





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
snake_case_				=			logging.get_logger(__name__)

snake_case_				=			{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
snake_case_				=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def lowerCamelCase__    (							snake_case_	:  Tuple       )      ->				Tuple:
   __snake_case							       =   {}
   with open(snake_case_       ,      '''r'''       ) as file:
      for line_number, line in enumerate(snake_case_       ):
         __snake_case							       =   line.strip()
         if line:
            __snake_case							       =   line.split()
            __snake_case							       =   line_number
            __snake_case							       =   words[0]
            __snake_case							       =   value
   return result






def lowerCamelCase__    (							snake_case_	:  Optional[int]       ,      snake_case_	:  Tuple       ,      snake_case_	:  List[Any]       ,      snake_case_	:  Dict       ,      snake_case_	:  Optional[Any]       )      ->				List[Any]:
   for attribute in key.split('''.'''       ):
      __snake_case							       =   getattr(snake_case_       ,      snake_case_       )

   __snake_case							       =   None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case_       ):
         __snake_case							       =   PARAM_MAPPING[full_name.split('''.'''       )[-1]]
         __snake_case							       =   '''param'''

   if weight_type is not None and weight_type != "param":
      __snake_case							       =   getattr(snake_case_       ,      snake_case_       ).shape
   elif weight_type is not None and weight_type == "param":
      __snake_case							       =   hf_pointer
      for attribute in hf_param_name.split('''.'''       ):
         __snake_case							       =   getattr(snake_case_       ,      snake_case_       )
      __snake_case							       =   shape_pointer.shape

      # let's reduce dimension
      __snake_case							       =   value[0]
   else:
      __snake_case							       =   hf_pointer.shape

   if hf_shape != value.shape:
      raise ValueError(
          f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
          f""" {value.shape} for {full_name}"""       )

   if weight_type == "weight":
      __snake_case							       =   value
   elif weight_type == "weight_g":
      __snake_case							       =   value
   elif weight_type == "weight_v":
      __snake_case							       =   value
   elif weight_type == "bias":
      __snake_case							       =   value
   elif weight_type == "param":
      for attribute in hf_param_name.split('''.'''       ):
         __snake_case							       =   getattr(snake_case_       ,      snake_case_       )
      __snake_case							       =   value
   else:
      __snake_case							       =   value

   logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""       )






def lowerCamelCase__    (							snake_case_	:  Dict       ,      snake_case_	:  Any       ,      snake_case_	:  Dict       ,      snake_case_	:  Union[str, Any]       ,      snake_case_	:  List[str]       )      ->				str:
   __snake_case							       =   None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(snake_case_       ):
         __snake_case							       =   PARAM_MAPPING[full_name.split('''.'''       )[-1]]
         __snake_case							       =   '''param'''

   if weight_type is not None and weight_type != "param":
      __snake_case							       =   '''.'''.join([key, weight_type]       )
   elif weight_type is not None and weight_type == "param":
      __snake_case							       =   '''.'''.join([key, hf_param_name]       )
   else:
      __snake_case							       =   key

   __snake_case							       =   value if '''lm_head''' in full_key else value[0]


snake_case_				=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  List[Any]       ,      snake_case_	:  List[str]=None       ,      snake_case_	:  Dict=None       )      ->				str:
   __snake_case							       =   False
   for key, mapped_key in MAPPING.items():
      __snake_case							       =   '''wav2vec2.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
      if key in name or key.split('''w2v_model.'''       )[-1] == name.split('''.'''       )[0]:
         __snake_case							       =   True
         if "*" in mapped_key:
            __snake_case							       =   name.split(snake_case_       )[0].split('''.'''       )[-2]
            __snake_case							       =   mapped_key.replace('''*'''       ,      snake_case_       )
         if "weight_g" in name:
            __snake_case							       =   '''weight_g'''
         elif "weight_v" in name:
            __snake_case							       =   '''weight_v'''
         elif "bias" in name:
            __snake_case							       =   '''bias'''
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __snake_case							       =   '''weight'''
         else:
            __snake_case							       =   None
         if hf_dict is not None:
            rename_dict(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
         else:
            set_recursively(snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       )
         return is_used
   return is_used






def lowerCamelCase__    (							snake_case_	:  int       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  List[str]       )      ->				int:
   __snake_case							       =   []
   __snake_case							       =   fairseq_model.state_dict()

   __snake_case							       =   hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case							       =   False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_       ,      snake_case_       ,      snake_case_       ,      snake_case_       ,      hf_model.config.feat_extract_norm == '''group'''       ,      )
         __snake_case							       =   True
      else:
         __snake_case							       =   load_wavaveca_layer(snake_case_       ,      snake_case_       ,      snake_case_       )
      if not is_used:
         unused_weights.append(snake_case_       )

   logger.warning(f"""Unused weights: {unused_weights}"""       )






def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Any       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  Optional[Any]       ,      snake_case_	:  List[str]       )      ->				List[Any]:
   __snake_case							       =   full_name.split('''conv_layers.'''       )[-1]
   __snake_case							       =   name.split('''.'''       )
   __snake_case							       =   int(items[0]       )
   __snake_case							       =   int(items[1]       )

   if type_id == 0:
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""       )
         __snake_case							       =   value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""       )
         __snake_case							       =   value
         logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""       )
   elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
      if "bias" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""       )
         __snake_case							       =   value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
      elif "weight" in name:
         if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
            raise ValueError(
                f"""{full_name} has size {value.shape}, but"""
                f""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""       )
         __snake_case							       =   value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""       )
   else:
      unused_weights.append(snake_case_       )






@torch.no_grad()
def lowerCamelCase__    (							snake_case_	:  List[str]       ,      snake_case_	:  Optional[int]       ,      snake_case_	:  int=None       ,      snake_case_	:  Any=None       ,      snake_case_	:  Any=True       ,      snake_case_	:  Any=False       )      ->				List[str]:
   if config_path is not None:
      __snake_case							       =   WavaVecaConfig.from_pretrained(snake_case_       )
   else:
      __snake_case							       =   WavaVecaConfig()

   if is_seq_class:
      __snake_case							       =   read_txt_into_dict(snake_case_       )
      __snake_case							       =   idalabel
      __snake_case							       =   WavaVecaForSequenceClassification(snake_case_       )
      __snake_case							       =   WavaVecaFeatureExtractor(
          feature_size=1       ,      sampling_rate=1_6000       ,      padding_value=0       ,      do_normalize=snake_case_       ,      return_attention_mask=snake_case_       ,      )
      feature_extractor.save_pretrained(snake_case_       )

   elif is_finetuned:
      if dict_path:
         __snake_case							       =   Dictionary.load(snake_case_       )

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __snake_case							       =   target_dict.pad_index
         __snake_case							       =   target_dict.bos_index
         __snake_case							       =   target_dict.eos_index
         __snake_case							       =   len(target_dict.symbols       )
         __snake_case							       =   os.path.join(snake_case_       ,      '''vocab.json'''       )
         if not os.path.isdir(snake_case_       ):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case_       )       )
            return
         os.makedirs(snake_case_       ,      exist_ok=snake_case_       )
         __snake_case							       =   target_dict.indices

         # fairseq has the <pad> and <s> switched
         __snake_case							       =   0
         __snake_case							       =   1
         with open(snake_case_       ,      '''w'''       ,      encoding='''utf-8'''       ) as vocab_handle:
            json.dump(snake_case_       ,      snake_case_       )
         __snake_case							       =   WavaVecaCTCTokenizer(
             snake_case_       ,      unk_token=target_dict.unk_word       ,      pad_token=target_dict.pad_word       ,      bos_token=target_dict.bos_word       ,      eos_token=target_dict.eos_word       ,      word_delimiter_token='''|'''       ,      do_lower_case=snake_case_       ,      )
         __snake_case							       =   True if config.feat_extract_norm == '''layer''' else False
         __snake_case							       =   WavaVecaFeatureExtractor(
             feature_size=1       ,      sampling_rate=1_6000       ,      padding_value=0       ,      do_normalize=snake_case_       ,      return_attention_mask=snake_case_       ,      )
         __snake_case							       =   WavaVecaProcessor(feature_extractor=snake_case_       ,      tokenizer=snake_case_       )
         processor.save_pretrained(snake_case_       )

      __snake_case							       =   WavaVecaForCTC(snake_case_       )
   else:
      __snake_case							       =   WavaVecaForPreTraining(snake_case_       )

   if is_finetuned or is_seq_class:
      __snake_case       ,	__snake_case       ,	__snake_case							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]       ,      arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
   else:
      __snake_case							       =   argparse.Namespace(task='''audio_pretraining'''       )
      __snake_case							       =   fairseq.tasks.setup_task(snake_case_       )

      __snake_case       ,	__snake_case       ,	__snake_case							       =   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,      task=snake_case_       )

   __snake_case							       =   model[0].eval()

   recursively_load_weights(snake_case_       ,      snake_case_       ,      not is_finetuned       )

   hf_wavavec.save_pretrained(snake_case_       )


if __name__ == "__main__":
    snake_case_				=			argparse.ArgumentParser()
    parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
    parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
    parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    parser.add_argument(
        '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    parser.add_argument(
        '--is_seq_class',
        action='store_true',
        help='Whether the model to convert is a fine-tuned sequence classification model or not',
    )
    snake_case_				=			parser.parse_args()

    snake_case_				=			not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )









import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest

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

from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
from accelerate.utils import ProjectConfiguration, set_seed


__UpperCAmelCase								=						logging.getLogger(__name__)




def      lowercase__      (     __snake_case    :							List[Any]=2     ,       __snake_case    :							Union[str, Any]=3     ,       __snake_case    :							Any=16     ,       __snake_case    :							int = 10     ,       __snake_case    :							int = 2  ):


					'''simple docstring'''


					def get_dataset(__snake_case    :							Optional[Any]  ):
										UpperCAmelCase_    :      Optional[Any]            =						torch.randn(batch_size * n_batches     ,       1  )
										return TensorDataset(__snake_case     ,       a * x + b + 0.1 * torch.randn(batch_size * n_batches     ,       1  )  )

					UpperCAmelCase_    :      Any            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      str            =						get_dataset(__snake_case  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					UpperCAmelCase_    :      int            =						DataLoader(__snake_case     ,       shuffle=__snake_case     ,       batch_size=__snake_case     ,       num_workers=4  )
					return (train_dataloader, valid_dataloader)




def      lowercase__      (     __snake_case    :							Optional[int]     ,       __snake_case    :							str     ,       __snake_case    :							Optional[int]     ,       __snake_case    :							List[str]     ,       __snake_case    :							Any     ,       __snake_case    :							Tuple=None  ):


					'''simple docstring'''


					UpperCAmelCase_    :      Optional[int]            =						[]
					for epoch in range(__snake_case  ):
										# Train quickly
										model.train()
										for batch in dataloader:
															UpperCAmelCase_       ,  UpperCAmelCase_    :      List[Any]            =						batch
															UpperCAmelCase_    :      List[Any]            =						model(__snake_case  )
															UpperCAmelCase_    :      int            =						torch.nn.functional.mse_loss(__snake_case     ,       __snake_case  )
															accelerator.backward(__snake_case  )
															optimizer.step()
															optimizer.zero_grad()
										rands.append(random.random()  )  # Introduce some randomness
										if scheduler is not None:
															scheduler.step()
					return rands



class   lowerCamelCase      (nn.Module			):







		'''simple docstring'''



		def __init__(							self   )	->							Optional[Any]:
							super().__init__()
							UpperCAmelCase_    :      List[Any]            =						nn.Parameter(torch.randn(1   )   )
							UpperCAmelCase_    :      Optional[int]            =						nn.Parameter(torch.randn(1   )   )



		def 					__UpperCAmelCase			(							self				,		_UpperCamelCase   )	->							Optional[Any]:
							return x * self.a + self.b





class   lowerCamelCase      (unittest.TestCase			):







		'''simple docstring'''



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[int]            =						ProjectConfiguration(total_limit=1				,		project_dir=_UpperCamelCase				,		automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Dict            =						Accelerator(project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()

												# Save second state
												accelerator.save_state()
												self.assertEqual(len(os.listdir(accelerator.project_dir   )   )				,		1   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[Any]            =						DummyModel()
												UpperCAmelCase_    :      str            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[Any]            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												UpperCAmelCase_    :      Any            =						os.path.join(_UpperCamelCase				,		'initial'   )
												accelerator.save_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												UpperCAmelCase_    :      Union[str, Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Union[str, Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Any            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      int            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      str            =						dummy_dataloaders()
												UpperCAmelCase_    :      Optional[Any]            =						Accelerator()
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[str]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Dict            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												UpperCAmelCase_    :      Union[str, Any]            =						os.path.join(_UpperCamelCase				,		'checkpoint'   )
												accelerator.save_state(_UpperCamelCase   )

												# Load everything back in and make sure all states work
												accelerator.load_state(_UpperCamelCase   )
												test_rands += train(1				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Union[str, Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Tuple            =						DummyModel()
												UpperCAmelCase_    :      Optional[int]            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Optional[int]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Any            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )

												# Train baseline
												UpperCAmelCase_    :      str            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Optional[int]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()
												UpperCAmelCase_    :      Optional[Any]            =						train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      Tuple            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Optional[int]            =						optimizer.state_dict()

												# Train partially
												set_seed(4_2   )
												UpperCAmelCase_    :      Any            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Union[str, Any]            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(iteration=1				,		automatic_checkpoint_naming=_UpperCamelCase   )
												UpperCAmelCase_    :      List[Any]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      str            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      List[Any]            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )

												UpperCAmelCase_    :      Union[str, Any]            =						train(2				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save everything
												accelerator.save_state()

												# Load everything back in and make sure all states work
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_1'   )   )
												test_rands += train(1				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												((UpperCAmelCase_)       ,  (UpperCAmelCase_))    :      List[Any]            =						model.a.item(), model.b.item()
												UpperCAmelCase_    :      Dict            =						optimizer.state_dict()
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )
												self.assertEqual(_UpperCamelCase				,		_UpperCamelCase   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							UpperCAmelCase_    :      Optional[Any]            =						torch.tensor([1, 2, 3]   )
							UpperCAmelCase_    :      Any            =						torch.tensor([2, 3, 4]   )
							UpperCAmelCase_    :      Union[str, Any]            =						DummyModel()
							UpperCAmelCase_    :      List[str]            =						torch.optim.Adam(net.parameters()   )
							UpperCAmelCase_    :      Any            =						Accelerator()
							with self.assertRaises(_UpperCamelCase   ) as ve:
												accelerator.register_for_checkpointing(_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
							UpperCAmelCase_    :      Optional[int]            =						str(ve.exception   )
							self.assertTrue('Item at index 0' in message   )
							self.assertTrue('Item at index 1' in message   )
							self.assertFalse('Item at index 2' in message   )
							self.assertFalse('Item at index 3' in message   )



		def 					__UpperCAmelCase			(							self   )	->							int:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      int            =						DummyModel()
												UpperCAmelCase_    :      Any            =						torch.optim.Adam(params=model.parameters()				,		lr=1E-3   )
												UpperCAmelCase_    :      Dict            =						torch.optim.lr_scheduler.StepLR(_UpperCamelCase				,		step_size=1				,		gamma=0.99   )
												UpperCAmelCase_       ,  UpperCAmelCase_    :      Tuple            =						dummy_dataloaders()
												UpperCAmelCase_    :      Tuple            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase   )
												# Train baseline
												UpperCAmelCase_    :      Tuple            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_       ,  UpperCAmelCase_    :      Any            =						accelerator.prepare(
												    _UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												# Save initial
												accelerator.save_state()
												UpperCAmelCase_    :      Dict            =						scheduler.state_dict()
												train(3				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase				,		_UpperCamelCase   )
												self.assertNotEqual(_UpperCamelCase				,		scheduler.state_dict()   )

												# Load everything back in and make sure all states work
												accelerator.load_state(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )
												self.assertEqual(_UpperCamelCase				,		scheduler.state_dict()   )



		def 					__UpperCAmelCase			(							self   )	->							Dict:
							with tempfile.TemporaryDirectory() as tmpdir:
												set_seed(4_2   )
												UpperCAmelCase_    :      Optional[int]            =						DummyModel()
												UpperCAmelCase_    :      Dict            =						ProjectConfiguration(automatic_checkpoint_naming=_UpperCamelCase				,		total_limit=2   )
												# Train baseline
												UpperCAmelCase_    :      Optional[int]            =						Accelerator(project_dir=_UpperCamelCase				,		project_config=_UpperCamelCase   )
												UpperCAmelCase_    :      str            =						accelerator.prepare(_UpperCamelCase   )
												# Save 3 states:
												for _ in range(1_1   ):
																	accelerator.save_state()
												self.assertTrue(not os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_0'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_9'   )   )   )
												self.assertTrue(os.path.exists(os.path.join(_UpperCamelCase				,		'checkpoints'				,		'checkpoint_10'   )   )   )



		@require_cuda
		def 					__UpperCAmelCase			(							self   )	->							str:
							UpperCAmelCase_    :      List[str]            =						['torchrun', f"--nproc_per_node={torch.cuda.device_count()}", inspect.getfile(self.__class__   )]
							execute_subprocess_async(_UpperCamelCase				,		env=os.environ.copy()   )


if __name__ == "__main__":
		__UpperCAmelCase								=						'/tmp/accelerate/state_checkpointing'
		__UpperCAmelCase								=						DummyModel()
		__UpperCAmelCase								=						torch.optim.Adam(params=model.parameters(), lr=1E-3)
		__UpperCAmelCase								=						torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9_9)
		__UpperCAmelCase							,   __UpperCAmelCase								=						dummy_dataloaders()
		__UpperCAmelCase								=						ProjectConfiguration(automatic_checkpoint_naming=True)
		# Train baseline
		__UpperCAmelCase								=						Accelerator(project_dir=savedir, project_config=project_config, mixed_precision='no')
		if accelerator.process_index == 0:
				if os.path.exists(savedir):
						shutil.rmtree(savedir)
				os.makedirs(savedir)
		__UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase							,   __UpperCAmelCase								=						accelerator.prepare(
		    model, optimizer, train_dataloader, valid_dataloader, scheduler
		)
		__UpperCAmelCase							,   __UpperCAmelCase								=						accelerator.prepare(model, optimizer)
		train(3, model, train_dataloader, optimizer, accelerator, scheduler)
		# Check that the intial optimizer is loaded on the GPU
		for group in optimizer.param_groups:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert param_device.type == accelerator.device.type
		__UpperCAmelCase								=						model.cpu()
		accelerator.wait_for_everyone()
		accelerator.save_state()
		accelerator.wait_for_everyone()

		# Check CPU state
		accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='cpu')
		for group in optimizer.param_groups:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert (
		    param_device.type == torch.device('cpu').type
		), F"Loaded optimizer states did not match, expected to be loaded on the CPU but got {param_device}"

		# Check device state
		model.to(accelerator.device)
		accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='on_device')
		for group in optimizer.param_groups:
				__UpperCAmelCase								=						group['params'][0].device
				break
		assert (
		    param_device.type == accelerator.device.type
		), F"Loaded optimizer states did not match, expected to be loaded on {accelerator.device} but got {param_device}"

		# Check error
		with pytest.raises(TypeError, match='Unsupported optimizer map location passed'):
				accelerator.load_state(os.path.join(savedir, 'checkpoints', 'checkpoint_0'), map_location='invalid')
		accelerator.wait_for_everyone()
		if accelerator.process_index == 0:
				shutil.rmtree(savedir)
		accelerator.wait_for_everyone()






