

"""simple docstring"""




import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 		_A       (			__a					):
      __a								=	['image_processor', 'tokenizer']
      __a								=	'ChineseCLIPImageProcessor'
      __a								=	('BertTokenizer', 'BertTokenizerFast')





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__		)   ->					Dict:
         lowerCamelCase__						=			None
         if "feature_extractor" in kwargs:
            warnings.warn(
                "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                " instead."    ,						SCREAMING_SNAKE_CASE__    ,						)
            lowerCamelCase__						=			kwargs.pop("feature_extractor"		)

         lowerCamelCase__						=			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
            raise ValueError("You need to specify an `image_processor`."		)
         if tokenizer is None:
            raise ValueError("You need to specify a `tokenizer`."		)

         super().__init__(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			self.image_processor





      def __call__(  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:

         if text is None and images is None:
            raise ValueError("You have to specify either text or images. Both cannot be none."		)

         if text is not None:
            lowerCamelCase__						=			self.tokenizer(SCREAMING_SNAKE_CASE__    ,						return_tensors=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)

         if images is not None:
            lowerCamelCase__						=			self.image_processor(SCREAMING_SNAKE_CASE__    ,						return_tensors=SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)

         if text is not None and images is not None:
            lowerCamelCase__						=			image_features.pixel_values
            return encoding
         elif text is not None:
            return encoding
         else:
            return BatchEncoding(data=dict(**SCREAMING_SNAKE_CASE__		)    ,						tensor_type=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Union[str, Any]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					str:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			self.tokenizer.model_input_names
         lowerCamelCase__						=			self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)





      @property
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         warnings.warn(
             "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."    ,						SCREAMING_SNAKE_CASE__    ,						)
         return self.image_processor_class







"""simple docstring"""




import json
import os
import tempfile
from unittest.mock import patch

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

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment



def 	UpperCAmelCase__   (				)      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(2	,    4   )
   lowerCamelCase__						=			torch.optim.AdamW(model.parameters()	,    lr=1.0   )
   lowerCamelCase__						=			torch.optim.lr_scheduler.OneCycleLR(A__	,    max_lr=0.01	,    steps_per_epoch=2	,    epochs=1   )
   lowerCamelCase__						=			DataLoader(TensorDataset(torch.tensor([1, 2, 3]   )   )   )
   lowerCamelCase__						=			DataLoader(TensorDataset(torch.tensor([4, 5, 6]   )   )   )

   return model, optimizer, scheduler, train_dl, valid_dl



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
   model.load_state_dict(A__   )


class 		_A       (			__a					):





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__						=			GradientState()
         assert state.num_steps == 1
         lowerCamelCase__						=			4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         lowerCamelCase__						=			False
         assert state.sync_gradients is False
         GradientState._reset_state()





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()

         (
             (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       
         )						=			accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         self.assertTrue(prepared_model in accelerator._models		)
         self.assertTrue(prepared_optimizer in accelerator._optimizers		)
         self.assertTrue(prepared_scheduler in accelerator._schedulers		)
         self.assertTrue(prepared_train_dl in accelerator._dataloaders		)
         self.assertTrue(prepared_valid_dl in accelerator._dataloaders		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         accelerator.free_memory()

         self.assertTrue(len(accelerator._models		) == 0		)
         self.assertTrue(len(accelerator._optimizers		) == 0		)
         self.assertTrue(len(accelerator._schedulers		) == 0		)
         self.assertTrue(len(accelerator._dataloaders		) == 0		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         PartialState._reset_state()

         # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
         def noop(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		):
            pass

         with patch("torch.cuda.set_device"    ,						SCREAMING_SNAKE_CASE__		), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"		):
            lowerCamelCase__						=			Accelerator()
            self.assertEqual(str(accelerator.state.device		)    ,						"cuda:64"		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # make sure loaded weights match
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

         # saving hook
         def save_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			{"class_name": models[0].__class__.__name__}

            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"data.json"		)    ,						"w"		) as f:
               json.dump(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

        # loading hook
         def load_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"data.json"		)    ,						"r"		) as f:
               lowerCamelCase__						=			json.load(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			config["class_name"]

         lowerCamelCase__						=			accelerator.register_save_state_pre_hook(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.register_load_state_pre_hook(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match with hooks
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

            # make sure loaded weights match with hooks
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)

            # mode.class_name is loaded from config
            self.assertTrue(model.class_name == model.__class__.__name__		)

         # remove hooks
         save_hook.remove()
         load_hook.remove()

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match with hooks removed
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

            # make sure loaded weights match with hooks removed
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)

            # mode.class_name is NOT loaded from config
            self.assertTrue(model.class_name != model.__class__.__name__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			None

         # This should work
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         self.assertTrue(dummy_obj is None		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			[1, 2, 3]

         # This should work
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Dummy object should have `_is_accelerate_prepared` set to `True`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Model is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					str:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map={"": 0}    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			Accelerator()

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            model.tie_weights()
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			"cpu"

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						device_map=SCREAMING_SNAKE_CASE__    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						llm_inta_enable_fpaa_cpu_offload=SCREAMING_SNAKE_CASE__		)

         # This should not work and get value error
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @slow
      @require_bnb
      @require_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			{"distributed_type": DistributedType.MULTI_GPU}

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            model.tie_weights()
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should not work and get value error
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)

         PartialState._reset_state()





      @slow
      @require_bnb
      @require_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         from transformers import AutoModelForCausalLM

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			torch.nn.Linear(10    ,						10		)
         lowerCamelCase__						=			torch.optim.SGD(model.parameters()    ,						lr=0.01		)
         lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)




