ddp_model.parameters()			,				lr=1e-3       )
														lowerCamelCase_							  =					LambdaLR(UpperCAmelCase__			,				lr_lambda=lambda UpperCAmelCase__       : epoch**0.6_5       )
														lowerCamelCase_							  =					LambdaLR(UpperCAmelCase__			,				lr_lambda=lambda UpperCAmelCase__       : epoch**0.6_5       )
							# Make a copy of `model`
							if sched:
														lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.prepare(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
							else:
														lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.prepare(UpperCAmelCase__			,				UpperCAmelCase__       )
							if sched:
														return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
							return model, ddp_model, dataloader



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Tuple       )  ->    List[Any]:
							# Test when on a single CPU or GPU that the context manager does nothing
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					get_training_setup(UpperCAmelCase__       )
							# Use a single batch
							lowerCamelCase_  ,   lowerCamelCase_							  =					next(iter(UpperCAmelCase__       )       ).values()
							for iteration in range(3       ):
														# Gather the distributed inputs and targs for the base model
														lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.gather((ddp_input, ddp_target)       )
														lowerCamelCase_  ,   lowerCamelCase_							  =					input.to(accelerator.device       ), target.to(accelerator.device       )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCAmelCase__       ):
																												step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														else:
																					# Sync grads
																					step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )

														# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
														check_model_parameters(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														for param, ddp_param in zip(model.parameters()			,				ddp_model.parameters()       ):
																					if not param.requires_grad:
																												continue
																					assert torch.allclose(
																					    param.grad			,				ddp_param.grad       ), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
														lowerCamelCase_							  =					ddp_input[torch.randperm(len(UpperCAmelCase__       )       )]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Union[str, Any]       )  ->    int:
							# Test on distributed setup that context manager behaves properly
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					get_training_setup(UpperCAmelCase__       )
							# Use a single batch
							lowerCamelCase_  ,   lowerCamelCase_							  =					next(iter(UpperCAmelCase__       )       ).values()
							for iteration in range(3       ):
														# Gather the distributed inputs and targs for the base model
														lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.gather((ddp_input, ddp_target)       )
														lowerCamelCase_  ,   lowerCamelCase_							  =					input.to(accelerator.device       ), target.to(accelerator.device       )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														# Do "gradient accumulation" (noop)
														if iteration % 2 == 0:
																					# Accumulate grads locally
																					with accelerator.no_sync(UpperCAmelCase__       ):
																												step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														else:
																					# Sync grads
																					step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()			,				ddp_model.parameters()       ):
																					if not param.requires_grad:
																												continue
																					if iteration % 2 == 0:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad			,				ddp_param.grad       ) is False
																												), F'''Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad			,				ddp_param.grad       ) is True
																												), F'''Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
														lowerCamelCase_							  =					ddp_input[torch.randperm(len(UpperCAmelCase__       )       )]



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str=False			,				UpperCAmelCase__				:      Union[str, Any]=False       )  ->    Optional[Any]:
							lowerCamelCase_							  =					Accelerator(
							    split_batches=UpperCAmelCase__			,				dispatch_batches=UpperCAmelCase__			,				gradient_accumulation_steps=2       )
							# Test that context manager behaves properly
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					get_training_setup(UpperCAmelCase__       )
							for iteration, batch in enumerate(UpperCAmelCase__       ):
														lowerCamelCase_  ,   lowerCamelCase_							  =					batch.values()
														# Gather the distributed inputs and targs for the base model
														lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.gather((ddp_input, ddp_target)       )
														lowerCamelCase_  ,   lowerCamelCase_							  =					input.to(accelerator.device       ), target.to(accelerator.device       )
														# Perform our initial ground truth step in non "DDP"
														step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														# Do "gradient accumulation" (noop)
														with accelerator.accumulate(UpperCAmelCase__       ):
																					step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )

														# DDP model and model should only be in sync when not (iteration % 2 == 0)
														for param, ddp_param in zip(model.parameters()			,				ddp_model.parameters()       ):
																					if not param.requires_grad:
																												continue
																					if ((iteration + 1) % 2 == 0) or (iteration == len(UpperCAmelCase__       ) - 1):
																												# Grads should be in sync
																												assert (
																												    torch.allclose(param.grad			,				ddp_param.grad       ) is True
																												), F'''Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'''
																					else:
																												# Grads should not be in sync
																												assert (
																												    torch.allclose(param.grad			,				ddp_param.grad       ) is False
																												), F'''Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'''

        # Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
														lowerCamelCase_							  =					ddp_input[torch.randperm(len(UpperCAmelCase__       )       )]
							GradientState._reset_state()



def      __lowerCAmelCase						(							UpperCAmelCase__				:      str=False			,				UpperCAmelCase__				:      Tuple=False       )  ->    List[str]:
							lowerCamelCase_							  =					Accelerator(
							    split_batches=UpperCAmelCase__			,				dispatch_batches=UpperCAmelCase__			,				gradient_accumulation_steps=2       )
							# Test that context manager behaves properly
							lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_  ,   lowerCamelCase_							  =					get_training_setup(UpperCAmelCase__			,				UpperCAmelCase__       )
							for iteration, batch in enumerate(UpperCAmelCase__       ):
														lowerCamelCase_  ,   lowerCamelCase_							  =					batch.values()
														# Gather the distributed inputs and targs for the base model
														lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.gather((ddp_input, ddp_target)       )
														lowerCamelCase_  ,   lowerCamelCase_							  =					input.to(accelerator.device       ), target.to(accelerator.device       )
														# Perform our initial ground truth step in non "DDP"
														model.train()
														ddp_model.train()
														step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														opt.step()

														if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(UpperCAmelCase__       )):
																					if split_batches:
																												sched.step()
																					else:
																												for _ in range(accelerator.num_processes       ):
																																			sched.step()
														opt.zero_grad()
														# Perform gradient accumulation under wrapper
														with accelerator.accumulate(UpperCAmelCase__       ):
																					step_model(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
																					ddp_opt.step()
																					ddp_sched.step()
																					ddp_opt.zero_grad()

														# Learning rates should be the same
														assert (
														    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
														), F'''Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]['lr']}\nDDP opt: {ddp_opt.param_groups[0]['lr']}\n'''
														lowerCamelCase_							  =					(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(UpperCAmelCase__       ))
														if accelerator.num_processes > 1:
																					check_model_parameters(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )
														# Shuffle ddp_input on each iteration
														torch.manual_seed(1_3_3_7 + iteration       )
							GradientState._reset_state()



def      __lowerCAmelCase						(							)  ->    Optional[Any]:
							lowerCamelCase_							  =					Accelerator()

							lowerCamelCase_							  =					RegressionDataset(length=8_0       )
							lowerCamelCase_							  =					DataLoader(UpperCAmelCase__			,				batch_size=1_6       )
							lowerCamelCase_							  =					RegressionDataset(length=9_6       )
							lowerCamelCase_							  =					DataLoader(UpperCAmelCase__			,				batch_size=1_6       )
							lowerCamelCase_  ,   lowerCamelCase_							  =					accelerator.prepare(UpperCAmelCase__			,				UpperCAmelCase__       )
							assert accelerator.gradient_state.active_dataloader is None
							for iteration, _ in enumerate(UpperCAmelCase__       ):
														assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCAmelCase__       )
														if iteration < len(UpperCAmelCase__       ) - 1:
																					assert not accelerator.gradient_state.end_of_dataloader
																					if iteration == 1:
																												for batch_num, _ in enumerate(UpperCAmelCase__       ):
																																			assert id(accelerator.gradient_state.active_dataloader       ) == id(UpperCAmelCase__       )
																																			if batch_num < len(UpperCAmelCase__       ) - 1:
																																										assert not accelerator.gradient_state.end_of_dataloader
																																			else:
																																										assert accelerator.gradient_state.end_of_dataloader
														else:
																					assert accelerator.gradient_state.end_of_dataloader
							assert accelerator.gradient_state.active_dataloader is None



def      __lowerCAmelCase						(							)  ->    int:
							lowerCamelCase_							  =					Accelerator()
							lowerCamelCase_							  =					accelerator.state
							if state.local_process_index == 0:
														print("""**Test `accumulate` gradient accumulation with dataloader break**"""       )
							test_dataloader_break()
							if state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print("""**Test NOOP `no_sync` context manager**"""       )
														test_noop_sync(UpperCAmelCase__       )
							if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
														if state.local_process_index == 0:
																					print("""**Test Distributed `no_sync` context manager**"""       )
														test_distributed_sync(UpperCAmelCase__       )
							if state.distributed_type == DistributedType.MULTI_GPU:
														for split_batch in [True, False]:
																					for dispatch_batches in [True, False]:
																												if state.local_process_index == 0:
																																			print(
																																			    """**Test `accumulate` gradient accumulation, """			,				F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''			,				)
																												test_gradient_accumulation(UpperCAmelCase__			,				UpperCAmelCase__       )

    # Currently will break on torch 2.0 +, need to investigate why
							if is_torch_version("""<"""			,				"""2.0"""       ) or state.distributed_type == DistributedType.NO:
														if state.local_process_index == 0:
																					print(
																					    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """			,				"""`split_batches=False`, `dispatch_batches=False`**"""			,				)
														test_gradient_accumulation_with_opt_and_scheduler()
														if state.distributed_type == DistributedType.MULTI_GPU:
																					for split_batch in [True, False]:
																												for dispatch_batches in [True, False]:
																																			if not split_batch and not dispatch_batches:
																																										continue
																																			if state.local_process_index == 0:
																																										print(
																																										    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """			,				F'''`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'''			,				)
																																			test_gradient_accumulation_with_opt_and_scheduler(UpperCAmelCase__			,				UpperCAmelCase__       )



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Any       )  ->    Union[str, Any]:
							# For xla_spawn (TPUs)
							main()


if __name__ == "__main__":
		main()







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import torch

from ..models.auto import AutoModelForSequenceClassification, AutoTokenizer
from .base import PipelineTool





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    '''facebook/bart-large-mnli'''
					SCREAMING_SNAKE_CASE			    =    (
					    '''This is a tool that classifies an English text using provided labels. It takes two inputs: `text`, which '''
					    '''should be the text to classify, and `labels`, which should be the list of labels to use for classification. '''
					    '''It returns the most likely label in the list of provided `labels` for the input text.'''
					)
					SCREAMING_SNAKE_CASE			    =    '''text_classifier'''
					SCREAMING_SNAKE_CASE			    =    AutoTokenizer
					SCREAMING_SNAKE_CASE			    =    AutoModelForSequenceClassification

					SCREAMING_SNAKE_CASE			    =    ['''text''', ['''text''']]
					SCREAMING_SNAKE_CASE			    =    ['''text''']

					def    lowercase__      (	self					:     Union[str, Any]   ):
												super().setup()
												lowerCamelCase_							  =					self.model.config
												lowerCamelCase_							  =					-1
												for idx, label in config.idalabel.items():
																			if label.lower().startswith("""entail"""   ):
																										lowerCamelCase_							  =					int(__UpperCamelCase   )
												if self.entailment_id == -1:
																			raise ValueError("""Could not determine the entailment ID from the model config, please pass it at init."""   )

					def    lowercase__      (	self					:     Any   ,					__UpperCamelCase					:     List[Any]   ,					__UpperCamelCase					:     Optional[Any]   ):
												lowerCamelCase_							  =					labels
												return self.pre_processor(
												    [text] * len(__UpperCamelCase   )   ,					[F'''This example is {label}''' for label in labels]   ,					return_tensors="""pt"""   ,					padding="""max_length"""   ,					)



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     List[str]   ):
												lowerCamelCase_							  =					outputs.logits
												lowerCamelCase_							  =					torch.argmax(logits[:, 2]   ).item()
												return self._labels[label_id]





