






import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only

from utils import save_json





def 						A_					(						lowercase_			)				->List[str]:







 """simple docstring"""

 SCREAMING_SNAKE_CASE				    =		filter(lambda lowercase_			: p.requires_grad	,  model.parameters()			)
 SCREAMING_SNAKE_CASE				    =		sum([np.prod(p.size()			) for p in model_parameters]			)
 return params


__UpperCAmelCase      =				logging.getLogger(__name__)
class 	a_(		pl.Callback	):



     """simple docstring"""



     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	List[Any])							->	Tuple:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		{f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
      pl_module.logger.log_metrics(lowerCAmelCase__)





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule  ,  lowerCAmelCase__					:	str  ,  lowerCAmelCase__					:	Optional[Any]=True)							->	None:

      """simple docstring"""
      logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****''')
      SCREAMING_SNAKE_CASE				    =		trainer.callback_metrics
      trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['log', 'progress_bar', 'preds']})
      # Log results
      SCREAMING_SNAKE_CASE				    =		Path(pl_module.hparams.output_dir)
      if type_path == "test":
       SCREAMING_SNAKE_CASE				    =		od / 'test_results.txt'
       SCREAMING_SNAKE_CASE				    =		od / 'test_generations.txt'
      else:
       # this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
       # If people want this it will be easy enough to add back.
       SCREAMING_SNAKE_CASE				    =		od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
       SCREAMING_SNAKE_CASE				    =		od / f'''{type_path}_generations/{trainer.global_step:05d}.txt'''
       results_file.parent.mkdir(exist_ok=lowerCAmelCase__)
       generations_file.parent.mkdir(exist_ok=lowerCAmelCase__)
      with open(lowerCAmelCase__  ,  'a+') as writer:
       for key in sorted(lowerCAmelCase__):
        if key in ["log", "progress_bar", "preds"]:
         continue
        SCREAMING_SNAKE_CASE				    =		metrics[key]
        if isinstance(lowerCAmelCase__  ,  torch.Tensor):
         SCREAMING_SNAKE_CASE				    =		val.item()
        SCREAMING_SNAKE_CASE				    =		f'''{key}: {val:.6f}\n'''
        writer.write(lowerCAmelCase__)

      if not save_generations:
       return

      if "preds" in metrics:
       SCREAMING_SNAKE_CASE				    =		'\n'.join(metrics['preds'])
       generations_file.open('w+').write(lowerCAmelCase__)





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	str)							->	Optional[int]:

      """simple docstring"""
      try:
       SCREAMING_SNAKE_CASE				    =		pl_module.model.model.num_parameters()
      except AttributeError:
       SCREAMING_SNAKE_CASE				    =		pl_module.model.num_parameters()

      SCREAMING_SNAKE_CASE				    =		count_trainable_parameters(lowerCAmelCase__)
      # mp stands for million parameters
      trainer.logger.log_metrics({'n_params': npars, 'mp': npars / 1e6, 'grad_mp': n_trainable_pars / 1e6})





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	List[str]  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	pl.LightningModule)							->	int:

      """simple docstring"""
      save_json(pl_module.metrics  ,  pl_module.metrics_save_path)
      return self._write_logs(lowerCAmelCase__  ,  lowerCAmelCase__  ,  'test')





     @rank_zero_only
     def     __UpperCamelCase						(		self					:	str  ,  lowerCAmelCase__					:	pl.Trainer  ,  lowerCAmelCase__					:	Dict)							->	List[Any]:

      """simple docstring"""
      save_json(pl_module.metrics  ,  pl_module.metrics_save_path)
      # Uncommenting this will save val generations
      # return self._write_logs(trainer, pl_module, "valid")





def 						A_					(						lowercase_	,  lowercase_	,  lowercase_=1	,  lowercase_=False			)				->Optional[Any]:







 """simple docstring"""

 if metric == "rouge2":
  SCREAMING_SNAKE_CASE				    =		'{val_avg_rouge2:.4f}-{step_count}'
 elif metric == "bleu":
  SCREAMING_SNAKE_CASE				    =		'{val_avg_bleu:.4f}-{step_count}'
 elif metric == "loss":
  SCREAMING_SNAKE_CASE				    =		'{val_avg_loss:.4f}-{step_count}'
 else:
  raise NotImplementedError(
      f'''seq2seq callbacks only support rouge2, bleu and loss, got {metric}, You can make your own by adding to'''
      ' this function.'			)

 SCREAMING_SNAKE_CASE				    =		ModelCheckpoint(
     dirpath=lowercase_	,  filename=lowercase_	,  monitor=f'''val_{metric}'''	,  mode='min' if 'loss' in metric else 'max'	,  save_top_k=lowercase_	,  )
 return checkpoint_callback





def 						A_					(						lowercase_	,  lowercase_			)				->Tuple:







 """simple docstring"""

 return EarlyStopping(
     monitor=f'''val_{metric}'''	,  mode='min' if 'loss' in metric else 'max'	,  patience=lowercase_	,  verbose=lowercase_	,  )








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
__UpperCamelCase							:							Optional[Any]    							=					"src/transformers"

# Matches is_xxx_available()
__UpperCamelCase							:							str    							=					re.compile(r"is\_([a-z_]*)_available()")
# Matches from xxx import bla
__UpperCamelCase							:							Union[str, Any]    							=					re.compile(r"\s+from\s+\S*\s+import\s+([^\(\s].*)\n")
__UpperCamelCase							:							Dict    							=					re.compile(r"^\s+if\s+not\s+\(?is\_[a-z_]*\_available\(\)")


__UpperCamelCase							:							Any    							=					"\n{0} = None\n"

__UpperCamelCase							:							Tuple    							=					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


__UpperCamelCase							:							Optional[Any]    							=					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"

def       _a	(					SCREAMING_SNAKE_CASE						:					str  ):







 """simple docstring"""



 if _re_test_backend.search(SCREAMING_SNAKE_CASE  ) is None:
  return None
 UpperCamelCase__       :     List[str] 							= [b[0] for b in _re_backend.findall(SCREAMING_SNAKE_CASE  )]
 backends.sort()
 return "_and_".join(SCREAMING_SNAKE_CASE  )

def       _a	(					):







 """simple docstring"""



 with open(os.path.join(SCREAMING_SNAKE_CASE     ,			'''__init__.py'''  )     ,			'''r'''     ,			encoding='''utf-8'''     ,			newline='''\n'''  ) as f:
  UpperCamelCase__       :     Union[str, Any] 							= f.readlines()

 # Get to the point we do the actual imports for type checking
 UpperCamelCase__       :     int 							= 0
 while not lines[line_index].startswith('''if TYPE_CHECKING'''  ):
  line_index += 1

 UpperCamelCase__       :     Optional[Any] 							= {}
 # Go through the end of the file
 while line_index < len(SCREAMING_SNAKE_CASE  ):
  # If the line is an if is_backend_available, we grab all objects associated.
  UpperCamelCase__       :     Any 							= find_backend(lines[line_index]  )
  if backend is not None:
   while not lines[line_index].startswith('''    else:'''  ):
    line_index += 1
   line_index += 1

   UpperCamelCase__       :     List[str] 							= []
   # Until we unindent, add backend objects to the list
   while len(lines[line_index]  ) <= 1 or lines[line_index].startswith(''' ''' * 8  ):
    UpperCamelCase__       :     str 							= lines[line_index]
    UpperCamelCase__       :     Union[str, Any] 							= _re_single_line_import.search(SCREAMING_SNAKE_CASE  )
    if single_line_import_search is not None:
     objects.extend(single_line_import_search.groups()[0].split(''', '''  )  )
    elif line.startswith(''' ''' * 12  ):
     objects.append(line[12:-2]  )
    line_index += 1

   UpperCamelCase__       :     Optional[int] 							= objects
  else:
   line_index += 1

 return backend_specific_objects

def       _a	(					SCREAMING_SNAKE_CASE						:					str     ,			SCREAMING_SNAKE_CASE						:					List[str]  ):







 """simple docstring"""



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

def       _a	(					SCREAMING_SNAKE_CASE						:					Dict=None  ):







 """simple docstring"""



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

 for backend, objects in backend_specific_objects.items():
  UpperCamelCase__       :     int 							= '''[''' + ''', '''.join(F"\"{b}\"" for b in backend.split('''_and_'''  )  ) + ''']'''
  UpperCamelCase__       :     Any 							= '''# 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(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  ) for o in objects]  )
  UpperCamelCase__       :     List[str] 							= dummy_file

 return dummy_files

def       _a	(					SCREAMING_SNAKE_CASE						:					Union[str, Any]=False  ):







 """simple docstring"""



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

 # Locate actual dummy modules and read their content.
 UpperCamelCase__       :     Optional[Any] 							= os.path.join(SCREAMING_SNAKE_CASE     ,			'''utils'''  )
 UpperCamelCase__       :     int 							= {
     backend: os.path.join(SCREAMING_SNAKE_CASE     ,			F"dummy_{short_names.get(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )}_objects.py"  )
     for backend in dummy_files.keys()
 }

 UpperCamelCase__       :     Optional[Any] 							= {}
 for backend, file_path in dummy_file_paths.items():
  if os.path.isfile(SCREAMING_SNAKE_CASE  ):
   with open(SCREAMING_SNAKE_CASE     ,			'''r'''     ,			encoding='''utf-8'''     ,			newline='''\n'''  ) as f:
    UpperCamelCase__       :     List[Any] 							= f.read()
  else:
   UpperCamelCase__       :     List[Any] 							= ''''''

 for backend in dummy_files.keys():
  if dummy_files[backend] != actual_dummies[backend]:
   if overwrite:
    print(
        F"Updating transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )}_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"transformers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE     ,			SCREAMING_SNAKE_CASE  )}_objects.py. Run `make fix-copies` "
        '''to fix this.'''  )


if __name__ == "__main__":
       __UpperCamelCase							:							Optional[int]    							=					argparse.ArgumentParser()
       parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
       __UpperCamelCase							:							str    							=					parser.parse_args()

       check_dummies(args.fix_and_overwrite)


