



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
lowerCamelCase__     =	"""src/transformers"""

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


lowerCamelCase__     =	"""
{0} = None
"""

lowerCamelCase__     =	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

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


lowerCamelCase__     =	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[Any]      ):
    """simple docstring"""







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

def 					lowerCAmelCase__  (							):
    """simple docstring"""







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

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

    __a					       =						{}
    # 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.
        __a					       =						find_backend(lines[line_index]      )
        if backend is not None:
            while not lines[line_index].startswith("""    else:"""      ):
                line_index += 1
            line_index += 1

            __a					       =						[]
            # Until we unindent, add backend objects to the list
            while len(lines[line_index]      ) <= 1 or lines[line_index].startswith(""" """ * 8      ):
                __a					       =						lines[line_index]
                __a					       =						_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

            __a					       =						objects
        else:
            line_index += 1

    return backend_specific_objects

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Tuple			,       _SCREAMING_SNAKE_CASE    :     int      ):
    """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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     List[str]=None      ):
    """simple docstring"""







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

    for backend, objects in backend_specific_objects.items():
        __a					       =						"""[""" + """, """.join(f"\"{b}\"" for b in backend.split("""_and_"""      )      ) + """]"""
        __a					       =						"""# 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]      )
        __a					       =						dummy_file

    return dummy_files

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[Any]=False      ):
    """simple docstring"""







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

    # Locate actual dummy modules and read their content.
    __a					       =						os.path.join(_SCREAMING_SNAKE_CASE			,       """utils"""      )
    __a					       =						{
        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()
    }

    __a					       =						{}
    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:
                __a					       =						f.read()
        else:
            __a					       =						""""""

    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__":
      lowerCamelCase__     =	argparse.ArgumentParser()
      parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
      lowerCamelCase__     =	parser.parse_args()

      check_dummies(args.fix_and_overwrite)





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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Dict      ):
				"""simple docstring"""







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


lowerCamelCase__     =	logging.getLogger(__name__)






class 				SCREAMING_SNAKE_CASE     (							pl.Callback   ):





							def   UpperCamelCase_   (				self						:						Dict ,			__lowercase						:						Dict ,			__lowercase						:						str		):

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule ,			__lowercase						:						str ,			__lowercase						:						Tuple=True		):

											'''simple docstring'''
											logger.info(F"***** {type_path} results at step {trainer.global_step:05d} *****"		)
											__a					       =						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
											__a					       =						Path(pl_module.hparams.output_dir		)
											if type_path == "test":
															__a					       =						od / """test_results.txt"""
															__a					       =						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.
															__a					       =						od / F"{type_path}_results/{trainer.global_step:05d}.txt"
															__a					       =						od / F"{type_path}_generations/{trainer.global_step:05d}.txt"
															results_file.parent.mkdir(exist_ok=__lowercase		)
															generations_file.parent.mkdir(exist_ok=__lowercase		)
											with open(__lowercase ,			"""a+"""		) as writer:
															for key in sorted(__lowercase		):
																			if key in ["log", "progress_bar", "preds"]:
																							continue
																			__a					       =						metrics[key]
																			if isinstance(__lowercase ,			torch.Tensor		):
																							__a					       =						val.item()
																			__a					       =						F"{key}: {val:.6f}\n"
																			writer.write(__lowercase		)

											if not save_generations:
															return

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





							@rank_zero_only
							def   UpperCamelCase_   (				self						:						int ,			__lowercase						:						Union[str, Any] ,			__lowercase						:						List[Any]		):

											'''simple docstring'''
											try:
															__a					       =						pl_module.model.model.num_parameters()
											except AttributeError:
															__a					       =						pl_module.model.num_parameters()

											__a					       =						count_trainable_parameters(__lowercase		)
											# 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						:						Union[str, Any] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						pl.LightningModule		):

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







							@rank_zero_only
							def   UpperCamelCase_   (				self						:						Optional[int] ,			__lowercase						:						pl.Trainer ,			__lowercase						:						Dict		):

											'''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 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     Optional[int]			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]			,       _SCREAMING_SNAKE_CASE    :     Tuple=1			,       _SCREAMING_SNAKE_CASE    :     List[str]=False      ):
				"""simple docstring"""







				if metric == "rouge2":
								__a					       =						"""{val_avg_rouge2:.4f}-{step_count}"""
				elif metric == "bleu":
								__a					       =						"""{val_avg_bleu:.4f}-{step_count}"""
				elif metric == "loss":
								__a					       =						"""{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."""      )

				__a					       =						ModelCheckpoint(
				    dirpath=_SCREAMING_SNAKE_CASE			,       filename=_SCREAMING_SNAKE_CASE			,       monitor=f"val_{metric}"			,       mode="""min""" if """loss""" in metric else """max"""			,       save_top_k=_SCREAMING_SNAKE_CASE			,       )
				return checkpoint_callback

def 					lowerCAmelCase__  (							_SCREAMING_SNAKE_CASE    :     int			,       _SCREAMING_SNAKE_CASE    :     Optional[Any]      ):
				"""simple docstring"""







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