






'''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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase=True							):


   """simple docstring"""




   model.train()
   lowercase_	:    Optional[Any]           =     model(_UpperCamelCase							)
   lowercase_	:    List[Any]           =     F.mse_loss(_UpperCamelCase						,		target.to(output.device							)							)
   if not do_backward:
      loss /= accelerator.gradient_accumulation_steps
      loss.backward()
   else:
      accelerator.backward(_UpperCamelCase							)




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=False							):


   """simple docstring"""




   set_seed(42							)
   lowercase_	:    List[str]           =     RegressionModel()
   lowercase_	:    Tuple           =     deepcopy(_UpperCamelCase							)
   lowercase_	:    Tuple           =     RegressionDataset(length=80							)
   lowercase_	:    List[Any]           =     DataLoader(_UpperCamelCase						,		batch_size=16							)
   model.to(accelerator.device							)
   if sched:
      lowercase_	:    List[str]           =     AdamW(params=model.parameters()						,		lr=1e-3							)
      lowercase_	:    Optional[Any]           =     AdamW(params=ddp_model.parameters()						,		lr=1e-3							)
      lowercase_	:    Tuple           =     LambdaLR(_UpperCamelCase						,		lr_lambda=lambda _UpperCamelCase							: epoch**0.65							)
      lowercase_	:    str           =     LambdaLR(_UpperCamelCase						,		lr_lambda=lambda _UpperCamelCase							: epoch**0.65							)
   # Make a copy of `model`
   if sched:
      lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    Any           =     accelerator.prepare(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
   else:
      lowercase_    ,		lowercase_	:    Any           =     accelerator.prepare(_UpperCamelCase						,		_UpperCamelCase							)
   if sched:
      return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
   return model, ddp_model, dataloader




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_    ,		lowercase_	:    List[Any]           =     get_training_setup(_UpperCamelCase							)
   # Use a single batch
   lowercase_    ,		lowercase_	:    Optional[int]           =     next(iter(_UpperCamelCase							)							).values()
   for iteration in range(3							):
      # Gather the distributed inputs and targs for the base model
      lowercase_    ,		lowercase_	:    List[str]           =     accelerator.gather((ddp_input, ddp_target)							)
      lowercase_    ,		lowercase_	:    Any           =     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(1337 + iteration							)
      lowercase_	:    Dict           =     ddp_input[torch.randperm(len(_UpperCamelCase							)							)]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_    ,		lowercase_    ,		lowercase_	:    int           =     get_training_setup(_UpperCamelCase							)
   # Use a single batch
   lowercase_    ,		lowercase_	:    List[Any]           =     next(iter(_UpperCamelCase							)							).values()
   for iteration in range(3							):
      # Gather the distributed inputs and targs for the base model
      lowercase_    ,		lowercase_	:    Any           =     accelerator.gather((ddp_input, ddp_target)							)
      lowercase_    ,		lowercase_	:    Union[str, Any]           =     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(1337 + iteration							)
      lowercase_	:    str           =     ddp_input[torch.randperm(len(_UpperCamelCase							)							)]




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase=False						,		_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_	:    Any           =     Accelerator(
       split_batches=_UpperCamelCase						,		dispatch_batches=_UpperCamelCase						,		gradient_accumulation_steps=2							)
   # Test that context manager behaves properly
   lowercase_    ,		lowercase_    ,		lowercase_	:    Any           =     get_training_setup(_UpperCamelCase							)
   for iteration, batch in enumerate(_UpperCamelCase							):
      lowercase_    ,		lowercase_	:    Optional[int]           =     batch.values()
      # Gather the distributed inputs and targs for the base model
      lowercase_    ,		lowercase_	:    Optional[int]           =     accelerator.gather((ddp_input, ddp_target)							)
      lowercase_    ,		lowercase_	:    int           =     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(1337 + iteration							)
      lowercase_	:    int           =     ddp_input[torch.randperm(len(_UpperCamelCase							)							)]
   GradientState._reset_state()




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase=False						,		_UpperCamelCase=False							):


   """simple docstring"""




   lowercase_	:    int           =     Accelerator(
       split_batches=_UpperCamelCase						,		dispatch_batches=_UpperCamelCase						,		gradient_accumulation_steps=2							)
   # Test that context manager behaves properly
   lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_    ,		lowercase_	:    List[Any]           =     get_training_setup(_UpperCamelCase						,		_UpperCamelCase							)
   for iteration, batch in enumerate(_UpperCamelCase							):
      lowercase_    ,		lowercase_	:    List[Any]           =     batch.values()
      # Gather the distributed inputs and targs for the base model
      lowercase_    ,		lowercase_	:    Tuple           =     accelerator.gather((ddp_input, ddp_target)							)
      lowercase_    ,		lowercase_	:    Optional[int]           =     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"""
      lowercase_	:    int           =     (((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(1337 + iteration							)
   GradientState._reset_state()




def 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     Accelerator()

   lowercase_	:    str           =     RegressionDataset(length=80							)
   lowercase_	:    List[Any]           =     DataLoader(_UpperCamelCase						,		batch_size=16							)
   lowercase_	:    List[str]           =     RegressionDataset(length=96							)
   lowercase_	:    int           =     DataLoader(_UpperCamelCase						,		batch_size=16							)
   lowercase_    ,		lowercase_	:    Union[str, 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 		__SCREAMING_SNAKE_CASE					(  ):


   """simple docstring"""




   lowercase_	:    Union[str, Any]           =     Accelerator()
   lowercase_	:    Tuple           =     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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   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


UpperCamelCase__ =			['text', 'image', 'audio']




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     []

   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(3000							)							)
      elif isinstance(_UpperCamelCase						,		_UpperCamelCase							):
         inputs.append(create_inputs(_UpperCamelCase							)							)
      else:
         raise ValueError(F"""Invalid type requested: {input_type}"""							)

   return inputs




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    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        _UpperCAmelCase						:




    def 				lowerCAmelCase__				(			self  :       List[Any]			):






       '''simple docstring'''






       self.assertTrue(hasattr(self.tool  ,     "inputs"			)			)
       self.assertTrue(hasattr(self.tool  ,     "outputs"			)			)

       lowercase_	:    Optional[Any]           =     self.tool.inputs
       for _input in inputs:
          if isinstance(_input  ,     a			):
             for __input in _input:
                self.assertTrue(__input in authorized_types			)
          else:
             self.assertTrue(_input in authorized_types			)

       lowercase_	:    Any           =     self.tool.outputs
       for _output in outputs:
          self.assertTrue(_output in authorized_types			)




    def 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    List[str]           =     create_inputs(self.tool.inputs			)
       lowercase_	:    List[str]           =     self.tool(*a			)

       # There is a single output
       if len(self.tool.outputs			) == 1:
          lowercase_	:    Union[str, Any]           =     [outputs]

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




    def 				lowerCAmelCase__				(			self  :       List[str]			):






       '''simple docstring'''






       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 				lowerCAmelCase__				(			self  :       Any			):






       '''simple docstring'''






       lowercase_	:    Any           =     create_inputs(self.tool.inputs			)
       lowercase_	:    str           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    List[Any]           =     [outputs]

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

       for output, output_type in zip(a  ,     self.tool.outputs			):
          lowercase_	:    int           =     AGENT_TYPE_MAPPING[output_type]
          self.assertTrue(isinstance(a  ,     a			)			)





    def 				lowerCAmelCase__				(			self  :       Tuple			):






       '''simple docstring'''






       lowercase_	:    Dict           =     create_inputs(self.tool.inputs			)

       lowercase_	:    Optional[int]           =     []

       for _input, input_type in zip(a  ,     self.tool.inputs			):
          if isinstance(a  ,     a			):
             _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
       lowercase_	:    Any           =     self.tool(*a			)

       if not isinstance(a  ,     a			):
          lowercase_	:    Any           =     [outputs]

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




