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 lowerCamelCase  (       ):
    '''simple docstring'''







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

    return model, optimizer, scheduler, train_dl, valid_dl



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Union[str, Any]        =		torch.nn.Linear(*tuple(model.weight.T.shape     )     ).state_dict()
    model.load_state_dict(SCREAMING_SNAKE_CASE     )


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    @require_cuda
    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Dict        =		Accelerator()
        assert PartialState._shared_state["_cpu"] is False
        assert PartialState._shared_state["device"].type == "cuda"
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Any        =		Accelerator(cpu=__lowercase)







    def       UpperCamelCase__       (     self)	->     Any:
        __UpperCamelCase					:List[Any]        =		Accelerator()
        __UpperCamelCase					:List[Any]        =		GradientState()
        assert state.num_steps == 1
        __UpperCamelCase					:Any        =		4
        assert state.num_steps == 4

        assert state.sync_gradients is True
        __UpperCamelCase					:int        =		False
        assert state.sync_gradients is False
        GradientState._reset_state()







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Tuple        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		create_components()

        (
            (
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	(
            __UpperCamelCase
        )  ,	
        )					:int        =		accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        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       UpperCamelCase__       (     self)	->     Optional[Any]:
        __UpperCamelCase					:str        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Optional[Any]        =		create_components()
        accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)
        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       UpperCamelCase__       (     self)	->     Union[str, Any]:
        PartialState._reset_state()

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

        with patch('''torch.cuda.set_device'''			,			__lowercase), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64'''):
            __UpperCamelCase					:Optional[Any]        =		Accelerator()
            self.assertEqual(str(accelerator.state.device)			,			'''cuda:64''')







    def       UpperCamelCase__       (     self)	->     List[Any]:
        __UpperCamelCase					:List[Any]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		create_components()
        accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        __UpperCamelCase					:Tuple        =		get_signature(__lowercase)

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

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

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







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:List[Any]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		create_components()
        accelerator.prepare(__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)

        __UpperCamelCase					:Any        =		get_signature(__lowercase)

        # saving hook
        def save_config(__lowercase			,			__lowercase			,			__lowercase):
            __UpperCamelCase					:Union[str, Any]        =		{'''class_name''': models[0].__class__.__name__}

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

        # loading hook
        def load_config(__lowercase			,			__lowercase):
            with open(os.path.join(__lowercase			,			'''data.json''')			,			'''r''') as f:
                __UpperCamelCase					:Dict        =		json.load(__lowercase)

            __UpperCamelCase					:Dict        =		config['''class_name''']

        __UpperCamelCase					:Union[str, Any]        =		accelerator.register_save_state_pre_hook(__lowercase)
        __UpperCamelCase					:Any        =		accelerator.register_load_state_pre_hook(__lowercase)

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

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

            # random class name to verify correct one is loaded
            __UpperCamelCase					:int        =		'''random'''

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

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

            # random class name to verify correct one is loaded
            __UpperCamelCase					:Dict        =		'''random'''

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

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







    def       UpperCamelCase__       (     self)	->     Dict:
        __UpperCamelCase					:Optional[Any]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		create_components()
        __UpperCamelCase					:Optional[Any]        =		None

        # This should work
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:List[Any]        =		accelerator.prepare(
            __lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase			,			__lowercase)
        self.assertTrue(dummy_obj is None)







    def       UpperCamelCase__       (     self)	->     List[str]:
        __UpperCamelCase					:List[str]        =		Accelerator()
        __UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase  ,	__UpperCamelCase					:Any        =		create_components()
        __UpperCamelCase					:Dict        =		[1, 2, 3]

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







    @slow
    @require_bnb
    def       UpperCamelCase__       (     self)	->     int:
        from transformers import AutoModelForCausalLM

        __UpperCamelCase					:Optional[int]        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			load_in_abit=__lowercase			,			device_map={'''''': 0}			,			)
        __UpperCamelCase					:Optional[Any]        =		Accelerator()

        # This should work
        __UpperCamelCase					:int        =		accelerator.prepare(__lowercase)







    @slow
    @require_bnb
    def       UpperCamelCase__       (     self)	->     List[str]:
        from transformers import AutoModelForCausalLM

        __UpperCamelCase					:str        =		Accelerator()

        with init_empty_weights():
            __UpperCamelCase					:List[Any]        =		AutoModelForCausalLM.from_pretrained(
                '''EleutherAI/gpt-neo-125m'''			,			)
            model.tie_weights()
            __UpperCamelCase					:List[str]        =		infer_auto_device_map(__lowercase)
            __UpperCamelCase					:str        =		'''cpu'''

        __UpperCamelCase					:List[Any]        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			device_map=__lowercase			,			load_in_abit=__lowercase			,			llm_inta_enable_fpaa_cpu_offload=__lowercase)

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







    @slow
    @require_bnb
    @require_multi_gpu
    def       UpperCamelCase__       (     self)	->     Union[str, Any]:
        from transformers import AutoModelForCausalLM

        __UpperCamelCase					:int        =		{'''distributed_type''': DistributedType.MULTI_GPU}

        with init_empty_weights():
            __UpperCamelCase					:Tuple        =		AutoModelForCausalLM.from_pretrained(
                '''EleutherAI/gpt-neo-125m'''			,			)
            model.tie_weights()
            __UpperCamelCase					:int        =		infer_auto_device_map(__lowercase)
            __UpperCamelCase					:List[Any]        =		1

        __UpperCamelCase					:int        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			load_in_abit=__lowercase			,			device_map=__lowercase			,			)
        __UpperCamelCase					:Dict        =		Accelerator()

        # This should not work and get value error
        with self.assertRaises(__lowercase):
            __UpperCamelCase					:Any        =		accelerator.prepare(__lowercase)

        PartialState._reset_state()







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

        with init_empty_weights():
            __UpperCamelCase					:Optional[int]        =		AutoModelForCausalLM.from_pretrained(
                '''EleutherAI/gpt-neo-125m'''			,			)
            __UpperCamelCase					:List[str]        =		infer_auto_device_map(__lowercase)
            __UpperCamelCase					:Optional[int]        =		1

        __UpperCamelCase					:Optional[int]        =		AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''			,			load_in_abit=__lowercase			,			device_map=__lowercase			,			)
        __UpperCamelCase					:int        =		Accelerator()

        # This should work
        __UpperCamelCase					:int        =		accelerator.prepare(__lowercase)







    @require_cuda
    def       UpperCamelCase__       (     self)	->     int:
        __UpperCamelCase					:Tuple        =		torch.nn.Linear(10			,			10)
        __UpperCamelCase					:Optional[Any]        =		torch.optim.SGD(model.parameters()			,			lr=0.01)
        __UpperCamelCase					:Any        =		Accelerator(cpu=__lowercase)
        __UpperCamelCase					:Tuple        =		accelerator.prepare(__lowercase)






import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


__lowercase										= logging.get_logger(__name__)
logging.set_verbosity_info()



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    if "xprophetnet" in prophetnet_checkpoint_path:
        __UpperCamelCase					:Dict        =		XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:int        =		XLMProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )
    else:
        __UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE     )
        __UpperCamelCase  ,	__UpperCamelCase					:Union[str, Any]        =		ProphetNetForConditionalGeneration.from_pretrained(
            SCREAMING_SNAKE_CASE  ,      output_loading_info=SCREAMING_SNAKE_CASE     )

    __UpperCamelCase					:Dict        =		['''key_proj''', '''value_proj''', '''query_proj''']

    __UpperCamelCase					:Optional[Any]        =		{
        '''self_attn''': '''ngram_self_attn''',
        '''cross_attn''': '''encoder_attn''',
        '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
        '''feed_forward_layer_norm''': '''final_layer_norm''',
        '''feed_forward''': '''''',
        '''intermediate''': '''fc1''',
        '''output''': '''fc2''',
        '''key_proj''': '''k_proj''',
        '''query_proj''': '''q_proj''',
        '''value_proj''': '''v_proj''',
        '''word_embeddings''': '''embed_tokens''',
        '''embeddings_layer_norm''': '''emb_layer_norm''',
        '''relative_pos_embeddings''': '''relative_linear''',
        '''ngram_embeddings''': '''ngram_input_embed''',
        '''position_embeddings''': '''embed_positions''',
    }

    for key in loading_info["missing_keys"]:
        __UpperCamelCase					:Tuple        =		key.split('''.'''     )

        if attributes[0] == "lm_head":
            __UpperCamelCase					:Union[str, Any]        =		prophet
            __UpperCamelCase					:Any        =		prophet_old
        else:
            __UpperCamelCase					:Any        =		prophet.prophetnet
            __UpperCamelCase					:int        =		prophet_old.model

        __UpperCamelCase					:Optional[Any]        =		False
        for attribute in attributes:
            if attribute in mapping:
                __UpperCamelCase					:str        =		mapping[attribute]
                if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ) and len(SCREAMING_SNAKE_CASE     ) > 0:
                    __UpperCamelCase					:Optional[int]        =		attribute
            elif hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                __UpperCamelCase					:List[Any]        =		attribute

            if attribute == "weight":
                assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
                __UpperCamelCase					:Tuple        =		old_model.weight
                logger.info(f"""{attribute} is initialized."""     )
                __UpperCamelCase					:Union[str, Any]        =		True
                break
            elif attribute == "bias":
                assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
                __UpperCamelCase					:Union[str, Any]        =		old_model.bias
                logger.info(f"""{attribute} is initialized"""     )
                __UpperCamelCase					:List[Any]        =		True
                break
            elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE  ,      '''in_proj_weight'''     ):
                __UpperCamelCase					:str        =		old_model.in_proj_weight.shape[0] // 3
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
                param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
                param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
                if attribute == "query_proj":
                    __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.in_proj_weight[:embed_dim, :]     )
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_bias[:embed_dim]     )

                elif attribute == "key_proj":
                    __UpperCamelCase					:List[Any]        =		nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]     )
                elif attribute == "value_proj":
                    __UpperCamelCase					:Tuple        =		nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]     )
                    __UpperCamelCase					:Dict        =		nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]     )
                __UpperCamelCase					:Optional[int]        =		True
                break
            elif attribute == "position_embeddings":
                assert (
                    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
                ), "Hidden size has to match"
                assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
                __UpperCamelCase					:Optional[int]        =		nn.Parameter(old_model.embed_positions.weight[:512, :]     )
                __UpperCamelCase					:List[Any]        =		True
                break

            if attribute.isdigit():
                __UpperCamelCase					:List[Any]        =		model[int(SCREAMING_SNAKE_CASE     )]
                __UpperCamelCase					:Optional[int]        =		old_model[int(SCREAMING_SNAKE_CASE     )]
            else:
                __UpperCamelCase					:Optional[Any]        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

                if old_attribute == "":
                    __UpperCamelCase					:Any        =		old_model
                else:
                    if not hasattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
                        raise ValueError(f"""{old_model} does not have {old_attribute}"""     )
                    __UpperCamelCase					:Any        =		getattr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )

        if not is_key_init:
            raise ValueError(f"""{key} was not correctly initialized!"""     )

    print(f"""Saving model to {pytorch_dump_folder_path}"""     )
    prophet.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
    __lowercase										= argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--prophetnet_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    __lowercase										= parser.parse_args()
    convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)





