
'''simple docstring'''



import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict

import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info

from transformers import (
    AdamW,
    AutoConfig,
    AutoModel,
    AutoModelForPreTraining,
    AutoModelForQuestionAnswering,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoModelForTokenClassification,
    AutoModelWithLMHead,
    AutoTokenizer,
    PretrainedConfig,
    PreTrainedTokenizer,
)
from transformers.optimization import (
    Adafactor,
    get_cosine_schedule_with_warmup,
    get_cosine_with_hard_restarts_schedule_with_warmup,
    get_linear_schedule_with_warmup,
    get_polynomial_decay_schedule_with_warmup,
)
from transformers.utils.versions import require_version


__A        =     logging.getLogger(__name__)

require_version("pytorch_lightning>=1.0.4")

__A        =     {
    "base": AutoModel,
    "sequence-classification": AutoModelForSequenceClassification,
    "question-answering": AutoModelForQuestionAnswering,
    "pretraining": AutoModelForPreTraining,
    "token-classification": AutoModelForTokenClassification,
    "language-modeling": AutoModelWithLMHead,
    "summarization": AutoModelForSeqaSeqLM,
    "translation": AutoModelForSeqaSeqLM,
}


# update this and the import above to support new schedulers from transformers.optimization
__A        =     {
    "linear": get_linear_schedule_with_warmup,
    "cosine": get_cosine_schedule_with_warmup,
    "cosine_w_restarts": get_cosine_with_hard_restarts_schedule_with_warmup,
    "polynomial": get_polynomial_decay_schedule_with_warmup,
    # '': get_constant_schedule,             # not supported for now
    # '': get_constant_schedule_with_warmup, # not supported for now
}
__A        =     sorted(arg_to_scheduler.keys())
__A        =     "{" + ", ".join(arg_to_scheduler_choices) + "}"





class       A				(							pl.LightningModule					):



			def __init__(       self							,  lowerCamelCase__							,  lowerCamelCase__=None							,  lowerCamelCase__="base"							,  lowerCamelCase__=None							,  lowerCamelCase__=None							,  lowerCamelCase__=None							,  **lowerCamelCase__							,  )      ->		Optional[int]:




									'''simple docstring'''



									super().__init__()
									# TODO: move to self.save_hyperparameters()
									# self.save_hyperparameters()
									# can also expand arguments into trainer signature for easier reading

									self.save_hyperparameters(A_							)
									lowercase__							=							0
									lowercase__							=							Path(self.hparams.output_dir							)
									lowercase__							=							self.hparams.cache_dir if self.hparams.cache_dir else None
									if config is None:
															lowercase__							=							AutoConfig.from_pretrained(
															    self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path							,  **({"""num_labels""": num_labels} if num_labels is not None else {})							,  cache_dir=A_							,  **A_							,  )
									else:
															lowercase__							=							config

									lowercase__							=							("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
									for p in extra_model_params:
															if getattr(self.hparams							,  A_							,  A_							):
																					assert hasattr(self.config							,  A_							), F'''model config doesn\'t have a `{p}` attribute'''
																					setattr(self.config							,  A_							,  getattr(self.hparams							,  A_							)							)

									if tokenizer is None:
															lowercase__							=							AutoTokenizer.from_pretrained(
															    self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path							,  cache_dir=A_							,  )
									else:
															lowercase__							=							tokenizer
									lowercase__							=							MODEL_MODES[mode]
									if model is None:
															lowercase__							=							self.model_type.from_pretrained(
															    self.hparams.model_name_or_path							,  from_tf=bool(""".ckpt""" in self.hparams.model_name_or_path							)							,  config=self.config							,  cache_dir=A_							,  )
									else:
															lowercase__							=							model



			def A__			(       self							,  *lowerCamelCase__							,  **lowerCamelCase__							)      ->		Optional[int]:




									'''simple docstring'''



									lowercase__							=							self.model_type.from_pretrained(*A_							,  **A_							)



			def A__			(       self							)      ->		Optional[Any]:




									'''simple docstring'''



									lowercase__							=							arg_to_scheduler[self.hparams.lr_scheduler]
									lowercase__							=							get_schedule_func(
									    self.opt							,  num_warmup_steps=self.hparams.warmup_steps							,  num_training_steps=self.total_steps()							)
									lowercase__							=							{"""scheduler""": scheduler, """interval""": """step""", """frequency""": 1}
									return scheduler



			def A__			(       self							)      ->		Tuple:




									'''simple docstring'''



									lowercase__							=							self.model
									lowercase__							=							["""bias""", """LayerNorm.weight"""]
									lowercase__							=							[
									    {
									        """params""": [
									            p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay							)
									        ],  # check this named paramters
									        """weight_decay""": self.hparams.weight_decay,
									    },
									    {
									        """params""": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay							)],
									        """weight_decay""": 0.0,
									    },
									]
									if self.hparams.adafactor:
															lowercase__							=							Adafactor(
															    A_							,  lr=self.hparams.learning_rate							,  scale_parameter=A_							,  relative_step=A_							)

									else:
															lowercase__							=							AdamW(
															    A_							,  lr=self.hparams.learning_rate							,  eps=self.hparams.adam_epsilon							)
									lowercase__							=							optimizer

									lowercase__							=							self.get_lr_scheduler()

									return [optimizer], [scheduler]



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							)      ->		Optional[Any]:




									'''simple docstring'''



									return self.validation_step(A_							,  A_							)



			def A__			(       self							,  lowerCamelCase__							)      ->		str:




									'''simple docstring'''



									return self.validation_end(A_							)



			def A__			(       self							)      ->		List[Any]:




									'''simple docstring'''



									lowercase__							=							max(1							,  self.hparams.gpus							)  # TODO: consider num_tpu_cores
									lowercase__							=							self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
									return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs



			def A__			(       self							,  lowerCamelCase__							)      ->		Tuple:




									'''simple docstring'''



									if stage == "test":
															lowercase__							=							len(self.test_dataloader().dataset							)
									else:
															lowercase__							=							self.get_dataloader("""train"""							,  self.hparams.train_batch_size							,  shuffle=A_							)
															lowercase__							=							len(self.train_dataloader().dataset							)



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							,  lowerCamelCase__ = False							)      ->		List[Any]:




									'''simple docstring'''



									raise NotImplementedError("""You must implement this for your task"""							)



			def A__			(       self							)      ->		List[str]:




									'''simple docstring'''



									return self.train_loader



			def A__			(       self							)      ->		Optional[Any]:




									'''simple docstring'''



									return self.get_dataloader("""dev"""							,  self.hparams.eval_batch_size							,  shuffle=A_							)



			def A__			(       self							)      ->		List[Any]:




									'''simple docstring'''



									return self.get_dataloader("""test"""							,  self.hparams.eval_batch_size							,  shuffle=A_							)



			def A__			(       self							,  lowerCamelCase__							)      ->		Tuple:




									'''simple docstring'''



									return os.path.join(
									    self.hparams.data_dir							,  """cached_{}_{}_{}""".format(
									        A_							,  list(filter(A_							,  self.hparams.model_name_or_path.split("""/"""							)							)							).pop()							,  str(self.hparams.max_seq_length							)							,  )							,  )



			@pl.utilities.rank_zero_only
			def A__			(       self							,  lowerCamelCase__							)      ->		str:




									'''simple docstring'''



									lowercase__							=							self.output_dir.joinpath("""best_tfmr"""							)
									lowercase__							=							self.step_count
									self.model.save_pretrained(A_							)
									self.tokenizer.save_pretrained(A_							)



			@staticmethod
			def A__			(       lowerCamelCase__							,  lowerCamelCase__							)      ->		Any:




									'''simple docstring'''



									parser.add_argument(
									    """--model_name_or_path"""							,  default=A_							,  type=A_							,  required=A_							,  help="""Path to pretrained model or model identifier from huggingface.co/models"""							,  )
									parser.add_argument(
									    """--config_name"""							,  default=""""""							,  type=A_							,  help="""Pretrained config name or path if not the same as model_name"""							)
									parser.add_argument(
									    """--tokenizer_name"""							,  default=A_							,  type=A_							,  help="""Pretrained tokenizer name or path if not the same as model_name"""							,  )
									parser.add_argument(
									    """--cache_dir"""							,  default=str(Path(A_							).parent / """test_run""" / """cache"""							)							,  type=A_							,  help="""Where do you want to store the pre-trained models downloaded from huggingface.co"""							,  )
									parser.add_argument(
									    """--encoder_layerdrop"""							,  type=A_							,  help="""Encoder layer dropout probability (Optional). Goes into model.config"""							,  )
									parser.add_argument(
									    """--decoder_layerdrop"""							,  type=A_							,  help="""Decoder layer dropout probability (Optional). Goes into model.config"""							,  )
									parser.add_argument(
									    """--dropout"""							,  type=A_							,  help="""Dropout probability (Optional). Goes into model.config"""							,  )
									parser.add_argument(
									    """--attention_dropout"""							,  type=A_							,  help="""Attention dropout probability (Optional). Goes into model.config"""							,  )
									parser.add_argument("""--learning_rate"""							,  default=5e-5							,  type=A_							,  help="""The initial learning rate for Adam."""							)
									parser.add_argument(
									    """--lr_scheduler"""							,  default="""linear"""							,  choices=A_							,  metavar=A_							,  type=A_							,  help="""Learning rate scheduler"""							,  )
									parser.add_argument("""--weight_decay"""							,  default=0.0							,  type=A_							,  help="""Weight decay if we apply some."""							)
									parser.add_argument("""--adam_epsilon"""							,  default=1e-8							,  type=A_							,  help="""Epsilon for Adam optimizer."""							)
									parser.add_argument("""--warmup_steps"""							,  default=0							,  type=A_							,  help="""Linear warmup over warmup_steps."""							)
									parser.add_argument("""--num_workers"""							,  default=4							,  type=A_							,  help="""kwarg passed to DataLoader"""							)
									parser.add_argument("""--num_train_epochs"""							,  dest="""max_epochs"""							,  default=3							,  type=A_							)
									parser.add_argument("""--train_batch_size"""							,  default=32							,  type=A_							)
									parser.add_argument("""--eval_batch_size"""							,  default=32							,  type=A_							)
									parser.add_argument("""--adafactor"""							,  action="""store_true"""							)





class       A				(							pl.Callback					):



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							)      ->		Dict:




									'''simple docstring'''



									if (
									    trainer.is_global_zero and trainer.global_rank == 0
									):  # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
															pl_module.model.rag.retriever.init_retrieval()  # better to use hook functions.





class       A				(							pl.Callback					):



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							)      ->		Union[str, Any]:




									'''simple docstring'''



									for name, param in pl_module.model.rag.named_parameters():
															if param.grad is None:
																					print(A_							)





class       A				(							pl.Callback					):



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							)      ->		Optional[int]:




									'''simple docstring'''



									lowercase__							=							trainer.lr_schedulers[0]["""scheduler"""]
									lowercase__							=							{F'''lr_group_{i}''': lr for i, lr in enumerate(lr_scheduler.get_lr()							)}
									pl_module.logger.log_metrics(A_							)



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							)      ->		Tuple:




									'''simple docstring'''



									rank_zero_info("""***** Validation results *****"""							)
									lowercase__							=							trainer.callback_metrics
									# Log results
									for key in sorted(A_							):
															if key not in ["log", "progress_bar"]:
																					rank_zero_info("""{} = {}\n""".format(A_							,  str(metrics[key]							)							)							)



			def A__			(       self							,  lowerCamelCase__							,  lowerCamelCase__							)      ->		List[str]:




									'''simple docstring'''



									rank_zero_info("""***** Test results *****"""							)
									lowercase__							=							trainer.callback_metrics
									# Log and save results to file
									lowercase__							=							os.path.join(pl_module.hparams.output_dir							,  """test_results.txt"""							)
									with open(A_							,  """w"""							) as writer:
															for key in sorted(A_							):
																					if key not in ["log", "progress_bar"]:
																											rank_zero_info("""{} = {}\n""".format(A_							,  str(metrics[key]							)							)							)
																											writer.write("""{} = {}\n""".format(A_							,  str(metrics[key]							)							)							)





def 			_A		(				lowercase__		,		lowercase__						):
						parser.add_argument(
						    """--output_dir"""		,		default=str(Path(_lowercase						).parent / """test_run""" / """model_checkpoints"""						)		,		type=_lowercase		,		help="""The output directory where the model predictions and checkpoints will be written."""		,		)
						parser.add_argument(
						    """--fp16"""		,		action="""store_true"""		,		help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""		,		)

						parser.add_argument(
						    """--fp16_opt_level"""		,		type=_lowercase		,		default="""O2"""		,		help=(
						        """For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']."""
						        """See details at https://nvidia.github.io/apex/amp.html"""
						    )		,		)
						parser.add_argument("""--n_tpu_cores"""		,		dest="""tpu_cores"""		,		type=_lowercase						)
						parser.add_argument("""--max_grad_norm"""		,		dest="""gradient_clip_val"""		,		default=1.0		,		type=_lowercase		,		help="""Max gradient norm"""						)
						parser.add_argument("""--do_train"""		,		action="""store_true"""		,		help="""Whether to run training."""						)
						parser.add_argument("""--do_predict"""		,		action="""store_true"""		,		help="""Whether to run predictions on the test set."""						)
						parser.add_argument(
						    """--gradient_accumulation_steps"""		,		dest="""accumulate_grad_batches"""		,		type=_lowercase		,		default=1		,		help="""Number of updates steps to accumulate before performing a backward/update pass."""		,		)
						parser.add_argument("""--seed"""		,		type=_lowercase		,		default=42		,		help="""random seed for initialization"""						)
						parser.add_argument(
						    """--data_dir"""		,		default=str(Path(_lowercase						).parent / """test_run""" / """dummy-train-data"""						)		,		type=_lowercase		,		help="""The input data dir. Should contain the training files for the CoNLL-2003 NER task."""		,		)







def 			_A		(				lowercase__		,		lowercase__		,		lowercase__=None		,		lowercase__=True		,		lowercase__=[]		,		lowercase__=None		,		lowercase__=None		,		**lowercase__		,		):
						pl.seed_everything(args.seed						)

						# init model
						lowercase__							=							Path(model.hparams.output_dir						)
						odir.mkdir(exist_ok=_lowercase						)

						# add custom checkpoints
						if checkpoint_callback is None:
												lowercase__							=							pl.callbacks.ModelCheckpoint(
												    filepath=args.output_dir		,		prefix="""checkpoint"""		,		monitor="""val_loss"""		,		mode="""min"""		,		save_top_k=1						)
						if early_stopping_callback:
												extra_callbacks.append(_lowercase						)
						if logging_callback is None:
												lowercase__							=							LoggingCallback()

						lowercase__							=							{}

						if args.fpaa:
												lowercase__							=							16

						if args.gpus > 1:
												lowercase__							=							"""auto"""
												lowercase__							=							"""ddp"""

						lowercase__							=							args.accumulate_grad_batches
						lowercase__							=							None
						lowercase__							=							"""auto"""

						lowercase__							=							pl.Trainer.from_argparse_args(
						    _lowercase		,		weights_summary=_lowercase		,		callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback]		,		logger=_lowercase		,		val_check_interval=1		,		num_sanity_val_steps=2		,		**_lowercase		,		)

						if args.do_train:
												trainer.fit(_lowercase						)

						else:
												print("""RAG modeling tests with new set functions successfuly executed!"""						)
						return trainer










import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
__snake_case   							=						'''src/diffusers'''

# Matches is_xxx_available()
__snake_case   							=						re.compile(r'''is\_([a-z_]*)_available\(\)''')
# Matches from xxx import bla
__snake_case   							=						re.compile(r'''\s+from\s+\S*\s+import\s+([^\(\s].*)\n''')


__snake_case   							=						'''
{0} = None
'''

__snake_case   							=						'''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
'''


__snake_case   							=						'''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''






def 		_A		(		_lowercase )					-> int:


     """simple docstring"""


     __UpperCamelCase =			_re_backend.findall(_lowercase )
     if len(_lowercase ) == 0:
          return None

     return "_and_".join(_lowercase )






def 		_A		(		)					-> Tuple:


     """simple docstring"""


     with open(os.path.join(_lowercase					,  '__init__.py' )					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
          __UpperCamelCase =			f.readlines()

     # Get to the point we do the actual imports for type checking
     __UpperCamelCase =			0
     __UpperCamelCase =			{}
     # Go through the end of the file
     while line_index < len(_lowercase ):
          # If the line contains is_backend_available, we grab all objects associated with the `else` block
          __UpperCamelCase =			find_backend(lines[line_index] )
          if backend is not None:
               while not lines[line_index].startswith('else:' ):
                    line_index += 1
               line_index += 1
               __UpperCamelCase =			[]
               # Until we unindent, add backend objects to the list
               while line_index < len(_lowercase ) and len(lines[line_index] ) > 1:
                    __UpperCamelCase =			lines[line_index]
                    __UpperCamelCase =			_re_single_line_import.search(_lowercase )
                    if single_line_import_search is not None:
                         objects.extend(single_line_import_search.groups()[0].split(', ' ) )
                    elif line.startswith(' ' * 8 ):
                         objects.append(line[8:-2] )
                    line_index += 1

               if len(_lowercase ) > 0:
                    __UpperCamelCase =			objects
          else:
               line_index += 1

     return backend_specific_objects






def 		_A		(		_lowercase					,  _lowercase )					-> Union[str, Any]:


     """simple docstring"""


     if name.isupper():
          return DUMMY_CONSTANT.format(_lowercase )
     elif name.islower():
          return DUMMY_FUNCTION.format(_lowercase					,  _lowercase )
     else:
          return DUMMY_CLASS.format(_lowercase					,  _lowercase )






def 		_A		(		_lowercase=None )					-> Optional[Any]:


     """simple docstring"""


     if backend_specific_objects is None:
          __UpperCamelCase =			read_init()
     # For special correspondence backend to module name as used in the function requires_modulename
     __UpperCamelCase =			{}

     for backend, objects in backend_specific_objects.items():
          __UpperCamelCase =			'[' + ', '.join(f'''"{b}"''' for b in backend.split('_and_' ) ) + ']'
          __UpperCamelCase =			'# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
          dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
          dummy_file += "\n".join([create_dummy_object(_lowercase					,  _lowercase ) for o in objects] )
          __UpperCamelCase =			dummy_file

     return dummy_files






def 		_A		(		_lowercase=False )					-> List[str]:


     """simple docstring"""


     __UpperCamelCase =			create_dummy_files()
     # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
     __UpperCamelCase =			{'torch': 'pt'}

     # Locate actual dummy modules and read their content.
     __UpperCamelCase =			os.path.join(_lowercase					,  'utils' )
     __UpperCamelCase =			{
         backend: os.path.join(_lowercase					,  f'''dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py''' )
         for backend in dummy_files.keys()
     }

     __UpperCamelCase =			{}
     for backend, file_path in dummy_file_paths.items():
          if os.path.isfile(_lowercase ):
               with open(_lowercase					,  'r'					,  encoding='utf-8'					,  newline='\n' ) as f:
                    __UpperCamelCase =			f.read()
          else:
               __UpperCamelCase =			''

     for backend in dummy_files.keys():
          if dummy_files[backend] != actual_dummies[backend]:
               if overwrite:
                    print(
                        f'''Updating diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py as the main '''
                        '__init__ has new objects.' )
                    with open(dummy_file_paths[backend]					,  'w'					,  encoding='utf-8'					,  newline='\n' ) as f:
                         f.write(dummy_files[backend] )
               else:
                    raise ValueError(
                        'The main __init__ has objects that are not present in '
                        f'''diffusers.utils.dummy_{short_names.get(_lowercase					,  _lowercase )}_objects.py. Run `make fix-copies` '''
                        'to fix this.' )


if __name__ == "__main__":
    __snake_case   							=						argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    __snake_case   							=						parser.parse_args()

    check_dummies(args.fix_and_overwrite)


