import os
import unittest
from tempfile import TemporaryDirectory

import torch
import torch.nn as nn

from accelerate.utils import (
    OffloadedWeightsLoader,
    extract_submodules_state_dict,
    load_offloaded_weight,
    offload_state_dict,
    offload_weight,
)





class       lowercase__		(     nn.Module):


      def __init__( self			:       Union[str, Any]		):





            '''simple docstring'''





            super().__init__()
            SCREAMING_SNAKE_CASE							:					Tuple           =							nn.Linear(3					,	4		)
            SCREAMING_SNAKE_CASE							:					List[Any]           =							nn.BatchNormad(4		)
            SCREAMING_SNAKE_CASE							:					Dict           =							nn.Linear(4					,	5		)



      def  __A ( self			:       Tuple					,	UpperCamelCase__			:       List[Any]		):





            '''simple docstring'''





            return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__		)		)		)






class       lowercase__		(     unittest.TestCase):


      def  __A ( self			:       Any		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Dict           =							ModelForTest()
            with TemporaryDirectory() as tmp_dir:
                  offload_state_dict(UpperCamelCase__					,	model.state_dict()		)
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							os.path.join(UpperCamelCase__					,	'''index.json'''		)
                  self.assertTrue(os.path.isfile(UpperCamelCase__		)		)
                  # TODO: add tests on what is inside the index

                  for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
                        SCREAMING_SNAKE_CASE							:					Optional[Any]           =							os.path.join(UpperCamelCase__					,	f"""{key}.dat"""		)
                        self.assertTrue(os.path.isfile(UpperCamelCase__		)		)
                        # TODO: add tests on the fact weights are properly loaded


      def  __A ( self			:       List[str]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					int           =							[torch.floataa, torch.floataa, torch.bfloataa]

            for dtype in dtypes:
                  SCREAMING_SNAKE_CASE							:					List[str]           =							torch.randn(2					,	3					,	dtype=UpperCamelCase__		)
                  with TemporaryDirectory() as tmp_dir:
                        SCREAMING_SNAKE_CASE							:					Tuple           =							offload_weight(UpperCamelCase__					,	'''weight'''					,	UpperCamelCase__					,	{}		)
                        SCREAMING_SNAKE_CASE							:					Tuple           =							os.path.join(UpperCamelCase__					,	'''weight.dat'''		)
                        self.assertTrue(os.path.isfile(UpperCamelCase__		)		)
                        self.assertDictEqual(UpperCamelCase__					,	{'''weight''': {'''shape''': [2, 3], '''dtype''': str(UpperCamelCase__		).split('''.'''		)[1]}}		)

                        SCREAMING_SNAKE_CASE							:					Optional[Any]           =							load_offloaded_weight(UpperCamelCase__					,	index['''weight''']		)
                        self.assertTrue(torch.equal(UpperCamelCase__					,	UpperCamelCase__		)		)


      def  __A ( self			:       Optional[int]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					Tuple           =							ModelForTest()
            SCREAMING_SNAKE_CASE							:					Any           =							model.state_dict()
            SCREAMING_SNAKE_CASE							:					Tuple           =							{k: v for k, v in state_dict.items() if '''linear2''' not in k}
            SCREAMING_SNAKE_CASE							:					Tuple           =							{k: v for k, v in state_dict.items() if '''linear2''' in k}

            with TemporaryDirectory() as tmp_dir:
                  offload_state_dict(UpperCamelCase__					,	UpperCamelCase__		)
                  SCREAMING_SNAKE_CASE							:					Any           =							OffloadedWeightsLoader(state_dict=UpperCamelCase__					,	save_folder=UpperCamelCase__		)

                  # Every key is there with the right value
                  self.assertEqual(sorted(UpperCamelCase__		)					,	sorted(state_dict.keys()		)		)
                  for key, param in state_dict.items():
                        self.assertTrue(torch.allclose(UpperCamelCase__					,	weight_map[key]		)		)

            SCREAMING_SNAKE_CASE							:					List[Any]           =							{k: v for k, v in state_dict.items() if '''weight''' in k}
            SCREAMING_SNAKE_CASE							:					List[Any]           =							{k: v for k, v in state_dict.items() if '''weight''' not in k}

            with TemporaryDirectory() as tmp_dir:
                  offload_state_dict(UpperCamelCase__					,	UpperCamelCase__		)
                  SCREAMING_SNAKE_CASE							:					List[Any]           =							OffloadedWeightsLoader(state_dict=UpperCamelCase__					,	save_folder=UpperCamelCase__		)

                  # Every key is there with the right value
                  self.assertEqual(sorted(UpperCamelCase__		)					,	sorted(state_dict.keys()		)		)
                  for key, param in state_dict.items():
                        self.assertTrue(torch.allclose(UpperCamelCase__					,	weight_map[key]		)		)

            with TemporaryDirectory() as tmp_dir:
                  offload_state_dict(UpperCamelCase__					,	UpperCamelCase__		)
                  # Duplicates are removed
                  SCREAMING_SNAKE_CASE							:					int           =							OffloadedWeightsLoader(state_dict=UpperCamelCase__					,	save_folder=UpperCamelCase__		)

                  # Every key is there with the right value
                  self.assertEqual(sorted(UpperCamelCase__		)					,	sorted(state_dict.keys()		)		)
                  for key, param in state_dict.items():
                        self.assertTrue(torch.allclose(UpperCamelCase__					,	weight_map[key]		)		)



      def  __A ( self			:       List[Any]		):





            '''simple docstring'''





            SCREAMING_SNAKE_CASE							:					int           =							{'''a.1''': 0, '''a.10''': 1, '''a.2''': 2}
            SCREAMING_SNAKE_CASE							:					int           =							extract_submodules_state_dict(UpperCamelCase__					,	['''a.1''', '''a.2''']		)
            self.assertDictEqual(UpperCamelCase__					,	{'''a.1''': 0, '''a.2''': 2}		)

            SCREAMING_SNAKE_CASE							:					Dict           =							{'''a.1.a''': 0, '''a.10.a''': 1, '''a.2.a''': 2}
            SCREAMING_SNAKE_CASE							:					Optional[int]           =							extract_submodules_state_dict(UpperCamelCase__					,	['''a.1''', '''a.2''']		)
            self.assertDictEqual(UpperCamelCase__					,	{'''a.1.a''': 0, '''a.2.a''': 2}		)






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


__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[Any]    		=  {
    'microsoft/deberta-v2-xlarge': 'https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xxlarge': 'https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json',
    'microsoft/deberta-v2-xlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'
    ),
    'microsoft/deberta-v2-xxlarge-mnli': (
        'https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'
    ),
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""deberta-v2"""


						def __init__( self			:       Optional[Any]					,	UpperCamelCase__			:       Any=12_8100					,	UpperCamelCase__			:       Optional[int]=1536					,	UpperCamelCase__			:       Dict=24					,	UpperCamelCase__			:       List[str]=24					,	UpperCamelCase__			:       Tuple=6144					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       Optional[int]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Optional[int]=512					,	UpperCamelCase__			:       Optional[Any]=0					,	UpperCamelCase__			:       Union[str, Any]=0.02					,	UpperCamelCase__			:       List[Any]=1E-7					,	UpperCamelCase__			:       List[str]=False					,	UpperCamelCase__			:       str=-1					,	UpperCamelCase__			:       List[Any]=0					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       Optional[Any]=None					,	UpperCamelCase__			:       Tuple=0					,	UpperCamelCase__			:       str="gelu"					,	**UpperCamelCase__			:       Optional[int]					,	):





												'''simple docstring'''





												super().__init__(**UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					List[Any]           =							hidden_size
												SCREAMING_SNAKE_CASE							:					int           =							num_hidden_layers
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							num_attention_heads
												SCREAMING_SNAKE_CASE							:					List[Any]           =							intermediate_size
												SCREAMING_SNAKE_CASE							:					Any           =							hidden_act
												SCREAMING_SNAKE_CASE							:					Dict           =							hidden_dropout_prob
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							attention_probs_dropout_prob
												SCREAMING_SNAKE_CASE							:					str           =							max_position_embeddings
												SCREAMING_SNAKE_CASE							:					Dict           =							type_vocab_size
												SCREAMING_SNAKE_CASE							:					str           =							initializer_range
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							relative_attention
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							max_relative_positions
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							pad_token_id
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							position_biased_input

												# Backwards compatibility
												if type(UpperCamelCase__		) == str:
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							[x.strip() for x in pos_att_type.lower().split('''|'''		)]

												SCREAMING_SNAKE_CASE							:					Any           =							pos_att_type
												SCREAMING_SNAKE_CASE							:					int           =							vocab_size
												SCREAMING_SNAKE_CASE							:					Dict           =							layer_norm_eps

												SCREAMING_SNAKE_CASE							:					Tuple           =							kwargs.get('''pooler_hidden_size'''					,	UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Any           =							pooler_dropout
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							pooler_hidden_act






class       lowercase__		(     UpperCamelCase_):


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												if self.task == "multiple-choice":
																		SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
												else:
																		SCREAMING_SNAKE_CASE							:					Optional[Any]           =							{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			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Dict					,	UpperCamelCase__			:       Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       "PreTrainedTokenizerBase" = None					,	):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							super().generate_dummy_inputs(preprocessor=UpperCamelCase__					,	framework=UpperCamelCase__		)
												if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
																		del dummy_inputs["token_type_ids"]
												return dummy_inputs





