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       a__      (					a=2  ,   a=True  ,   a=False							)		->	Any:
       A_							:       Dict	      =				Accelerator(step_scheduler_with_optimizer=a  ,   split_batches=a							)
       A_							:       List[str]	      =				torch.nn.Linear(2  ,   4							)
       A_							:       Optional[int]	      =				torch.optim.AdamW(model.parameters()  ,   lr=1.0							)
       A_							:       str	      =				torch.optim.lr_scheduler.OneCycleLR(a  ,   max_lr=0.0_1  ,   steps_per_epoch=2  ,   epochs=1							)
       A_      ,							A_      ,							A_							:       Any	      =				accelerator.prepare(a  ,   a  ,   a							)

       # 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       a__      (					a=2  ,   a=True  ,   a=False							)		->	Optional[int]:
       A_							:       List[Any]	      =				Accelerator(step_scheduler_with_optimizer=a  ,   split_batches=a							)
       A_							:       Optional[int]	      =				torch.nn.Linear(2  ,   4							)
       A_							:       List[str]	      =				torch.optim.AdamW(model.parameters()  ,   lr=1.0							)
       A_							:       Optional[int]	      =				torch.optim.lr_scheduler.LambdaLR(a  ,   lr_lambda=lambda a							: 1 - n / 1_0							)
       A_      ,							A_      ,							A_							:       str	      =				accelerator.prepare(a  ,   a  ,   a							)

       # Optimizer has stepped
       A_							:       Optional[int]	      =				False
       scheduler.step()
       A_							:       Optional[int]	      =				1 - (num_processes if (step_scheduler_with_optimizer and not split_batches) else 1) / 1_0
       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
       A_							:       Tuple	      =				True
       scheduler.step()
       if not step_scheduler_with_optimizer:
              A_							:       Optional[Any]	      =				1 - 2 / 1_0
       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       a__      (					a = 2							)		->	List[Any]:
       from transformers import get_linear_schedule_with_warmup

       A_							:       int	      =				[1, 2, 4]
       for num_steps in steps:
              A_							:       str	      =				GradientAccumulationPlugin(num_steps=a  ,   adjust_scheduler=num_steps > 1							)
              A_							:       Any	      =				Accelerator(gradient_accumulation_plugin=a							)
              A_							:       int	      =				torch.nn.Linear(2  ,   4							)
              A_							:       Optional[int]	      =				torch.optim.AdamW(model.parameters()  ,   lr=1_0.0							)
              A_							:       str	      =				get_linear_schedule_with_warmup(optimizer=a  ,   num_warmup_steps=0  ,   num_training_steps=2_0							)

              A_      ,							A_      ,							A_							:       List[Any]	      =				accelerator.prepare(a  ,   a  ,   a							)

              for i in range(1_0 * num_steps							):
                     with accelerator.accumulate(a							):
                            optimizer.step()
                            scheduler.step()

                     if i == (1_0 * 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     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        debug_launcher(partial(__magic_name__ ,       num_processes=1      ) ,       num_processes=1      )
        debug_launcher(partial(__magic_name__ ,       num_processes=1 ,       split_batches=__magic_name__      ) ,       num_processes=1      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        debug_launcher(partial(__magic_name__ ,       num_processes=1      ) ,       num_processes=1      )
        debug_launcher(partial(__magic_name__ ,       num_processes=1 ,       split_batches=__magic_name__      ) ,       num_processes=1      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        debug_launcher(partial(__magic_name__ ,       num_processes=1 ,       step_scheduler_with_optimizer=__magic_name__      ) ,       num_processes=1      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        debug_launcher(partial(__magic_name__ ,       num_processes=1 ,       step_scheduler_with_optimizer=__magic_name__      ) ,       num_processes=1      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        AcceleratorState._reset_state(__magic_name__      )
        debug_launcher(__magic_name__      )
        debug_launcher(partial(__magic_name__ ,       num_processes=1 ,       split_batches=__magic_name__      ) ,       num_processes=1      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        AcceleratorState._reset_state(__magic_name__      )
        debug_launcher(__magic_name__      )
        debug_launcher(partial(__magic_name__ ,       num_processes=1 ,       split_batches=__magic_name__      ) ,       num_processes=1      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        AcceleratorState._reset_state(__magic_name__      )
        debug_launcher(partial(__magic_name__ ,       step_scheduler_with_optimizer=__magic_name__      )      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        AcceleratorState._reset_state(__magic_name__      )
        debug_launcher(partial(__magic_name__ ,       step_scheduler_with_optimizer=__magic_name__      )      )
 @require_huggingface_suite
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        AcceleratorState._reset_state(__magic_name__      )
        debug_launcher(partial(__magic_name__ ,       num_processes=1      )      )
        debug_launcher(__magic_name__      )



import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
    from PIL import Image

    from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class     __UpperCAmelCase(    unittest.TestCase				):






 """simple docstring"""




 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				tempfile.mkdtemp()

        A_							:       str	      =				CLIPImageProcessor()
        A_							:       List[Any]	      =				BertTokenizer.from_pretrained(
            '''hf-internal-testing/tiny-random-BertModel''' ,       model_input_names=['''input_ids''', '''attention_mask''']      )

        A_							:       Optional[int]	      =				GitProcessor(__magic_name__ ,       __magic_name__      )

        processor.save_pretrained(self.tmpdirname      )
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).tokenizer
 def  UpperCAmelCase   (    self ,       **__magic_name__      ):

        """simple docstring"""

        return AutoProcessor.from_pretrained(self.tmpdirname ,       **__magic_name__      ).image_processor
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        shutil.rmtree(self.tmpdirname      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Union[str, Any]	      =				[np.random.randint(255 ,       size=(3, 30, 400) ,       dtype=np.uinta      )]

        A_							:       Union[str, Any]	      =				[Image.fromarray(np.moveaxis(__magic_name__ ,       0 ,       -1      )      ) for x in image_inputs]

        return image_inputs
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Any	      =				GitProcessor(tokenizer=self.get_tokenizer() ,       image_processor=self.get_image_processor()      )
        processor.save_pretrained(self.tmpdirname      )

        A_							:       int	      =				self.get_tokenizer(bos_token='''(BOS)''' ,       eos_token='''(EOS)'''      )
        A_							:       str	      =				self.get_image_processor(do_normalize=__magic_name__ ,       padding_value=1.0      )

        A_							:       int	      =				GitProcessor.from_pretrained(
            self.tmpdirname ,       bos_token='''(BOS)''' ,       eos_token='''(EOS)''' ,       do_normalize=__magic_name__ ,       padding_value=1.0      )

        self.assertEqual(processor.tokenizer.get_vocab() ,       tokenizer_add_kwargs.get_vocab()      )
        self.assertIsInstance(processor.tokenizer ,       __magic_name__      )

        self.assertEqual(processor.image_processor.to_json_string() ,       image_processor_add_kwargs.to_json_string()      )
        self.assertIsInstance(processor.image_processor ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       str	      =				self.prepare_image_inputs()

        A_							:       List[str]	      =				image_processor(__magic_name__ ,       return_tensors='''np'''      )
        A_							:       int	      =				processor(images=__magic_name__ ,       return_tensors='''np'''      )

        for key in input_feat_extract.keys():
               self.assertAlmostEqual(input_feat_extract[key].sum() ,       input_processor[key].sum() ,       delta=1e-2      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       str	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''

        A_							:       Tuple	      =				processor(text=__magic_name__      )

        A_							:       List[Any]	      =				tokenizer(__magic_name__ ,       return_token_type_ids=__magic_name__      )

        for key in encoded_tok.keys():
               self.assertListEqual(encoded_tok[key] ,       encoded_processor[key]      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       List[str]	      =				self.get_image_processor()
        A_							:       Tuple	      =				self.get_tokenizer()

        A_							:       List[str]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				'''lower newer'''
        A_							:       Dict	      =				self.prepare_image_inputs()

        A_							:       Union[str, Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )

        # test if it raises when no input is passed
        with pytest.raises(__magic_name__      ):
               processor()
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       Optional[Any]	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       Optional[int]	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       List[Any]	      =				[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        A_							:       List[Any]	      =				processor.batch_decode(__magic_name__      )
        A_							:       Dict	      =				tokenizer.batch_decode(__magic_name__      )

        self.assertListEqual(__magic_name__ ,       __magic_name__      )
 def  UpperCAmelCase   (    self      ):

        """simple docstring"""

        A_							:       int	      =				self.get_image_processor()
        A_							:       Optional[int]	      =				self.get_tokenizer()

        A_							:       str	      =				GitProcessor(tokenizer=__magic_name__ ,       image_processor=__magic_name__      )

        A_							:       Tuple	      =				'''lower newer'''
        A_							:       List[str]	      =				self.prepare_image_inputs()

        A_							:       Optional[Any]	      =				processor(text=__magic_name__ ,       images=__magic_name__      )

        # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
        self.assertListEqual(list(inputs.keys()      ) ,       ['''input_ids''', '''attention_mask''', '''pixel_values''']      )


