



'''simple docstring'''






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__    : Dict    ,       UpperCamelCase__    : int    ,       UpperCamelCase__    : str    ,       UpperCamelCase__    : Tuple		)		->  Union[str, 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__    : Dict    ,       UpperCamelCase__    : List[str]    ,       UpperCamelCase__    : Any    ,       UpperCamelCase__    : List[str]=True		)		->  Optional[int]:
    model.train()
    A       :       Dict							=						model(UpperCamelCase__		)
    A       :       str							=						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__    : Dict    ,       UpperCamelCase__    : Optional[int]=False		)		->  int:
    set_seed(42		)
    A       :       List[str]							=						RegressionModel()
    A       :       str							=						deepcopy(UpperCamelCase__		)
    A       :       Optional[int]							=						RegressionDataset(length=80		)
    A       :       Optional[int]							=						DataLoader(UpperCamelCase__    ,       batch_size=16		)
    model.to(accelerator.device		)
    if sched:
        A       :       Tuple							=						AdamW(params=model.parameters()    ,       lr=1e-3		)
        A       :       int							=						AdamW(params=ddp_model.parameters()    ,       lr=1e-3		)
        A       :       List[Any]							=						LambdaLR(UpperCamelCase__    ,       lr_lambda=lambda UpperCamelCase__		: epoch**0.6_5		)
        A       :       Tuple							=						LambdaLR(UpperCamelCase__    ,       lr_lambda=lambda UpperCamelCase__		: epoch**0.6_5		)
    # Make a copy of `model`
    if sched:
        A       :       str							=						accelerator.prepare(UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__    ,       UpperCamelCase__		)
    else:
        A       :       Tuple							=						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		)		->  int:
    # Test when on a single CPU or GPU that the context manager does nothing
    A       :       Union[str, Any]							=						get_training_setup(UpperCamelCase__		)
    # Use a single batch
    A       :       Optional[int]							=						next(iter(UpperCamelCase__		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        A       :       List[Any]							=						accelerator.gather((ddp_input, ddp_target)		)
        A       :       Optional[int]							=						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_337 + iteration		)
        A       :       Optional[Any]							=						ddp_input[torch.randperm(len(UpperCamelCase__		)		)]
def 							_lowerCamelCase(  UpperCamelCase__    : Optional[int]		)		->  Any:
    # Test on distributed setup that context manager behaves properly
    A       :       List[Any]							=						get_training_setup(UpperCamelCase__		)
    # Use a single batch
    A       :       Optional[int]							=						next(iter(UpperCamelCase__		)		).values()
    for iteration in range(3		):
        # Gather the distributed inputs and targs for the base model
        A       :       List[str]							=						accelerator.gather((ddp_input, ddp_target)		)
        A       :       Tuple							=						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_337 + iteration		)
        A       :       int							=						ddp_input[torch.randperm(len(UpperCamelCase__		)		)]
def 							_lowerCamelCase(  UpperCamelCase__    : Union[str, Any]=False    ,       UpperCamelCase__    : List[Any]=False		)		->  str:
    A       :       Optional[Any]							=						Accelerator(
        split_batches=UpperCamelCase__    ,       dispatch_batches=UpperCamelCase__    ,       gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    A       :       Optional[Any]							=						get_training_setup(UpperCamelCase__		)
    for iteration, batch in enumerate(UpperCamelCase__		):
        A       :       List[str]							=						batch.values()
        # Gather the distributed inputs and targs for the base model
        A       :       List[Any]							=						accelerator.gather((ddp_input, ddp_target)		)
        A       :       Tuple							=						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_337 + iteration		)
        A       :       Optional[int]							=						ddp_input[torch.randperm(len(UpperCamelCase__		)		)]
    GradientState._reset_state()
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]=False    ,       UpperCamelCase__    : List[str]=False		)		->  Dict:
    A       :       Tuple							=						Accelerator(
        split_batches=UpperCamelCase__    ,       dispatch_batches=UpperCamelCase__    ,       gradient_accumulation_steps=2		)
    # Test that context manager behaves properly
    A       :       Any							=						get_training_setup(UpperCamelCase__    ,       UpperCamelCase__		)
    for iteration, batch in enumerate(UpperCamelCase__		):
        A       :       Dict							=						batch.values()
        # Gather the distributed inputs and targs for the base model
        A       :       Dict							=						accelerator.gather((ddp_input, ddp_target)		)
        A       :       Dict							=						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'''
        A       :       Tuple							=						(((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_337 + iteration		)
    GradientState._reset_state()
def 							_lowerCamelCase(  )		->  Union[str, Any]:
    A       :       str							=						Accelerator()

    A       :       Tuple							=						RegressionDataset(length=80		)
    A       :       str							=						DataLoader(UpperCamelCase__    ,       batch_size=16		)
    A       :       List[str]							=						RegressionDataset(length=96		)
    A       :       Any							=						DataLoader(UpperCamelCase__    ,       batch_size=16		)
    A       :       List[Any]							=						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(  )		->  Union[str, Any]:
    A       :       List[str]							=						Accelerator()
    A       :       int							=						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__    : Optional[Any]		)		->  List[Any]:
    # For xla_spawn (TPUs)
    main()


if __name__ == "__main__":
     main()







'''simple docstring'''






from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


snake_case_     =	["""text""", """image""", """audio"""]
def 							_lowerCamelCase(  UpperCamelCase__    : List[str]		)		->  str:
    A       :       int							=						[]

    for input_type in input_types:
        if input_type == "text":
            inputs.append('''Text input'''		)
        elif input_type == "image":
            inputs.append(
                Image.open(Path(get_tests_dir('''fixtures/tests_samples/COCO'''		)		) / '''000000039769.png'''		).resize((512, 512)		)		)
        elif input_type == "audio":
            inputs.append(torch.ones(3_000		)		)
        elif isinstance(UpperCamelCase__    ,       UpperCamelCase__		):
            inputs.append(create_inputs(UpperCamelCase__		)		)
        else:
            raise ValueError(F'''Invalid type requested: {input_type}'''		)

    return inputs
def 							_lowerCamelCase(  UpperCamelCase__    : List		)		->  Tuple:
    A       :       Optional[int]							=						[]

    for output in outputs:
        if isinstance(UpperCamelCase__    ,       (str, AgentText)		):
            output_types.append('''text'''		)
        elif isinstance(UpperCamelCase__    ,       (Image.Image, AgentImage)		):
            output_types.append('''image'''		)
        elif isinstance(UpperCamelCase__    ,       (torch.Tensor, AgentAudio)		):
            output_types.append('''audio'''		)
        else:
            raise ValueError(F'''Invalid output: {output}'''		)

    return output_types





@is_tool_test
class   _lowercase						:


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''inputs'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''outputs'''					)					)

       A       :       Any							=						self.tool.inputs
       for _input in inputs:
           if isinstance(_input    ,   _UpperCAmelCase					):
               for __input in _input:
                   self.assertTrue(__input in authorized_types					)
           else:
               self.assertTrue(_input in authorized_types					)

       A       :       Tuple							=						self.tool.outputs
       for _output in outputs:
           self.assertTrue(_output in authorized_types					)


   def 					snake_case				( self					):
       A       :       Any							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       # There is a single output
       if len(self.tool.outputs					) == 1:
           A       :       Optional[int]							=						[outputs]

       self.assertListEqual(output_types(_UpperCAmelCase					)    ,   self.tool.outputs					)


   def 					snake_case				( self					):
       self.assertTrue(hasattr(self.tool    ,   '''description'''					)					)
       self.assertTrue(hasattr(self.tool    ,   '''default_checkpoint'''					)					)
       self.assertTrue(self.tool.description.startswith('''This is a tool that'''					)					)


   def 					snake_case				( self					):
       A       :       List[str]							=						create_inputs(self.tool.inputs					)
       A       :       Dict							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       int							=						[outputs]

       self.assertEqual(len(_UpperCAmelCase					)    ,   len(self.tool.outputs					)					)

       for output, output_type in zip(_UpperCAmelCase    ,   self.tool.outputs					):
           A       :       Tuple							=						AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					)					)


   def 					snake_case				( self					):
       A       :       Tuple							=						create_inputs(self.tool.inputs					)

       A       :       Dict							=						[]

       for _input, input_type in zip(_UpperCAmelCase    ,   self.tool.inputs					):
           if isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
               _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
           else:
               _inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)

        # Should not raise an error
       A       :       List[Any]							=						self.tool(*_UpperCAmelCase					)

       if not isinstance(_UpperCAmelCase    ,   _UpperCAmelCase					):
           A       :       Any							=						[outputs]

       self.assertEqual(len(_UpperCAmelCase					)    ,   len(self.tool.outputs					)					)
