
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


lowercase  :					Tuple						    =							logging.getLogger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__=2					,	SCREAMING_SNAKE_CASE__=3					,	SCREAMING_SNAKE_CASE__=16					,	SCREAMING_SNAKE_CASE__ = 10					,	SCREAMING_SNAKE_CASE__ = 2				) ->    Union[str, Any]:

       def get_dataset(SCREAMING_SNAKE_CASE__				):
              lowercase							:     Union[str, Any]       	=      torch.randn(batch_size * n_batches					,	1				)
              return TensorDataset(SCREAMING_SNAKE_CASE__					,	a * x + b + 0.1 * torch.randn(batch_size * n_batches					,	1				)				)

       lowercase							:     Dict       	=      get_dataset(SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      get_dataset(SCREAMING_SNAKE_CASE__				)
       lowercase							:     List[str]       	=      DataLoader(SCREAMING_SNAKE_CASE__					,	shuffle=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_SNAKE_CASE__					,	num_workers=4				)
       lowercase							:     str       	=      DataLoader(SCREAMING_SNAKE_CASE__					,	shuffle=SCREAMING_SNAKE_CASE__					,	batch_size=SCREAMING_SNAKE_CASE__					,	num_workers=4				)
       return (train_dataloader, valid_dataloader)







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=None				) ->    int:
       lowercase							:     Dict       	=      []
       for epoch in range(SCREAMING_SNAKE_CASE__				):
              # Train quickly
              model.train()
              for batch in dataloader:
                     lowercase	,			lowercase							:     Tuple       	=      batch
                     lowercase							:     str       	=      model(SCREAMING_SNAKE_CASE__				)
                     lowercase							:     int       	=      torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
                     accelerator.backward(SCREAMING_SNAKE_CASE__				)
                     optimizer.step()
                     optimizer.zero_grad()
              rands.append(random.random()				)  # Introduce some randomness
              if scheduler is not None:
                     scheduler.step()
       return rands

class __snake_case		(      nn.Module     ):
    def __init__(  self      ):



           '''simple docstring'''







           super().__init__()
           lowercase							:     Optional[int]       	=      nn.Parameter(torch.randn(1      )      )
           lowercase							:     List[str]       	=      nn.Parameter(torch.randn(1      )      )
    def       _SCREAMING_SNAKE_CASE       (  self		,snake_case      ):



           '''simple docstring'''







           return x * self.a + self.b






class __snake_case		(      unittest.TestCase     ):
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42      )
                  lowercase							:     Any       	=      DummyModel()
                  lowercase							:     List[Any]       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     int       	=      dummy_dataloaders()
                  lowercase							:     List[str]       	=      ProjectConfiguration(total_limit=1		,project_dir=snake_case		,automatic_checkpoint_naming=snake_case      )
                  # Train baseline
                  lowercase							:     Tuple       	=      Accelerator(project_config=snake_case      )
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save initial
                  accelerator.save_state()

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



           '''simple docstring'''







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

                  # Train partially
                  set_seed(42      )
                  lowercase							:     Tuple       	=      DummyModel()
                  lowercase							:     Dict       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     Union[str, Any]       	=      dummy_dataloaders()
                  lowercase							:     Union[str, Any]       	=      Accelerator()
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     int       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  accelerator.load_state(snake_case      )
                  ((lowercase)	,			(lowercase))							:     int       	=      model.a.item(), model.b.item()
                  lowercase							:     Optional[Any]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )

                  lowercase							:     Any       	=      train(2		,snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save everything
                  lowercase							:     List[Any]       	=      os.path.join(snake_case		,"""checkpoint"""      )
                  accelerator.save_state(snake_case      )

                  # Load everything back in and make sure all states work
                  accelerator.load_state(snake_case      )
                  test_rands += train(1		,snake_case		,snake_case		,snake_case		,snake_case      )
                  ((lowercase)	,			(lowercase))							:     int       	=      model.a.item(), model.b.item()
                  lowercase							:     List[str]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           with tempfile.TemporaryDirectory() as tmpdir:
                  set_seed(42      )
                  lowercase							:     Dict       	=      DummyModel()
                  lowercase							:     List[Any]       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     Union[str, Any]       	=      dummy_dataloaders()
                  lowercase							:     Any       	=      ProjectConfiguration(automatic_checkpoint_naming=snake_case      )

                  # Train baseline
                  lowercase							:     int       	=      Accelerator(project_dir=snake_case		,project_config=snake_case      )
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     Tuple       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save initial
                  accelerator.save_state()
                  ((lowercase)	,			(lowercase))							:     Union[str, Any]       	=      model.a.item(), model.b.item()
                  lowercase							:     int       	=      optimizer.state_dict()
                  lowercase							:     Optional[Any]       	=      train(3		,snake_case		,snake_case		,snake_case		,snake_case      )
                  ((lowercase)	,			(lowercase))							:     Any       	=      model.a.item(), model.b.item()
                  lowercase							:     List[str]       	=      optimizer.state_dict()

                  # Train partially
                  set_seed(42      )
                  lowercase							:     Optional[int]       	=      DummyModel()
                  lowercase							:     Union[str, Any]       	=      torch.optim.Adam(params=model.parameters()		,lr=1e-3      )
                  lowercase	,			lowercase							:     Any       	=      dummy_dataloaders()
                  lowercase							:     Tuple       	=      ProjectConfiguration(iteration=1		,automatic_checkpoint_naming=snake_case      )
                  lowercase							:     Tuple       	=      Accelerator(project_dir=snake_case		,project_config=snake_case      )
                  lowercase	,			lowercase	,			lowercase	,			lowercase							:     List[Any]       	=      accelerator.prepare(
                      snake_case		,snake_case		,snake_case		,snake_case      )
                  accelerator.load_state(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_0"""      )      )
                  ((lowercase)	,			(lowercase))							:     Optional[int]       	=      model.a.item(), model.b.item()
                  lowercase							:     List[Any]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )

                  lowercase							:     int       	=      train(2		,snake_case		,snake_case		,snake_case		,snake_case      )
                  # Save everything
                  accelerator.save_state()

                  # Load everything back in and make sure all states work
                  accelerator.load_state(os.path.join(snake_case		,"""checkpoints"""		,"""checkpoint_1"""      )      )
                  test_rands += train(1		,snake_case		,snake_case		,snake_case		,snake_case      )
                  ((lowercase)	,			(lowercase))							:     Tuple       	=      model.a.item(), model.b.item()
                  lowercase							:     Optional[int]       	=      optimizer.state_dict()
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
                  self.assertEqual(snake_case		,snake_case      )
    def       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







           lowercase							:     Tuple       	=      torch.tensor([1, 2, 3]      )
           lowercase							:     int       	=      torch.tensor([2, 3, 4]      )
           lowercase							:     str       	=      DummyModel()
           lowercase							:     Optional[Any]       	=      torch.optim.Adam(net.parameters()      )
           lowercase							:     Any       	=      Accelerator()
           with self.assertRaises(snake_case      ) as ve:
                  accelerator.register_for_checkpointing(snake_case		,snake_case		,snake_case		,snake_case      )
           lowercase							:     int       	=      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       _SCREAMING_SNAKE_CASE       (  self      ):



           '''simple docstring'''







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

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



           '''simple docstring'''







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



           '''simple docstring'''







           lowercase							:     Optional[Any]       	=      ["""torchrun""", f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__      )]
           execute_subprocess_async(snake_case		,env=os.environ.copy()      )


if __name__ == "__main__":
  lowercase  :					int						    =							"""/tmp/accelerate/state_checkpointing"""
  lowercase  :					List[str]						    =							DummyModel()
  lowercase  :					Tuple						    =							torch.optim.Adam(params=model.parameters(), lr=1e-3)
  lowercase  :					List[str]						    =							torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
  lowercase    ,  lowercase  :					str						    =							dummy_dataloaders()
  lowercase  :					str						    =							ProjectConfiguration(automatic_checkpoint_naming=True)
  # Train baseline
  lowercase  :					Tuple						    =							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)
  lowercase    ,  lowercase    ,  lowercase    ,  lowercase    ,  lowercase  :					Optional[Any]						    =							accelerator.prepare(
      model, optimizer, train_dataloader, valid_dataloader, scheduler
  )
  lowercase    ,  lowercase  :					Union[str, Any]						    =							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:
    lowercase  :					Dict						    =							group["""params"""][0].device
    break
  assert param_device.type == accelerator.device.type
  lowercase  :					int						    =							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:
    lowercase  :					Tuple						    =							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:
    lowercase  :					List[str]						    =							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 CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPImageProcessor, CLIPProcessor







@require_vision
class 						lowerCAmelCase__   (      unittest.TestCase					):



 """simple docstring"""



 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> List[Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      tempfile.mkdtemp()

      # fmt: off
      __SCREAMING_SNAKE_CASE     =      ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
      # fmt: on
      __SCREAMING_SNAKE_CASE     =      dict(zip(__SCREAMING_SNAKE_CASE					,							range(len(__SCREAMING_SNAKE_CASE			)			)			)			)
      __SCREAMING_SNAKE_CASE     =      ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
      __SCREAMING_SNAKE_CASE     =      {"""unk_token""": """<unk>"""}

      __SCREAMING_SNAKE_CASE     =      os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES["""vocab_file"""]			)
      __SCREAMING_SNAKE_CASE     =      os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES["""merges_file"""]			)
      with open(self.vocab_file					,							"""w"""					,							encoding="""utf-8"""			) as fp:
           fp.write(json.dumps(__SCREAMING_SNAKE_CASE			) + """\n"""			)
      with open(self.merges_file					,							"""w"""					,							encoding="""utf-8"""			) as fp:
           fp.write("""\n""".join(__SCREAMING_SNAKE_CASE			)			)

      __SCREAMING_SNAKE_CASE     =      {
          """do_resize""": True,
          """size""": 20,
          """do_center_crop""": True,
          """crop_size""": 18,
          """do_normalize""": True,
          """image_mean""": [0.48145466, 0.4578275, 0.40821073],
          """image_std""": [0.26862954, 0.26130258, 0.27577711],
      }
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       Optional[int]					,							**__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> str:


      """simple docstring"""




      return CLIPTokenizer.from_pretrained(self.tmpdirname					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Tuple					,							**__SCREAMING_SNAKE_CASE					:       Any			)  -> int:


      """simple docstring"""




      return CLIPTokenizerFast.from_pretrained(self.tmpdirname					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]					,							**__SCREAMING_SNAKE_CASE					:       Optional[int]			)  -> List[str]:


      """simple docstring"""




      return CLIPImageProcessor.from_pretrained(self.tmpdirname					,							**__SCREAMING_SNAKE_CASE			)
 def       UpperCAmelCase__		(		self					:       Tuple			)  -> str:


      """simple docstring"""




      shutil.rmtree(self.tmpdirname			)
 def       UpperCAmelCase__		(		self					:       Optional[Any]			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      [np.random.randint(255					,							size=(3, 30, 400)					,							dtype=np.uinta			)]

      __SCREAMING_SNAKE_CASE     =      [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE					,							0					,							-1			)			) for x in image_inputs]

      return image_inputs
 def       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()
      __SCREAMING_SNAKE_CASE     =      self.get_rust_tokenizer()
      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)
      processor_slow.save_pretrained(self.tmpdirname			)
      __SCREAMING_SNAKE_CASE     =      CLIPProcessor.from_pretrained(self.tmpdirname					,							use_fast=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)
      processor_fast.save_pretrained(self.tmpdirname			)
      __SCREAMING_SNAKE_CASE     =      CLIPProcessor.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       UpperCAmelCase__		(		self					:       Optional[int]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=self.get_tokenizer()					,							image_processor=self.get_image_processor()			)
      processor.save_pretrained(self.tmpdirname			)

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

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor.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       UpperCAmelCase__		(		self					:       List[str]			)  -> Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      self.prepare_image_inputs()

      __SCREAMING_SNAKE_CASE     =      image_processor(__SCREAMING_SNAKE_CASE					,							return_tensors="""np"""			)
      __SCREAMING_SNAKE_CASE     =      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       UpperCAmelCase__		(		self					:       List[Any]			)  -> int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      """lower newer"""

      __SCREAMING_SNAKE_CASE     =      processor(text=__SCREAMING_SNAKE_CASE			)

      __SCREAMING_SNAKE_CASE     =      tokenizer(__SCREAMING_SNAKE_CASE			)

      for key in encoded_tok.keys():
           self.assertListEqual(encoded_tok[key]					,							encoded_processor[key]			)
 def       UpperCAmelCase__		(		self					:       Dict			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)

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

      __SCREAMING_SNAKE_CASE     =      processor(text=__SCREAMING_SNAKE_CASE					,							images=__SCREAMING_SNAKE_CASE			)

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

      # test if it raises when no input is passed
      with pytest.raises(__SCREAMING_SNAKE_CASE			):
           processor()
 def       UpperCAmelCase__		(		self					:       List[str]			)  -> Union[str, Any]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)

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

      __SCREAMING_SNAKE_CASE     =      processor.batch_decode(__SCREAMING_SNAKE_CASE			)
      __SCREAMING_SNAKE_CASE     =      tokenizer.batch_decode(__SCREAMING_SNAKE_CASE			)

      self.assertListEqual(__SCREAMING_SNAKE_CASE					,							__SCREAMING_SNAKE_CASE			)


 def       UpperCAmelCase__		(		self					:       int			)  -> str:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      self.get_image_processor()
      __SCREAMING_SNAKE_CASE     =      self.get_tokenizer()

      __SCREAMING_SNAKE_CASE     =      CLIPProcessor(tokenizer=__SCREAMING_SNAKE_CASE					,							image_processor=__SCREAMING_SNAKE_CASE			)

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

      __SCREAMING_SNAKE_CASE     =      processor(text=__SCREAMING_SNAKE_CASE					,							images=__SCREAMING_SNAKE_CASE			)

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






