





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    ):





      def __init__(				self     ):
       '''simple docstring'''




       super().__init__()
       UpperCamelCase			:      Optional[Any] 			=     nn.Linear(3 ,							4     )
       UpperCamelCase			:      Dict 			=     nn.BatchNormad(4     )
       UpperCamelCase			:      List[str] 			=     nn.Linear(4 ,							5     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       return self.lineara(self.batchnorm(self.lineara(A_     )     )     )



class       A__     ( unittest.TestCase    ):





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      str 			=     [torch.floataa, torch.floataa, torch.bfloataa]

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

         UpperCamelCase			:      List[Any] 			=     load_offloaded_weight(A_ ,							index["weight"]     )
         self.assertTrue(torch.equal(A_ ,							A_     )     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     ModelForTest()
       UpperCamelCase			:      Optional[int] 			=     model.state_dict()
       UpperCamelCase			:      List[Any] 			=     {k: v for k, v in state_dict.items() if "linear2" not in k}
       UpperCamelCase			:      Optional[int] 			=     {k: v for k, v in state_dict.items() if "linear2" in k}

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(A_ ,							A_     )
        UpperCamelCase			:      Optional[int] 			=     OffloadedWeightsLoader(state_dict=A_ ,							save_folder=A_     )

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

       UpperCamelCase			:      str 			=     {k: v for k, v in state_dict.items() if "weight" in k}
       UpperCamelCase			:      List[str] 			=     {k: v for k, v in state_dict.items() if "weight" not in k}

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(A_ ,							A_     )
        UpperCamelCase			:      Any 			=     OffloadedWeightsLoader(state_dict=A_ ,							save_folder=A_     )

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

       with TemporaryDirectory() as tmp_dir:
        offload_state_dict(A_ ,							A_     )
        # Duplicates are removed
        UpperCamelCase			:      Dict 			=     OffloadedWeightsLoader(state_dict=A_ ,							save_folder=A_     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      int 			=     {"a.1": 0, "a.10": 1, "a.2": 2}
       UpperCamelCase			:      int 			=     extract_submodules_state_dict(A_ ,							["a.1", "a.2"]     )
       self.assertDictEqual(A_ ,							{"a.1": 0, "a.2": 2}     )

       UpperCamelCase			:      Tuple 			=     {"a.1.a": 0, "a.10.a": 1, "a.2.a": 2}
       UpperCamelCase			:      Dict 			=     extract_submodules_state_dict(A_ ,							["a.1", "a.2"]     )
       self.assertDictEqual(A_ ,							{"a.1.a": 0, "a.2.a": 2}     )







from collections import OrderedDict
from typing import Mapping

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


__lowerCamelCase :  Tuple										=  logging.get_logger(__name__)

__lowerCamelCase :  str										=  {
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:Union[str, Any]							=     'camembert'





      def __init__(				self ,							A_=3_0522 ,							A_=768 ,							A_=12 ,							A_=12 ,							A_=3072 ,							A_="gelu" ,							A_=0.1 ,							A_=0.1 ,							A_=512 ,							A_=2 ,							A_=0.02 ,							A_=1e-12 ,							A_=1 ,							A_=0 ,							A_=2 ,							A_="absolute" ,							A_=True ,							A_=None ,							**A_ ,							):
       '''simple docstring'''




       super().__init__(pad_token_id=A_ ,							bos_token_id=A_ ,							eos_token_id=A_ ,							**A_     )

       UpperCamelCase			:      List[str] 			=     vocab_size
       UpperCamelCase			:      Union[str, Any] 			=     hidden_size
       UpperCamelCase			:      Any 			=     num_hidden_layers
       UpperCamelCase			:      Union[str, Any] 			=     num_attention_heads
       UpperCamelCase			:      Dict 			=     hidden_act
       UpperCamelCase			:      str 			=     intermediate_size
       UpperCamelCase			:      str 			=     hidden_dropout_prob
       UpperCamelCase			:      Dict 			=     attention_probs_dropout_prob
       UpperCamelCase			:      Union[str, Any] 			=     max_position_embeddings
       UpperCamelCase			:      Optional[Any] 			=     type_vocab_size
       UpperCamelCase			:      int 			=     initializer_range
       UpperCamelCase			:      List[str] 			=     layer_norm_eps
       UpperCamelCase			:      Dict 			=     position_embedding_type
       UpperCamelCase			:      int 			=     use_cache
       UpperCamelCase			:      List[str] 			=     classifier_dropout



class       A__     ( __snake_case    ):





      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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