"""simple docstring"""


import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


UpperCAmelCase__			=    logging.getLogger(__name__)






def      __UpperCAmelCase       (      lowercase=2 ,lowercase=3 ,lowercase=16 ,lowercase = 10 ,lowercase = 2		):







  """simple docstring"""





  def get_dataset(lowercase		):
    _UpperCAmelCase    			=   torch.randn(batch_size * n_batches ,1		)
    return TensorDataset(UpperCAmelCase_ ,a * x + b + 0.1 * torch.randn(batch_size * n_batches ,1		)		)

  _UpperCAmelCase    			=   get_dataset(UpperCAmelCase_		)
  _UpperCAmelCase    			=   get_dataset(UpperCAmelCase_		)
  _UpperCAmelCase    			=   DataLoader(UpperCAmelCase_ ,shuffle=UpperCAmelCase_ ,batch_size=UpperCAmelCase_ ,num_workers=4		)
  _UpperCAmelCase    			=   DataLoader(UpperCAmelCase_ ,shuffle=UpperCAmelCase_ ,batch_size=UpperCAmelCase_ ,num_workers=4		)
  return (train_dataloader, valid_dataloader)






def      __UpperCAmelCase       (      lowercase ,lowercase ,lowercase ,lowercase ,lowercase ,lowercase=None		):







  """simple docstring"""
  _UpperCAmelCase    			=   []
  for epoch in range(UpperCAmelCase_		):
    # Train quickly
    model.train()
    for batch in dataloader:
      _UpperCAmelCase			,					_UpperCAmelCase    			=   batch
      _UpperCAmelCase    			=   model(UpperCAmelCase_		)
      _UpperCAmelCase    			=   torch.nn.functional.mse_loss(UpperCAmelCase_ ,UpperCAmelCase_		)
      accelerator.backward(UpperCAmelCase_		)
      optimizer.step()
      optimizer.zero_grad()
    rands.append(random.random()		)  # Introduce some randomness
    if scheduler is not None:
      scheduler.step()
  return rands


class 							a					(		nn.Module ):


    def __init__(       self						:	List[Any]       ):
      super().__init__()
      _UpperCAmelCase    			=   nn.Parameter(torch.randn(1       )       )
      _UpperCAmelCase    			=   nn.Parameter(torch.randn(1       )       )


    def   lowerCAmelCase_	(       self						:	Dict ,					__lowerCAmelCase						:	Dict       ):
      return x * self.a + self.b



class 							a					(		unittest.TestCase ):


    def   lowerCAmelCase_	(       self						:	int       ):
      with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(42       )
        _UpperCAmelCase    			=   DummyModel()
        _UpperCAmelCase    			=   torch.optim.Adam(params=model.parameters() ,					lr=1e-3       )
        _UpperCAmelCase			,					_UpperCAmelCase    			=   dummy_dataloaders()
        _UpperCAmelCase    			=   ProjectConfiguration(total_limit=1 ,					project_dir=SCREAMING_SNAKE_CASE__ ,					automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__       )
        # Train baseline
        _UpperCAmelCase    			=   Accelerator(project_config=SCREAMING_SNAKE_CASE__       )
        _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   accelerator.prepare(
            SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        # Save initial
        accelerator.save_state()

        # Save second state
        accelerator.save_state()
        self.assertEqual(len(os.listdir(accelerator.project_dir       )       ) ,					1       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(42       )
        _UpperCAmelCase    			=   DummyModel()
        _UpperCAmelCase    			=   torch.optim.Adam(params=model.parameters() ,					lr=1e-3       )
        _UpperCAmelCase			,					_UpperCAmelCase    			=   dummy_dataloaders()
        # Train baseline
        _UpperCAmelCase    			=   Accelerator()
        _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   accelerator.prepare(
            SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        # Save initial
        _UpperCAmelCase    			=   os.path.join(SCREAMING_SNAKE_CASE__ ,					"""initial"""       )
        accelerator.save_state(SCREAMING_SNAKE_CASE__       )
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()
        _UpperCAmelCase    			=   train(3 ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()

        # Train partially
        set_seed(42       )
        _UpperCAmelCase    			=   DummyModel()
        _UpperCAmelCase    			=   torch.optim.Adam(params=model.parameters() ,					lr=1e-3       )
        _UpperCAmelCase			,					_UpperCAmelCase    			=   dummy_dataloaders()
        _UpperCAmelCase    			=   Accelerator()
        _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   accelerator.prepare(
            SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        accelerator.load_state(SCREAMING_SNAKE_CASE__       )
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )

        _UpperCAmelCase    			=   train(2 ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        # Save everything
        _UpperCAmelCase    			=   os.path.join(SCREAMING_SNAKE_CASE__ ,					"""checkpoint"""       )
        accelerator.save_state(SCREAMING_SNAKE_CASE__       )

        # Load everything back in and make sure all states work
        accelerator.load_state(SCREAMING_SNAKE_CASE__       )
        test_rands += train(1 ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )


    def   lowerCAmelCase_	(       self						:	List[Any]       ):
      with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(42       )
        _UpperCAmelCase    			=   DummyModel()
        _UpperCAmelCase    			=   torch.optim.Adam(params=model.parameters() ,					lr=1e-3       )
        _UpperCAmelCase			,					_UpperCAmelCase    			=   dummy_dataloaders()
        _UpperCAmelCase    			=   ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__       )

        # Train baseline
        _UpperCAmelCase    			=   Accelerator(project_dir=SCREAMING_SNAKE_CASE__ ,					project_config=SCREAMING_SNAKE_CASE__       )
        _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   accelerator.prepare(
            SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        # Save initial
        accelerator.save_state()
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()
        _UpperCAmelCase    			=   train(3 ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()

        # Train partially
        set_seed(42       )
        _UpperCAmelCase    			=   DummyModel()
        _UpperCAmelCase    			=   torch.optim.Adam(params=model.parameters() ,					lr=1e-3       )
        _UpperCAmelCase			,					_UpperCAmelCase    			=   dummy_dataloaders()
        _UpperCAmelCase    			=   ProjectConfiguration(iteration=1 ,					automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__       )
        _UpperCAmelCase    			=   Accelerator(project_dir=SCREAMING_SNAKE_CASE__ ,					project_config=SCREAMING_SNAKE_CASE__       )
        _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   accelerator.prepare(
            SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE__ ,					"""checkpoints""" ,					"""checkpoint_0"""       )       )
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )

        _UpperCAmelCase    			=   train(2 ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        # Save everything
        accelerator.save_state()

        # Load everything back in and make sure all states work
        accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE__ ,					"""checkpoints""" ,					"""checkpoint_1"""       )       )
        test_rands += train(1 ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        ((_UpperCAmelCase)			,					(_UpperCAmelCase))    			=   model.a.item(), model.b.item()
        _UpperCAmelCase    			=   optimizer.state_dict()
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )


    def   lowerCAmelCase_	(       self						:	Dict       ):
      _UpperCAmelCase    			=   torch.tensor([1, 2, 3]       )
      _UpperCAmelCase    			=   torch.tensor([2, 3, 4]       )
      _UpperCAmelCase    			=   DummyModel()
      _UpperCAmelCase    			=   torch.optim.Adam(net.parameters()       )
      _UpperCAmelCase    			=   Accelerator()
      with self.assertRaises(SCREAMING_SNAKE_CASE__       ) as ve:
        accelerator.register_for_checkpointing(SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
      _UpperCAmelCase    			=   str(ve.exception       )
      self.assertTrue("""Item at index 0""" in message       )
      self.assertTrue("""Item at index 1""" in message       )
      self.assertFalse("""Item at index 2""" in message       )
      self.assertFalse("""Item at index 3""" in message       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(42       )
        _UpperCAmelCase    			=   DummyModel()
        _UpperCAmelCase    			=   torch.optim.Adam(params=model.parameters() ,					lr=1e-3       )
        _UpperCAmelCase    			=   torch.optim.lr_scheduler.StepLR(SCREAMING_SNAKE_CASE__ ,					step_size=1 ,					gamma=0.99       )
        _UpperCAmelCase			,					_UpperCAmelCase    			=   dummy_dataloaders()
        _UpperCAmelCase    			=   ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__       )
        # Train baseline
        _UpperCAmelCase    			=   Accelerator(project_dir=SCREAMING_SNAKE_CASE__ ,					project_config=SCREAMING_SNAKE_CASE__       )
        _UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase			,					_UpperCAmelCase    			=   accelerator.prepare(
            SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        # Save initial
        accelerator.save_state()
        _UpperCAmelCase    			=   scheduler.state_dict()
        train(3 ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__ ,					SCREAMING_SNAKE_CASE__       )
        self.assertNotEqual(SCREAMING_SNAKE_CASE__ ,					scheduler.state_dict()       )

        # Load everything back in and make sure all states work
        accelerator.load_state(os.path.join(SCREAMING_SNAKE_CASE__ ,					"""checkpoints""" ,					"""checkpoint_0"""       )       )
        self.assertEqual(SCREAMING_SNAKE_CASE__ ,					scheduler.state_dict()       )


    def   lowerCAmelCase_	(       self						:	Union[str, Any]       ):
      with tempfile.TemporaryDirectory() as tmpdir:
        set_seed(42       )
        _UpperCAmelCase    			=   DummyModel()
        _UpperCAmelCase    			=   ProjectConfiguration(automatic_checkpoint_naming=SCREAMING_SNAKE_CASE__ ,					total_limit=2       )
        # Train baseline
        _UpperCAmelCase    			=   Accelerator(project_dir=SCREAMING_SNAKE_CASE__ ,					project_config=SCREAMING_SNAKE_CASE__       )
        _UpperCAmelCase    			=   accelerator.prepare(SCREAMING_SNAKE_CASE__       )
        # Save 3 states:
        for _ in range(11       ):
          accelerator.save_state()
        self.assertTrue(not os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__ ,					"""checkpoints""" ,					"""checkpoint_0"""       )       )       )
        self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__ ,					"""checkpoints""" ,					"""checkpoint_9"""       )       )       )
        self.assertTrue(os.path.exists(os.path.join(SCREAMING_SNAKE_CASE__ ,					"""checkpoints""" ,					"""checkpoint_10"""       )       )       )


    @require_cuda
    def   lowerCAmelCase_	(       self						:	int       ):
      _UpperCAmelCase    			=   ["""torchrun""", f'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__       )]
      execute_subprocess_async(SCREAMING_SNAKE_CASE__ ,					env=os.environ.copy()       )


if __name__ == "__main__":
    UpperCAmelCase__			=    """/tmp/accelerate/state_checkpointing"""
    UpperCAmelCase__			=    DummyModel()
    UpperCAmelCase__			=    torch.optim.Adam(params=model.parameters(), lr=1E-3)
    UpperCAmelCase__			=    torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.99)
    UpperCAmelCase__		, UpperCAmelCase__			=    dummy_dataloaders()
    UpperCAmelCase__			=    ProjectConfiguration(automatic_checkpoint_naming=True)
    # Train baseline
    UpperCAmelCase__			=    Accelerator(project_dir=savedir, project_config=project_config, mixed_precision="""no""")
    if accelerator.process_index == 0:
        if os.path.exists(savedir):
            shutil.rmtree(savedir)
        os.makedirs(savedir)
    UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__		, UpperCAmelCase__			=    accelerator.prepare(
        model, optimizer, train_dataloader, valid_dataloader, scheduler
    )
    UpperCAmelCase__		, UpperCAmelCase__			=    accelerator.prepare(model, optimizer)
    train(3, model, train_dataloader, optimizer, accelerator, scheduler)
    # Check that the intial optimizer is loaded on the GPU
    for group in optimizer.param_groups:
        UpperCAmelCase__			=    group["""params"""][0].device
        break
    assert param_device.type == accelerator.device.type
    UpperCAmelCase__			=    model.cpu()
    accelerator.wait_for_everyone()
    accelerator.save_state()
    accelerator.wait_for_everyone()

    # Check CPU state
    accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""cpu""")
    for group in optimizer.param_groups:
        UpperCAmelCase__			=    group["""params"""][0].device
        break
    assert (
        param_device.type == torch.device("""cpu""").type
    ), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

    # Check device state
    model.to(accelerator.device)
    accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""on_device""")
    for group in optimizer.param_groups:
        UpperCAmelCase__			=    group["""params"""][0].device
        break
    assert (
        param_device.type == accelerator.device.type
    ), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

    # Check error
    with pytest.raises(TypeError, match="""Unsupported optimizer map location passed"""):
        accelerator.load_state(os.path.join(savedir, """checkpoints""", """checkpoint_0"""), map_location="""invalid""")
    accelerator.wait_for_everyone()
    if accelerator.process_index == 0:
        shutil.rmtree(savedir)
    accelerator.wait_for_everyone()





"""simple docstring"""







import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AlignProcessor, EfficientNetImageProcessor






@require_vision
class       UpperCamelCase__			(   unittest.TestCase      ):

			"""simple docstring"""


			def   snake_case__	(      self		)      ->	Any:
				A__											=					tempfile.mkdtemp()

				A__											=					[
				    "[UNK]",
				    "[CLS]",
				    "[SEP]",
				    "[PAD]",
				    "[MASK]",
				    "want",
				    "##want",
				    "##ed",
				    "wa",
				    "un",
				    "runn",
				    "##ing",
				    ",",
				    "low",
				    "lowest",
				]
				A__											=					os.path.join(self.tmpdirname						,	VOCAB_FILES_NAMES["vocab_file"]		)
				with open(self.vocab_file						,	"w"						,	encoding="utf-8"		) as vocab_writer:
					vocab_writer.write("".join([x + "\n" for x in vocab_tokens]		)		)

				A__											=					{
				    "do_resize": True,
				    "size": 20,
				    "do_center_crop": True,
				    "crop_size": 18,
				    "do_normalize": True,
				    "image_mean": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
				    "image_std": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
				}
				A__											=					os.path.join(self.tmpdirname						,	SCREAMING_SNAKE_CASE__		)
				with open(self.image_processor_file						,	"w"						,	encoding="utf-8"		) as fp:
					json.dump(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	List[Any]:
				return BertTokenizer.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	Tuple:
				return BertTokenizerFast.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self						,	**SCREAMING_SNAKE_CASE__		)      ->	str:
				return EfficientNetImageProcessor.from_pretrained(self.tmpdirname						,	**SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[str]:
				shutil.rmtree(self.tmpdirname		)


			def   snake_case__	(      self		)      ->	Any:
				A__											=					[np.random.randint(255						,	size=(3, 30, 400)						,	dtype=np.uinta		)]
				A__											=					[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__						,	0						,	-1		)		) for x in image_inputs]
				return image_inputs


			def   snake_case__	(      self		)      ->	Dict:
				A__											=					self.get_tokenizer()
				A__											=					self.get_rust_tokenizer()
				A__											=					self.get_image_processor()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_slow.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.from_pretrained(self.tmpdirname						,	use_fast=SCREAMING_SNAKE_CASE__		)

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)
				processor_fast.save_pretrained(self.tmpdirname		)
				A__											=					AlignProcessor.from_pretrained(self.tmpdirname		)

				self.assertEqual(processor_slow.tokenizer.get_vocab()						,	tokenizer_slow.get_vocab()		)
				self.assertEqual(processor_fast.tokenizer.get_vocab()						,	tokenizer_fast.get_vocab()		)
				self.assertEqual(tokenizer_slow.get_vocab()						,	tokenizer_fast.get_vocab()		)
				self.assertIsInstance(processor_slow.tokenizer						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.tokenizer						,	SCREAMING_SNAKE_CASE__		)

				self.assertEqual(processor_slow.image_processor.to_json_string()						,	image_processor.to_json_string()		)
				self.assertEqual(processor_fast.image_processor.to_json_string()						,	image_processor.to_json_string()		)
				self.assertIsInstance(processor_slow.image_processor						,	SCREAMING_SNAKE_CASE__		)
				self.assertIsInstance(processor_fast.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					AlignProcessor(tokenizer=self.get_tokenizer()						,	image_processor=self.get_image_processor()		)
				processor.save_pretrained(self.tmpdirname		)

				A__											=					self.get_tokenizer(bos_token="(BOS)"						,	eos_token="(EOS)"		)
				A__											=					self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

				A__											=					AlignProcessor.from_pretrained(
				    self.tmpdirname						,	bos_token="(BOS)"						,	eos_token="(EOS)"						,	do_normalize=SCREAMING_SNAKE_CASE__						,	padding_value=1.0		)

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

				self.assertEqual(processor.image_processor.to_json_string()						,	image_processor_add_kwargs.to_json_string()		)
				self.assertIsInstance(processor.image_processor						,	SCREAMING_SNAKE_CASE__		)


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					self.prepare_image_inputs()

				A__											=					image_processor(SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)
				A__											=					processor(images=SCREAMING_SNAKE_CASE__						,	return_tensors="np"		)

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


			def   snake_case__	(      self		)      ->	List[Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"

				A__											=					processor(text=SCREAMING_SNAKE_CASE__		)

				A__											=					tokenizer(SCREAMING_SNAKE_CASE__						,	padding="max_length"						,	max_length=64		)

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


			def   snake_case__	(      self		)      ->	int:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

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

				# test if it raises when no input is passed
				with pytest.raises(SCREAMING_SNAKE_CASE__		):
					processor()


			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				A__											=					processor.batch_decode(SCREAMING_SNAKE_CASE__		)
				A__											=					tokenizer.batch_decode(SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__		)







			def   snake_case__	(      self		)      ->	Union[str, Any]:
				A__											=					self.get_image_processor()
				A__											=					self.get_tokenizer()

				A__											=					AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE__						,	image_processor=SCREAMING_SNAKE_CASE__		)

				A__											=					"lower newer"
				A__											=					self.prepare_image_inputs()

				A__											=					processor(text=SCREAMING_SNAKE_CASE__						,	images=SCREAMING_SNAKE_CASE__		)

				self.assertListEqual(list(inputs.keys()		)						,	processor.model_input_names		)




