
"""simple docstring"""

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      _UpperCAmelCase							(		nn.Module							):




						def __init__(							self							)						->					Optional[Any]:
										super().__init__()
										UpperCAmelCase									=					nn.Linear(3   ,       4							)
										UpperCAmelCase									=					nn.BatchNormad(4							)
										UpperCAmelCase									=					nn.Linear(4   ,       5							)




						def     a_      (							self   ,       lowercase_							)						->					Optional[Any]:
										return self.lineara(self.batchnorm(self.lineara(lowercase_							)							)							)

class      _UpperCAmelCase							(		unittest.TestCase							):




						def     a_      (							self							)						->					str:
										UpperCAmelCase									=					ModelForTest()
										with TemporaryDirectory() as tmp_dir:
														offload_state_dict(lowercase_   ,       model.state_dict()							)
														UpperCAmelCase									=					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"]:
																		UpperCAmelCase									=					os.path.join(lowercase_   ,       F"{key}.dat"							)
																		self.assertTrue(os.path.isfile(lowercase_							)							)
																		# TODO: add tests on the fact weights are properly loaded




						def     a_      (							self							)						->					Optional[int]:
										UpperCAmelCase									=					[torch.floataa, torch.floataa, torch.bfloataa]

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

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




						def     a_      (							self							)						->					Optional[Any]:
										UpperCAmelCase									=					ModelForTest()
										UpperCAmelCase									=					model.state_dict()
										UpperCAmelCase									=					{k: v for k, v in state_dict.items() if 'linear2' not in k}
										UpperCAmelCase									=					{k: v for k, v in state_dict.items() if 'linear2' in k}

										with TemporaryDirectory() as tmp_dir:
														offload_state_dict(lowercase_   ,       lowercase_							)
														UpperCAmelCase									=					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]							)							)

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

										with TemporaryDirectory() as tmp_dir:
														offload_state_dict(lowercase_   ,       lowercase_							)
														UpperCAmelCase									=					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
														UpperCAmelCase									=					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     a_      (							self							)						->					str:
										UpperCAmelCase									=					{'a.1': 0, 'a.10': 1, 'a.2': 2}
										UpperCAmelCase									=					extract_submodules_state_dict(lowercase_   ,       ['a.1', 'a.2']							)
										self.assertDictEqual(lowercase_   ,       {'a.1': 0, 'a.2': 2}							)

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





"""simple docstring"""

from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
   import datasets
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_							):




      @slow
      @require_torch
      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'   ,       'prajjwal1/bert-tiny'							)
          UpperCAmelCase									=					BertTokenizer.from_pretrained('bert-base-uncased'							)

          UpperCAmelCase									=					bertabert.config.encoder.vocab_size
          UpperCAmelCase									=					tokenizer.sep_token_id
          UpperCAmelCase									=					tokenizer.cls_token_id
          UpperCAmelCase									=					1_2_8

          UpperCAmelCase									=					datasets.load_dataset('cnn_dailymail'   ,       '3.0.0'   ,       split='train[:1%]'							)
          UpperCAmelCase									=					datasets.load_dataset('cnn_dailymail'   ,       '3.0.0'   ,       split='validation[:1%]'							)

          UpperCAmelCase									=					train_dataset.select(range(3_2							)							)
          UpperCAmelCase									=					val_dataset.select(range(1_6							)							)

          UpperCAmelCase									=					4

          def _map_to_encoder_decoder_inputs(lowercase_							):
              # Tokenizer will automatically set [BOS] <text> [EOS]
              UpperCAmelCase									=					tokenizer(batch['article']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=5_1_2							)
              UpperCAmelCase									=					tokenizer(batch['highlights']   ,       padding='max_length'   ,       truncation=lowercase_   ,       max_length=1_2_8							)
              UpperCAmelCase									=					inputs.input_ids
              UpperCAmelCase									=					inputs.attention_mask

              UpperCAmelCase									=					outputs.input_ids
              UpperCAmelCase									=					outputs.input_ids.copy()
              UpperCAmelCase									=					[
                  [-1_0_0 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
              ]
              UpperCAmelCase									=					outputs.attention_mask

              assert all(len(lowercase_							) == 5_1_2 for x in inputs.input_ids							)
              assert all(len(lowercase_							) == 1_2_8 for x in outputs.input_ids							)

              return batch

          def _compute_metrics(lowercase_							):
              UpperCAmelCase									=					pred.label_ids
              UpperCAmelCase									=					pred.predictions

              # all unnecessary tokens are removed
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)
              UpperCAmelCase									=					tokenizer.batch_decode(lowercase_   ,       skip_special_tokens=lowercase_							)

              UpperCAmelCase									=					sum([int(pred_str[i] == label_str[i]							) for i in range(len(lowercase_							)							)]							) / len(lowercase_							)

              return {"accuracy": accuracy}

          # map train dataset
          UpperCAmelCase									=					train_dataset.map(
              _map_to_encoder_decoder_inputs   ,       batched=lowercase_   ,       batch_size=lowercase_   ,       remove_columns=['article', 'highlights']   ,       )
          train_dataset.set_format(
              type='torch'   ,       columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,       )

          # same for validation dataset
          UpperCAmelCase									=					val_dataset.map(
              _map_to_encoder_decoder_inputs   ,       batched=lowercase_   ,       batch_size=lowercase_   ,       remove_columns=['article', 'highlights']   ,       )
          val_dataset.set_format(
              type='torch'   ,       columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']   ,       )

          UpperCAmelCase									=					self.get_auto_remove_tmp_dir()

          UpperCAmelCase									=					SeqaSeqTrainingArguments(
              output_dir=lowercase_   ,       per_device_train_batch_size=lowercase_   ,       per_device_eval_batch_size=lowercase_   ,       predict_with_generate=lowercase_   ,       evaluation_strategy='steps'   ,       do_train=lowercase_   ,       do_eval=lowercase_   ,       warmup_steps=0   ,       eval_steps=2   ,       logging_steps=2   ,       )

          # instantiate trainer
          UpperCAmelCase									=					SeqaSeqTrainer(
              model=lowercase_   ,       args=lowercase_   ,       compute_metrics=_compute_metrics   ,       train_dataset=lowercase_   ,       eval_dataset=lowercase_   ,       tokenizer=lowercase_   ,       )

          # start training
          trainer.train()



