

from collections import OrderedDict
from typing import Mapping

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


lowercase_			=  logging.get_logger(__name__)

lowercase_			=  {
    "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json",
    "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json",
    "junnyu/roformer_chinese_char_small": (
        "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json"
    ),
    "junnyu/roformer_chinese_char_base": (
        "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json"
    ),
    "junnyu/roformer_small_discriminator": (
        "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json"
    ),
    "junnyu/roformer_small_generator": (
        "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json"
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}


class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    lowerCamelCase		     =							'roformer'


    def __init__(				self			:	Union[str, Any],lowercase_			:	List[Any]=5_0_0_0_0,lowercase_			:	Dict=None,lowercase_			:	Union[str, Any]=7_6_8,lowercase_			:	Any=1_2,lowercase_			:	Tuple=1_2,lowercase_			:	Optional[int]=3_0_7_2,lowercase_			:	Optional[int]="gelu",lowercase_			:	Any=0.1,lowercase_			:	Dict=0.1,lowercase_			:	List[Any]=1_5_3_6,lowercase_			:	Optional[int]=2,lowercase_			:	List[Any]=0.02,lowercase_			:	List[str]=1E-12,lowercase_			:	Optional[int]=0,lowercase_			:	Any=False,lowercase_			:	Dict=True,**lowercase_			:	Tuple,)->   Dict:





        '''simple docstring'''







        super().__init__(pad_token_id=lowercase_,**lowercase_		)

        A__   				=       vocab_size
        A__   				=       hidden_size if embedding_size is None else embedding_size
        A__   				=       hidden_size
        A__   				=       num_hidden_layers
        A__   				=       num_attention_heads
        A__   				=       hidden_act
        A__   				=       intermediate_size
        A__   				=       hidden_dropout_prob
        A__   				=       attention_probs_dropout_prob
        A__   				=       max_position_embeddings
        A__   				=       type_vocab_size
        A__   				=       initializer_range
        A__   				=       layer_norm_eps
        A__   				=       rotary_value
        A__   				=       use_cache




class 			A						(							_UpperCAmelCase			):







    """simple docstring"""





    @property
    def 						snake_case__					(				self			:	List[str]		)->   Mapping[str, Mapping[int, str]]:





        '''simple docstring'''







        if self.task == "multiple-choice":
            A__   				=       {0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
            A__   				=       {0: 'batch', 1: 'sequence'}
        A__   				=       {0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
                ('token_type_ids', dynamic_axis),
            ]		)




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 			A						(							nn.Module			):







    """simple docstring"""





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





        '''simple docstring'''







        super().__init__()
        A__   				=       nn.Linear(3,4		)
        A__   				=       nn.BatchNormad(4		)
        A__   				=       nn.Linear(4,5		)


    def 						snake_case__					(				self			:	Dict,lowercase_			:	Union[str, Any]		)->   Tuple:





        '''simple docstring'''







        return self.lineara(self.batchnorm(self.lineara(lowercase_		)		)		)




class 			A						(							unittest.TestCase			):







    """simple docstring"""





    def 						snake_case__					(				self			:	List[str]		)->   Any:





        '''simple docstring'''







        A__   				=       ModelForTest()
        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,model.state_dict()		)
            A__   				=       os.path.join(lowercase_,'index.json'		)
            self.assertTrue(os.path.isfile(lowercase_		)		)
            # TODO: add tests on what is inside the index

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


    def 						snake_case__					(				self			:	List[Any]		)->   Optional[int]:





        '''simple docstring'''







        A__   				=       [torch.floataa, torch.floataa, torch.bfloataa]

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

                A__   				=       load_offloaded_weight(lowercase_,index['weight']		)
                self.assertTrue(torch.equal(lowercase_,lowercase_		)		)


    def 						snake_case__					(				self			:	Optional[int]		)->   Optional[Any]:





        '''simple docstring'''







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

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

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

        A__   				=       {k: v for k, v in state_dict.items() if 'weight' in k}
        A__   				=       {k: v for k, v in state_dict.items() if 'weight' not in k}

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

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

        with TemporaryDirectory() as tmp_dir:
            offload_state_dict(lowercase_,lowercase_		)
            # Duplicates are removed
            A__   				=       OffloadedWeightsLoader(state_dict=lowercase_,save_folder=lowercase_		)

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


    def 						snake_case__					(				self			:	int		)->   Union[str, Any]:





        '''simple docstring'''







        A__   				=       {'a.1': 0, 'a.10': 1, 'a.2': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1': 0, 'a.2': 2}		)

        A__   				=       {'a.1.a': 0, 'a.10.a': 1, 'a.2.a': 2}
        A__   				=       extract_submodules_state_dict(lowercase_,['a.1', 'a.2']		)
        self.assertDictEqual(lowercase_,{'a.1.a': 0, 'a.2.a': 2}		)

