

"""simple docstring"""




import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


SCREAMING_SNAKE_CASE_     :			List[str]	  =			logging.getLogger(__name__)


class 		_A       (			__a					):





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None		)   ->					str:
         lowerCamelCase__						=			self.layer[current_layer](SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						head_mask[current_layer]		)

         lowerCamelCase__						=			layer_outputs[0]

         return hidden_states






@add_start_docstrings(
    'The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.'				,       __a				,       )
class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					List[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			BertEncoderWithPabee(SCREAMING_SNAKE_CASE__		)

         self.init_weights()
         lowerCamelCase__						=			0
         lowerCamelCase__						=			0
         lowerCamelCase__						=			0

         lowerCamelCase__						=			0





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Any:
         lowerCamelCase__						=			threshold





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:
         lowerCamelCase__						=			patience





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			0
         lowerCamelCase__						=			0





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         lowerCamelCase__						=			self.inference_layers_num / self.inference_instances_num
         lowerCamelCase__						=			(
             f'*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='
             f' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'
         )
         print(SCREAMING_SNAKE_CASE__		)





      @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__		)
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=False    ,						)   ->					Union[str, Any]:

         if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"		)
         elif input_ids is not None:
            lowerCamelCase__						=			input_ids.size()
         elif inputs_embeds is not None:
            lowerCamelCase__						=			inputs_embeds.size()[:-1]
         else:
            raise ValueError("You have to specify either input_ids or inputs_embeds"		)

         lowerCamelCase__						=			input_ids.device if input_ids is not None else inputs_embeds.device

         if attention_mask is None:
            lowerCamelCase__						=			torch.ones(SCREAMING_SNAKE_CASE__    ,						device=SCREAMING_SNAKE_CASE__		)
         if token_type_ids is None:
            lowerCamelCase__						=			torch.zeros(SCREAMING_SNAKE_CASE__    ,						dtype=torch.long    ,						device=SCREAMING_SNAKE_CASE__		)

         # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
         # ourselves in which case we just need to make it broadcastable to all heads.
         lowerCamelCase__						=			self.get_extended_attention_mask(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         # If a 2D ou 3D attention mask is provided for the cross-attention
         # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
         if self.config.is_decoder and encoder_hidden_states is not None:
            lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			encoder_hidden_states.size()
            lowerCamelCase__						=			(encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None:
               lowerCamelCase__						=			torch.ones(SCREAMING_SNAKE_CASE__    ,						device=SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			self.invert_attention_mask(SCREAMING_SNAKE_CASE__		)
         else:
            lowerCamelCase__						=			None

         # Prepare head mask if needed
         # 1.0 in head_mask indicate we keep the head
         # attention_probs has shape bsz x n_heads x N x N
         # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
         # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
         lowerCamelCase__						=			self.get_head_mask(SCREAMING_SNAKE_CASE__    ,						self.config.num_hidden_layers		)

         lowerCamelCase__						=			self.embeddings(
             input_ids=SCREAMING_SNAKE_CASE__    ,						position_ids=SCREAMING_SNAKE_CASE__    ,						token_type_ids=SCREAMING_SNAKE_CASE__    ,						inputs_embeds=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			embedding_output

         if self.training:
            lowerCamelCase__						=			[]
            for i in range(self.config.num_hidden_layers		):
               lowerCamelCase__						=			self.encoder.adaptive_forward(
                   SCREAMING_SNAKE_CASE__    ,						current_layer=SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						head_mask=SCREAMING_SNAKE_CASE__		)

               lowerCamelCase__						=			self.pooler(SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			output_layers[i](output_dropout(SCREAMING_SNAKE_CASE__		)		)
               res.append(SCREAMING_SNAKE_CASE__		)
         elif self.patience == 0:  # Use all layers for inference
            lowerCamelCase__						=			self.encoder(
                SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						head_mask=SCREAMING_SNAKE_CASE__    ,						encoder_hidden_states=SCREAMING_SNAKE_CASE__    ,						encoder_attention_mask=SCREAMING_SNAKE_CASE__    ,						)
            lowerCamelCase__						=			self.pooler(encoder_outputs[0]		)
            lowerCamelCase__						=			[output_layers[self.config.num_hidden_layers - 1](SCREAMING_SNAKE_CASE__		)]
         else:
            lowerCamelCase__						=			0
            lowerCamelCase__						=			None
            lowerCamelCase__						=			0
            for i in range(self.config.num_hidden_layers		):
               calculated_layer_num += 1
               lowerCamelCase__						=			self.encoder.adaptive_forward(
                   SCREAMING_SNAKE_CASE__    ,						current_layer=SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						head_mask=SCREAMING_SNAKE_CASE__		)

               lowerCamelCase__						=			self.pooler(SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			output_layers[i](SCREAMING_SNAKE_CASE__		)
               if regression:
                  lowerCamelCase__						=			logits.detach()
                  if patient_result is not None:
                     lowerCamelCase__						=			patient_result.detach()
                  if (patient_result is not None) and torch.abs(patient_result - labels		) < self.regression_threshold:
                     patient_counter += 1
                  else:
                     lowerCamelCase__						=			0
               else:
                  lowerCamelCase__						=			logits.detach().argmax(dim=1		)
                  if patient_result is not None:
                     lowerCamelCase__						=			patient_result.detach().argmax(dim=1		)
                  if (patient_result is not None) and torch.all(labels.eq(SCREAMING_SNAKE_CASE__		)		):
                     patient_counter += 1
                  else:
                     lowerCamelCase__						=			0

               lowerCamelCase__						=			logits
               if patient_counter == self.patience:
                  break
            lowerCamelCase__						=			[patient_result]
            self.inference_layers_num += calculated_layer_num
            self.inference_instances_num += 1

         return res






@add_start_docstrings(
    'Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n    the pooled output) e.g. for GLUE tasks. '				,       __a				,       )
class 		_A       (			__a					):





      def __init__(  self    ,						SCREAMING_SNAKE_CASE__		)   ->					Dict:
         super().__init__(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			config.num_labels

         lowerCamelCase__						=			BertModelWithPabee(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			nn.Dropout(config.hidden_dropout_prob		)
         lowerCamelCase__						=			nn.ModuleList(
             [nn.Linear(config.hidden_size    ,						self.config.num_labels		) for _ in range(config.num_hidden_layers		)]		)

         self.init_weights()





      @add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__		)
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						SCREAMING_SNAKE_CASE__=None    ,						)   ->					int:
         lowerCamelCase__						=			self.bert(
             input_ids=SCREAMING_SNAKE_CASE__    ,						attention_mask=SCREAMING_SNAKE_CASE__    ,						token_type_ids=SCREAMING_SNAKE_CASE__    ,						position_ids=SCREAMING_SNAKE_CASE__    ,						head_mask=SCREAMING_SNAKE_CASE__    ,						inputs_embeds=SCREAMING_SNAKE_CASE__    ,						output_dropout=self.dropout    ,						output_layers=self.classifiers    ,						regression=self.num_labels == 1    ,						)

         lowerCamelCase__						=			(logits[-1],)

         if labels is not None:
            lowerCamelCase__						=			None
            lowerCamelCase__						=			0
            for ix, logits_item in enumerate(SCREAMING_SNAKE_CASE__		):
               if self.num_labels == 1:
                  #  We are doing regression
                  lowerCamelCase__						=			MSELoss()
                  lowerCamelCase__						=			loss_fct(logits_item.view(-1		)    ,						labels.view(-1		)		)
               else:
                  lowerCamelCase__						=			CrossEntropyLoss()
                  lowerCamelCase__						=			loss_fct(logits_item.view(-1    ,						self.num_labels		)    ,						labels.view(-1		)		)
               if total_loss is None:
                  lowerCamelCase__						=			loss
               else:
                  total_loss += loss * (ix + 1)
               total_weights += ix + 1
            lowerCamelCase__						=			(total_loss / total_weights,) + outputs

         return outputs







"""simple docstring"""




import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment



def 	UpperCAmelCase__   (				)      ->      List[Any]:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(2	,    4   )
   lowerCamelCase__						=			torch.optim.AdamW(model.parameters()	,    lr=1.0   )
   lowerCamelCase__						=			torch.optim.lr_scheduler.OneCycleLR(A__	,    max_lr=0.01	,    steps_per_epoch=2	,    epochs=1   )
   lowerCamelCase__						=			DataLoader(TensorDataset(torch.tensor([1, 2, 3]   )   )   )
   lowerCamelCase__						=			DataLoader(TensorDataset(torch.tensor([4, 5, 6]   )   )   )

   return model, optimizer, scheduler, train_dl, valid_dl



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   lowerCamelCase__						=			torch.nn.Linear(*tuple(model.weight.T.shape   )   ).state_dict()
   model.load_state_dict(A__   )


class 		_A       (			__a					):





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         assert PartialState._shared_state["_cpu"] is False
         assert PartialState._shared_state["device"].type == "cuda"
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__						=			GradientState()
         assert state.num_steps == 1
         lowerCamelCase__						=			4
         assert state.num_steps == 4

         assert state.sync_gradients is True
         lowerCamelCase__						=			False
         assert state.sync_gradients is False
         GradientState._reset_state()





      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()

         (
             (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       (
             lowerCamelCase__
         )					,       
         )						=			accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         self.assertTrue(prepared_model in accelerator._models		)
         self.assertTrue(prepared_optimizer in accelerator._optimizers		)
         self.assertTrue(prepared_scheduler in accelerator._schedulers		)
         self.assertTrue(prepared_train_dl in accelerator._dataloaders		)
         self.assertTrue(prepared_valid_dl in accelerator._dataloaders		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         accelerator.free_memory()

         self.assertTrue(len(accelerator._models		) == 0		)
         self.assertTrue(len(accelerator._optimizers		) == 0		)
         self.assertTrue(len(accelerator._schedulers		) == 0		)
         self.assertTrue(len(accelerator._dataloaders		) == 0		)





      def 					_lowerCamelCase  (  self		)   ->					Dict:
         PartialState._reset_state()

         # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
         def noop(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		):
            pass

         with patch("torch.cuda.set_device"    ,						SCREAMING_SNAKE_CASE__		), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"		):
            lowerCamelCase__						=			Accelerator()
            self.assertEqual(str(accelerator.state.device		)    ,						"cuda:64"		)





      def 					_lowerCamelCase  (  self		)   ->					Optional[Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # make sure loaded weights match
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)





      def 					_lowerCamelCase  (  self		)   ->					Any:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         accelerator.prepare(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			get_signature(SCREAMING_SNAKE_CASE__		)

         # saving hook
         def save_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			{"class_name": models[0].__class__.__name__}

            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"data.json"		)    ,						"w"		) as f:
               json.dump(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)

        # loading hook
         def load_config(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            with open(os.path.join(SCREAMING_SNAKE_CASE__    ,						"data.json"		)    ,						"r"		) as f:
               lowerCamelCase__						=			json.load(SCREAMING_SNAKE_CASE__		)

            lowerCamelCase__						=			config["class_name"]

         lowerCamelCase__						=			accelerator.register_save_state_pre_hook(SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.register_load_state_pre_hook(SCREAMING_SNAKE_CASE__		)

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match with hooks
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

            # make sure loaded weights match with hooks
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)

            # mode.class_name is loaded from config
            self.assertTrue(model.class_name == model.__class__.__name__		)

         # remove hooks
         save_hook.remove()
         load_hook.remove()

         with tempfile.TemporaryDirectory() as tmpdirname:
            accelerator.save_state(SCREAMING_SNAKE_CASE__		)

            # make sure random weights don't match with hooks removed
            load_random_weights(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) > 1e-3		)

            # random class name to verify correct one is loaded
            lowerCamelCase__						=			"random"

            # make sure loaded weights match with hooks removed
            accelerator.load_state(SCREAMING_SNAKE_CASE__		)
            self.assertTrue(abs(model_signature - get_signature(SCREAMING_SNAKE_CASE__		)		) < 1e-3		)

            # mode.class_name is NOT loaded from config
            self.assertTrue(model.class_name != model.__class__.__name__		)





      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			None

         # This should work
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         self.assertTrue(dummy_obj is None		)





      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			Accelerator()
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			create_components()
         lowerCamelCase__						=			[1, 2, 3]

         # This should work
         lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			accelerator.prepare(
             SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Dummy object should have `_is_accelerate_prepared` set to `True`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Model is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Optimizer is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Scheduler is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)
         self.assertEqual(
             getattr(SCREAMING_SNAKE_CASE__    ,						"_is_accelerate_prepared"    ,						SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						"Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`"    ,						)





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					str:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map={"": 0}    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @slow
      @require_bnb
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			Accelerator()

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            model.tie_weights()
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			"cpu"

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						device_map=SCREAMING_SNAKE_CASE__    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						llm_inta_enable_fpaa_cpu_offload=SCREAMING_SNAKE_CASE__		)

         # This should not work and get value error
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @slow
      @require_bnb
      @require_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					Dict:
         from transformers import AutoModelForCausalLM

         lowerCamelCase__						=			{"distributed_type": DistributedType.MULTI_GPU}

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            model.tie_weights()
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should not work and get value error
         with self.assertRaises(SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)

         PartialState._reset_state()





      @slow
      @require_bnb
      @require_multi_gpu
      def 					_lowerCamelCase  (  self		)   ->					List[Any]:
         from transformers import AutoModelForCausalLM

         with init_empty_weights():
            lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
                "EleutherAI/gpt-neo-125m"    ,						)
            lowerCamelCase__						=			infer_auto_device_map(SCREAMING_SNAKE_CASE__		)
            lowerCamelCase__						=			1

         lowerCamelCase__						=			AutoModelForCausalLM.from_pretrained(
             "EleutherAI/gpt-neo-125m"    ,						load_in_abit=SCREAMING_SNAKE_CASE__    ,						device_map=SCREAMING_SNAKE_CASE__    ,						)
         lowerCamelCase__						=			Accelerator()

         # This should work
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)





      @require_cuda
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         lowerCamelCase__						=			torch.nn.Linear(10    ,						10		)
         lowerCamelCase__						=			torch.optim.SGD(model.parameters()    ,						lr=0.01		)
         lowerCamelCase__						=			Accelerator(cpu=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			accelerator.prepare(SCREAMING_SNAKE_CASE__		)




