

"""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       SCREAMING_SNAKE_CASE							( )			->						Any:
 lowerCAmelCase__   :				List[Any] 			=				torch.nn.Linear(2					,					4   )
 lowerCAmelCase__   :				Union[str, Any] 			=				torch.optim.AdamW(model.parameters()					,					lr=1.0   )
 lowerCAmelCase__   :				Union[str, Any] 			=				torch.optim.lr_scheduler.OneCycleLR(lowercase__					,					max_lr=0.01					,					steps_per_epoch=2					,					epochs=1   )
 lowerCAmelCase__   :				Optional[int] 			=				DataLoader(TensorDataset(torch.tensor([1, 2, 3]   )   )   )
 lowerCAmelCase__   :				Dict 			=				DataLoader(TensorDataset(torch.tensor([4, 5, 6]   )   )   )

 return model, optimizer, scheduler, train_dl, valid_dl



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Tuple:
 return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						int:
 lowerCAmelCase__   :				str 			=				torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
 model.load_state_dict(lowercase__   )






class 			__a					(						__magic_name__       ):





       """simple docstring"""


       @require_cuda
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				Accelerator()
        assert PartialState._shared_state["_cpu"] is False
        assert PartialState._shared_state["device"].type == "cuda"
        with self.assertRaises(snake_case   ):
         lowerCAmelCase__   :				List[str] 			=				Accelerator(cpu=snake_case   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				Accelerator()
        lowerCAmelCase__   :				Union[str, Any] 			=				GradientState()
        assert state.num_steps == 1
        lowerCAmelCase__   :				Tuple 			=				4
        assert state.num_steps == 4

        assert state.sync_gradients is True
        lowerCAmelCase__   :				Optional[int] 			=				False
        assert state.sync_gradients is False
        GradientState._reset_state()






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				Accelerator()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				create_components()

        (
            (
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						(
            lowerCAmelCase__
        )			,						
        )   :				Optional[int] 			=				accelerator.prepare(snake_case			,					snake_case			,					snake_case			,					snake_case			,					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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[Any] 			=				Accelerator()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				create_components()
        accelerator.prepare(snake_case			,					snake_case			,					snake_case			,					snake_case			,					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 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        PartialState._reset_state()

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

        with patch("torch.cuda.set_device"			,					snake_case   ), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"   ):
         lowerCAmelCase__   :				List[str] 			=				Accelerator()
         self.assertEqual(str(accelerator.state.device   )			,					"cuda:64"   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				List[str] 			=				Accelerator()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				create_components()
        accelerator.prepare(snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   )

        lowerCAmelCase__   :				Dict 			=				get_signature(snake_case   )

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

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Union[str, Any] 			=				Accelerator()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				List[Any] 			=				create_components()
        accelerator.prepare(snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   )

        lowerCAmelCase__   :				List[Any] 			=				get_signature(snake_case   )

        # saving hook
        def save_config(snake_case			,					snake_case			,					snake_case   ):
         lowerCAmelCase__   :				List[str] 			=				{"class_name": models[0].__class__.__name__}

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

        # loading hook
        def load_config(snake_case			,					snake_case   ):
         with open(os.path.join(snake_case			,					"data.json"   )			,					"r"   ) as f:
          lowerCAmelCase__   :				Optional[int] 			=				json.load(snake_case   )

         lowerCAmelCase__   :				Optional[Any] 			=				config["class_name"]

        lowerCAmelCase__   :				Union[str, Any] 			=				accelerator.register_save_state_pre_hook(snake_case   )
        lowerCAmelCase__   :				List[Any] 			=				accelerator.register_load_state_pre_hook(snake_case   )

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

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

         # random class name to verify correct one is loaded
         lowerCAmelCase__   :				Dict 			=				"random"

         # make sure loaded weights match with hooks
         accelerator.load_state(snake_case   )
         self.assertTrue(abs(model_signature - get_signature(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(snake_case   )

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

         # random class name to verify correct one is loaded
         lowerCAmelCase__   :				Tuple 			=				"random"

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

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






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				Optional[int] 			=				Accelerator()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Union[str, Any] 			=				create_components()
        lowerCAmelCase__   :				Union[str, Any] 			=				None

        # This should work
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				str 			=				accelerator.prepare(
            snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   )
        self.assertTrue(dummy_obj is None   )






       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				int 			=				Accelerator()
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				int 			=				create_components()
        lowerCAmelCase__   :				List[Any] 			=				[1, 2, 3]

        # This should work
        lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__   :				Optional[int] 			=				accelerator.prepare(
            snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case			,					snake_case   )
        self.assertEqual(
            getattr(snake_case			,					"_is_accelerate_prepared"			,					snake_case   )			,					snake_case			,					"Dummy object should have `_is_accelerate_prepared` set to `True`"			,					)
        self.assertEqual(
            getattr(snake_case			,					"_is_accelerate_prepared"			,					snake_case   )			,					snake_case			,					"Model is missing `_is_accelerator_prepared` or is set to `False`"			,					)
        self.assertEqual(
            getattr(snake_case			,					"_is_accelerate_prepared"			,					snake_case   )			,					snake_case			,					"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"			,					)
        self.assertEqual(
            getattr(snake_case			,					"_is_accelerate_prepared"			,					snake_case   )			,					snake_case			,					"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"			,					)
        self.assertEqual(
            getattr(snake_case			,					"_is_accelerate_prepared"			,					snake_case   )			,					snake_case			,					"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"			,					)
        self.assertEqual(
            getattr(snake_case			,					"_is_accelerate_prepared"			,					snake_case   )			,					snake_case			,					"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"			,					)






       @slow
       @require_bnb
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        from transformers import AutoModelForCausalLM

        lowerCAmelCase__   :				Tuple 			=				AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"			,					load_in_abit=snake_case			,					device_map={"": 0}			,					)
        lowerCAmelCase__   :				str 			=				Accelerator()

        # This should work
        lowerCAmelCase__   :				str 			=				accelerator.prepare(snake_case   )






       @slow
       @require_bnb
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        from transformers import AutoModelForCausalLM

        lowerCAmelCase__   :				Union[str, Any] 			=				Accelerator()

        with init_empty_weights():
         lowerCAmelCase__   :				List[Any] 			=				AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"			,					)
         model.tie_weights()
         lowerCAmelCase__   :				Optional[int] 			=				infer_auto_device_map(snake_case   )
         lowerCAmelCase__   :				Optional[int] 			=				"cpu"

        lowerCAmelCase__   :				List[Any] 			=				AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"			,					device_map=snake_case			,					load_in_abit=snake_case			,					llm_inta_enable_fpaa_cpu_offload=snake_case   )

        # This should not work and get value error
        with self.assertRaises(snake_case   ):
         lowerCAmelCase__   :				Optional[Any] 			=				accelerator.prepare(snake_case   )






       @slow
       @require_bnb
       @require_multi_gpu
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        from transformers import AutoModelForCausalLM

        lowerCAmelCase__   :				Any 			=				{"distributed_type": DistributedType.MULTI_GPU}

        with init_empty_weights():
         lowerCAmelCase__   :				str 			=				AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"			,					)
         model.tie_weights()
         lowerCAmelCase__   :				Any 			=				infer_auto_device_map(snake_case   )
         lowerCAmelCase__   :				int 			=				1

        lowerCAmelCase__   :				Optional[int] 			=				AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"			,					load_in_abit=snake_case			,					device_map=snake_case			,					)
        lowerCAmelCase__   :				Any 			=				Accelerator()

        # This should not work and get value error
        with self.assertRaises(snake_case   ):
         lowerCAmelCase__   :				Union[str, Any] 			=				accelerator.prepare(snake_case   )

        PartialState._reset_state()






       @slow
       @require_bnb
       @require_multi_gpu
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        from transformers import AutoModelForCausalLM

        with init_empty_weights():
         lowerCAmelCase__   :				str 			=				AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"			,					)
         lowerCAmelCase__   :				Optional[Any] 			=				infer_auto_device_map(snake_case   )
         lowerCAmelCase__   :				List[Any] 			=				1

        lowerCAmelCase__   :				Optional[int] 			=				AutoModelForCausalLM.from_pretrained(
            "EleutherAI/gpt-neo-125m"			,					load_in_abit=snake_case			,					device_map=snake_case			,					)
        lowerCAmelCase__   :				str 			=				Accelerator()

        # This should work
        lowerCAmelCase__   :				Tuple 			=				accelerator.prepare(snake_case   )






       @require_cuda
       def 			SCREAMING_SNAKE_CASE_	(   self   ):





        """simple docstring"""

        lowerCAmelCase__   :				str 			=				torch.nn.Linear(10			,					10   )
        lowerCAmelCase__   :				Dict 			=				torch.optim.SGD(model.parameters()			,					lr=0.01   )
        lowerCAmelCase__   :				Optional[Any] 			=				Accelerator(cpu=snake_case   )
        lowerCAmelCase__   :				Dict 			=				accelerator.prepare(snake_case   )









"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

_UpperCamelCase		     =    TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_UpperCamelCase		     =    TaTokenizerFast

_UpperCamelCase		     =    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    [
        """MT5EncoderModel""",
        """MT5ForConditionalGeneration""",
        """MT5ForQuestionAnswering""",
        """MT5Model""",
        """MT5PreTrainedModel""",
        """MT5Stack""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    _UpperCamelCase		     =    _LazyModule(
        __name__,
        globals()["""__file__"""],
        _import_structure,
        extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
        module_spec=__spec__,
    )






