



'''simple docstring'''







import unittest
from functools import partial

import torch

from accelerate import Accelerator, debug_launcher
from accelerate.state import AcceleratorState, GradientState
from accelerate.test_utils import require_cpu, require_huggingface_suite
from accelerate.utils import GradientAccumulationPlugin





def 						snake_case__ (lowerCAmelCase_=2					,  lowerCAmelCase_=True					,  lowerCAmelCase_=False	):
    UpperCamelCase__      =			Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_					,  split_batches=lowerCAmelCase_	)
    UpperCamelCase__      =			torch.nn.Linear(2					,  4	)
    UpperCamelCase__      =			torch.optim.AdamW(model.parameters()					,  lr=1.0	)
    UpperCamelCase__      =			torch.optim.lr_scheduler.OneCycleLR(lowerCAmelCase_					,  max_lr=0.01					,  steps_per_epoch=2					,  epochs=1	)
    UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			accelerator.prepare(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	)

    # Optimizer has stepped
    scheduler.step()
    if step_scheduler_with_optimizer or (num_processes == 1):
        assert (
            scheduler.scheduler.last_epoch == num_processes
        ), F'Last Epoch ({scheduler.scheduler.last_epoch}) != Num Processes ({num_processes})'
    else:
        assert (
            scheduler.scheduler.last_epoch != num_processes
        ), F'Last Epoch ({scheduler.scheduler.last_epoch}) == Num Processes ({num_processes})'





def 						snake_case__ (lowerCAmelCase_=2					,  lowerCAmelCase_=True					,  lowerCAmelCase_=False	):
    UpperCamelCase__      =			Accelerator(step_scheduler_with_optimizer=lowerCAmelCase_					,  split_batches=lowerCAmelCase_	)
    UpperCamelCase__      =			torch.nn.Linear(2					,  4	)
    UpperCamelCase__      =			torch.optim.AdamW(model.parameters()					,  lr=1.0	)
    UpperCamelCase__      =			torch.optim.lr_scheduler.LambdaLR(lowerCAmelCase_					,  lr_lambda=lambda lowerCAmelCase_	: 1 - n / 10	)
    UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			accelerator.prepare(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	)

    # Optimizer has stepped
    UpperCamelCase__      =			False
    scheduler.step()
    UpperCamelCase__      =			1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F'Wrong lr found at first step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'

    # Optimizer has not stepped
    UpperCamelCase__      =			True
    scheduler.step()
    if not step_scheduler_with_optimizer:
        UpperCamelCase__      =			1 - 2 / 10
    assert (
        scheduler.get_last_lr()[0] == expected_lr
    ), F'Wrong lr found at second step, expected {expected_lr}, got {scheduler.get_last_lr()[0]}'





def 						snake_case__ (lowerCAmelCase_ = 2	):
    from transformers import get_linear_schedule_with_warmup

    UpperCamelCase__      =			[1, 2, 4]
    for num_steps in steps:
        UpperCamelCase__      =			GradientAccumulationPlugin(num_steps=lowerCAmelCase_					,  adjust_scheduler=num_steps > 1	)
        UpperCamelCase__      =			Accelerator(gradient_accumulation_plugin=lowerCAmelCase_	)
        UpperCamelCase__      =			torch.nn.Linear(2					,  4	)
        UpperCamelCase__      =			torch.optim.AdamW(model.parameters()					,  lr=10.0	)
        UpperCamelCase__      =			get_linear_schedule_with_warmup(optimizer=lowerCAmelCase_					,  num_warmup_steps=0					,  num_training_steps=20	)

        UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__      =			accelerator.prepare(lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_	)

        for i in range(10 * num_steps	):
            with accelerator.accumulate(lowerCAmelCase_	):
                optimizer.step()
                scheduler.step()

            if i == (10 * num_steps - 2):
                assert (
                    scheduler.get_last_lr()[0] != 0
                ), F'Wrong lr found at second-to-last step, expected non-zero, got {scheduler.get_last_lr()[0]}. num_steps: {num_steps}'
        assert (
            scheduler.get_last_lr()[0] == 0
        ), F'Wrong lr found at last step, expected 0, got {scheduler.get_last_lr()[0]}'
        GradientState._reset_state()





@require_cpu
class       lowerCamelCase_							(	unittest.TestCase  ):






   '''simple docstring'''

   def 	_UpperCamelCase       (  self ):
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1 )     ,						num_processes=1 )
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1     ,						split_batches=__UpperCamelCase )     ,						num_processes=1 )


   def 	_UpperCamelCase       (  self ):
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1 )     ,						num_processes=1 )
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1     ,						split_batches=__UpperCamelCase )     ,						num_processes=1 )


   def 	_UpperCamelCase       (  self ):
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1     ,						step_scheduler_with_optimizer=__UpperCamelCase )     ,						num_processes=1 )


   def 	_UpperCamelCase       (  self ):
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1     ,						step_scheduler_with_optimizer=__UpperCamelCase )     ,						num_processes=1 )


   def 	_UpperCamelCase       (  self ):
       AcceleratorState._reset_state(__UpperCamelCase )
       debug_launcher(__UpperCamelCase )
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1     ,						split_batches=__UpperCamelCase )     ,						num_processes=1 )


   def 	_UpperCamelCase       (  self ):
       AcceleratorState._reset_state(__UpperCamelCase )
       debug_launcher(__UpperCamelCase )
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1     ,						split_batches=__UpperCamelCase )     ,						num_processes=1 )


   def 	_UpperCamelCase       (  self ):
       AcceleratorState._reset_state(__UpperCamelCase )
       debug_launcher(partial(__UpperCamelCase     ,						step_scheduler_with_optimizer=__UpperCamelCase ) )


   def 	_UpperCamelCase       (  self ):
       AcceleratorState._reset_state(__UpperCamelCase )
       debug_launcher(partial(__UpperCamelCase     ,						step_scheduler_with_optimizer=__UpperCamelCase ) )







   @require_huggingface_suite
   def 	_UpperCamelCase       (  self ):
       AcceleratorState._reset_state(__UpperCamelCase )
       debug_launcher(partial(__UpperCamelCase     ,						num_processes=1 ) )
       debug_launcher(__UpperCamelCase )









'''simple docstring'''







import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types

class       lowerCamelCase_							(	unittest.TestCase					,    lowerCAmelCase_  ):






   '''simple docstring'''

   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			load_tool("translation" )
       self.tool.setup()
       UpperCamelCase__      =			load_tool("translation"     ,						remote=__UpperCamelCase )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool("Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			self.remote_tool(text="Hey, what's up?"     ,						src_lang="English"     ,						tgt_lang="French" )
       self.assertEqual(__UpperCamelCase     ,						"- Hé, comment ça va?" )


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

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

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


   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

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

       for output, output_type in zip(__UpperCamelCase     ,						self.tool.outputs ):
           UpperCamelCase__      =			AGENT_TYPE_MAPPING[output_type]
           self.assertTrue(isinstance(__UpperCamelCase     ,						__UpperCamelCase ) )







   def 	_UpperCamelCase       (  self ):
       UpperCamelCase__      =			["Hey, what's up?", "English", "Spanish"]

       UpperCamelCase__      =			[]

       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
       UpperCamelCase__      =			self.tool(*__UpperCamelCase )

       if not isinstance(__UpperCamelCase     ,						__UpperCamelCase ):
           UpperCamelCase__      =			[outputs]

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




