




'''simple docstring'''





import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
 import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


SCREAMING_SNAKE_CASE_						:					Any			   =       get_logger(__name__)
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = None   ,    ):




    for i, state in enumerate(lowerCamelCase__		):
        a__      =       F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        save(lowerCamelCase__   ,    lowerCamelCase__		)
        logger.info(F"""Model weights saved in {output_model_file}"""		)
    # Optimizer states
    for i, opt in enumerate(lowerCamelCase__		):
        a__      =       opt.state_dict()
        a__      =       F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        save(lowerCamelCase__   ,    lowerCamelCase__		)
        logger.info(F"""Optimizer state saved in {output_optimizer_file}"""		)
    # Scheduler states
    for i, scheduler in enumerate(lowerCamelCase__		):
        a__      =       scheduler.state_dict()
        a__      =       F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        save(lowerCamelCase__   ,    lowerCamelCase__		)
        logger.info(F"""Scheduler state saved in {output_scheduler_file}"""		)
    # GradScaler state
    if scaler is not None:
        a__      =       scaler.state_dict()
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        torch.save(lowerCamelCase__   ,    lowerCamelCase__		)
        logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""		)
    # Random number generator states
    a__      =       {}
    a__      =       F"""{RNG_STATE_NAME}_{process_index}.pkl"""
    a__      =       random.getstate()
    a__      =       np.random.get_state()
    a__      =       torch.get_rng_state()
    if is_xpu_available():
        a__      =       torch.xpu.get_rng_state_all()
    else:
        a__      =       torch.cuda.get_rng_state_all()
    if is_tpu_available():
        a__      =       xm.get_rng_state()
    a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
    torch.save(lowerCamelCase__   ,    lowerCamelCase__		)
    logger.info(F"""Random states saved in {output_states_file}"""		)
    return output_dir
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_=None   ,    UpperCAmelCase_=None   ,    **UpperCAmelCase_   ,    ):




    if map_location not in [None, "cpu", "on_device"]:
        raise TypeError(
            """Unsupported optimizer map location passed, please choose one of `None`, `'cpu'`, or `'on_device'`"""		)
    if map_location is None:
        a__      =       """cpu"""
    elif map_location == "on_device":
        a__      =       PartialState().device
    # Model states
    for i, model in enumerate(lowerCamelCase__		):
        a__      =       F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        models[i].load_state_dict(torch.load(lowerCamelCase__   ,    map_location=lowerCamelCase__		)   ,    **lowerCamelCase__		)
    logger.info("""All model weights loaded successfully"""		)

    # Optimizer states
    for i, opt in enumerate(lowerCamelCase__		):
        a__      =       F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        a__      =       torch.load(lowerCamelCase__   ,    map_location=lowerCamelCase__		)
        optimizers[i].load_state_dict(lowerCamelCase__		)
    logger.info("""All optimizer states loaded successfully"""		)

    # Scheduler states
    for i, scheduler in enumerate(lowerCamelCase__		):
        a__      =       F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        scheduler.load_state_dict(torch.load(lowerCamelCase__		)		)
    logger.info("""All scheduler states loaded successfully"""		)

    # GradScaler state
    if scaler is not None:
        a__      =       os.path.join(lowerCamelCase__   ,    lowerCamelCase__		)
        scaler.load_state_dict(torch.load(lowerCamelCase__		)		)
        logger.info("""GradScaler state loaded successfully"""		)

    # Random states
    try:
        a__      =       torch.load(os.path.join(lowerCamelCase__   ,    F"""{RNG_STATE_NAME}_{process_index}.pkl"""		)		)
        random.setstate(states["""random_state"""]		)
        np.random.set_state(states["""numpy_random_seed"""]		)
        torch.set_rng_state(states["""torch_manual_seed"""]		)
        if is_xpu_available():
            torch.xpu.set_rng_state_all(states["""torch_xpu_manual_seed"""]		)
        else:
            torch.cuda.set_rng_state_all(states["""torch_cuda_manual_seed"""]		)
        if is_tpu_available():
            xm.set_rng_state(states["""xm_seed"""]		)
        logger.info("""All random states loaded successfully"""		)
    except Exception:
        logger.info("""Could not load random states"""		)
def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = 0		):
    a__      =       Path(lowerCamelCase__		) / F"""custom_checkpoint_{index}.pkl"""
    logger.info(F"""Saving the state of {get_pretty_name(lowerCamelCase__		)} to {save_location}"""		)
    torch.save(obj.state_dict()   ,    lowerCamelCase__		)







def        __lowerCAmelCase (UpperCAmelCase_   ,    UpperCAmelCase_   ,    UpperCAmelCase_ = 0		):
    a__      =       F"""{path}/custom_checkpoint_{index}.pkl"""
    logger.info(F"""Loading the state of {get_pretty_name(lowerCamelCase__		)} from {load_location}"""		)
    obj.load_state_dict(torch.load(lowerCamelCase__   ,    map_location="""cpu"""		)		)












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
 import torch

 from transformers import CamembertModel







@require_torch
@require_sentencepiece
@require_tokenizers
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



     @slow
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			List[Any]:






         """simple docstring"""
         a__      =       CamembertModel.from_pretrained("""camembert-base"""    )
         model.to(A    )

         a__      =       torch.tensor(
             [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]						,				device=A						,				dtype=torch.long						,				)  # J'aime le camembert !
         with torch.no_grad():
             a__      =       model(A    )["""last_hidden_state"""]
         a__      =       torch.Size((1, 10, 7_68)    )
         self.assertEqual(output.shape						,				A    )
         # compare the actual values for a slice.
         a__      =       torch.tensor(
             [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]						,				device=A						,				dtype=torch.float						,				)
         # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
         # camembert.eval()
         # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

         self.assertTrue(torch.allclose(output[:, :3, :3]						,				A						,				atol=1E-4    )    )






