






from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
					from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


lowerCAmelCase_     =			logging.get_logger(__name__)

lowerCAmelCase_     =			{
    "microsoft/deberta-base": "https://huggingface.co/microsoft/deberta-base/resolve/main/config.json",
    "microsoft/deberta-large": "https://huggingface.co/microsoft/deberta-large/resolve/main/config.json",
    "microsoft/deberta-xlarge": "https://huggingface.co/microsoft/deberta-xlarge/resolve/main/config.json",
    "microsoft/deberta-base-mnli": "https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/config.json",
    "microsoft/deberta-large-mnli": "https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/config.json",
    "microsoft/deberta-xlarge-mnli": "https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/config.json",
}
class   UpperCamelCase			(					SCREAMING_SNAKE_CASE__			):







		"""simple docstring"""



		snake_case			 = '''deberta'''
		def __init__(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							Tuple=5_0_2_6_5    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=7_6_8    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=1_2    ,_SCREAMING_SNAKE_CASE		:							str=1_2    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=3_0_7_2    ,_SCREAMING_SNAKE_CASE		:							List[str]="gelu"    ,_SCREAMING_SNAKE_CASE		:							Tuple=0.1    ,_SCREAMING_SNAKE_CASE		:							Tuple=0.1    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=5_1_2    ,_SCREAMING_SNAKE_CASE		:							Tuple=0    ,_SCREAMING_SNAKE_CASE		:							Tuple=0.02    ,_SCREAMING_SNAKE_CASE		:							str=1E-7    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=False    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=-1    ,_SCREAMING_SNAKE_CASE		:							Any=0    ,_SCREAMING_SNAKE_CASE		:							List[Any]=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=None    ,_SCREAMING_SNAKE_CASE		:							Dict=0    ,_SCREAMING_SNAKE_CASE		:							Tuple="gelu"    ,**_SCREAMING_SNAKE_CASE		:							int    ,)     -> Optional[Any]:


						'''simple docstring'''
						super().__init__(**A__		)

						A									=   hidden_size
						A									=   num_hidden_layers
						A									=   num_attention_heads
						A									=   intermediate_size
						A									=   hidden_act
						A									=   hidden_dropout_prob
						A									=   attention_probs_dropout_prob
						A									=   max_position_embeddings
						A									=   type_vocab_size
						A									=   initializer_range
						A									=   relative_attention
						A									=   max_relative_positions
						A									=   pad_token_id
						A									=   position_biased_input

						# Backwards compatibility
						if type(A__		) == str:
										A									=   [x.strip() for x in pos_att_type.lower().split('|'		)]

						A									=   pos_att_type
						A									=   vocab_size
						A									=   layer_norm_eps

						A									=   kwargs.get('pooler_hidden_size'    ,A__		)
						A									=   pooler_dropout
						A									=   pooler_hidden_act


class   UpperCamelCase			(					SCREAMING_SNAKE_CASE__			):







		"""simple docstring"""



		@property
		def    A(	self		:							List[Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						if self.task == "multiple-choice":
										A									=   {0: 'batch', 1: 'choice', 2: 'sequence'}
						else:
										A									=   {0: 'batch', 1: 'sequence'}
						if self._config.type_vocab_size > 0:
										return OrderedDict(
										    [('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)]		)
						else:
										return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)]		)
		@property
		def    A(	self		:							str		)     -> List[Any]:


						'''simple docstring'''
						return 1_2







		def    A(	self		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							Tuple = -1    ,_SCREAMING_SNAKE_CASE		:							Tuple = -1    ,_SCREAMING_SNAKE_CASE		:							List[str] = -1    ,_SCREAMING_SNAKE_CASE		:							List[Any] = False    ,_SCREAMING_SNAKE_CASE		:							Dict = None    ,_SCREAMING_SNAKE_CASE		:							Optional[Any] = 3    ,_SCREAMING_SNAKE_CASE		:							int = 4_0    ,_SCREAMING_SNAKE_CASE		:							List[str] = 4_0    ,_SCREAMING_SNAKE_CASE		:							int = None    ,)     -> str:


						'''simple docstring'''
						A									=   super().generate_dummy_inputs(preprocessor=A__    ,framework=A__		)
						if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
										del dummy_inputs["token_type_ids"]
						return dummy_inputs







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


lowercase__						:Optional[Any]					    =       get_logger(__name__)



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = None					,     ):
					'''simple docstring'''
					# Model states
					for i, state in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		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__						):
										lowercase		      =		opt.state_dict()
										lowercase		      =		f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										lowercase		      =		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__						):
										lowercase		      =		scheduler.state_dict()
										lowercase		      =		f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										lowercase		      =		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:
										lowercase		      =		scaler.state_dict()
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
										logger.info(f'Gradient scaler state saved in {output_scaler_file}'						)
					# Random number generator states
					lowercase		      =		{}
					lowercase		      =		f'{RNG_STATE_NAME}_{process_index}.pkl'
					lowercase		      =		random.getstate()
					lowercase		      =		np.random.get_state()
					lowercase		      =		torch.get_rng_state()
					if is_xpu_available():
										lowercase		      =		torch.xpu.get_rng_state_all()
					else:
										lowercase		      =		torch.cuda.get_rng_state_all()
					if is_tpu_available():
										lowercase		      =		xm.get_rng_state()
					lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
					torch.save(lowerCAmelCase__					,     lowerCAmelCase__						)
					logger.info(f'Random states saved in {output_states_file}'						)
					return output_dir



def 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__=None					,     lowerCAmelCase__=None					,     **lowerCAmelCase__					,     ):
					'''simple docstring'''
					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:
										lowercase		      =		'''cpu'''
					elif map_location == "on_device":
										lowercase		      =		PartialState().device
					# Model states
					for i, model in enumerate(lowerCAmelCase__						):
										lowercase		      =		f'{MODEL_NAME}.bin' if i == 0 else f'{MODEL_NAME}_{i}.bin'
										lowercase		      =		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__						):
										lowercase		      =		f'{OPTIMIZER_NAME}.bin' if i == 0 else f'{OPTIMIZER_NAME}_{i}.bin'
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										lowercase		      =		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__						):
										lowercase		      =		f'{SCHEDULER_NAME}.bin' if i == 0 else f'{SCHEDULER_NAME}_{i}.bin'
										lowercase		      =		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:
										lowercase		      =		os.path.join(lowerCAmelCase__					,     lowerCAmelCase__						)
										scaler.load_state_dict(torch.load(lowerCAmelCase__						)						)
										logger.info('''GradScaler state loaded successfully'''						)

					# Random states
					try:
										lowercase		      =		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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		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 			UpperCamelCase       (				lowerCAmelCase__					,     lowerCAmelCase__					,     lowerCAmelCase__ = 0						):
					'''simple docstring'''
					lowercase		      =		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'''						)						)




