




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            = "\n{0} = None\n"

_snake_case            = "\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n"


_snake_case            = "\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"







def        lowerCAmelCase_			(  snake_case_  ):
      _A							:  List[Any]    =		_re_backend.findall(snake_case_  )
      if len(snake_case_  ) == 0:
            return None

      return "_and_".join(snake_case_  )







def        lowerCAmelCase_			(  ):
      with open(os.path.join(snake_case_,"""__init__.py"""  ),"""r""",encoding="""utf-8""",newline="""\n"""  ) as f:
            _A							:  Optional[int]    =		f.readlines()

      # Get to the point we do the actual imports for type checking
      _A							:  Any    =		0
      _A							:  Optional[int]    =		{}
      # Go through the end of the file
      while line_index < len(snake_case_  ):
            # If the line contains is_backend_available, we grab all objects associated with the `else` block
            _A							:  str    =		find_backend(lines[line_index]  )
            if backend is not None:
                  while not lines[line_index].startswith("""else:"""  ):
                        line_index += 1
                  line_index += 1
                  _A							:  Any    =		[]
                  # Until we unindent, add backend objects to the list
                  while line_index < len(snake_case_  ) and len(lines[line_index]  ) > 1:
                        _A							:  Optional[Any]    =		lines[line_index]
                        _A							:  Tuple    =		_re_single_line_import.search(snake_case_  )
                        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(snake_case_  ) > 0:
                        _A							:  Any    =		objects
            else:
                  line_index += 1

      return backend_specific_objects







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







def        lowerCAmelCase_			(  snake_case_=None  ):
      if backend_specific_objects is None:
            _A							:  Dict    =		read_init()
      # For special correspondence backend to module name as used in the function requires_modulename
      _A							:  List[Any]    =		{}

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

      return dummy_files







def        lowerCAmelCase_			(  snake_case_=False  ):
      _A							:  Dict    =		create_dummy_files()
      # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
      _A							:  Optional[int]    =		{"""torch""": """pt"""}

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

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

      for backend in dummy_files.keys():
            if dummy_files[backend] != actual_dummies[backend]:
                  if overwrite:
                        print(
                            f'''Updating diffusers.utils.dummy_{short_names.get(snake_case_,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'''diffusers.utils.dummy_{short_names.get(snake_case_,snake_case_  )}_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)







from typing import Any, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler

from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
    Adafactor,
    AdamW,
    get_constant_schedule,
    get_constant_schedule_with_warmup,
    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.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available


if is_fairscale_available():
  from fairscale.optim import OSS


_snake_case            = logging.get_logger(__name__)

_snake_case            = {
    "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,
    "constant": get_constant_schedule,
    "constant_w_warmup": get_constant_schedule_with_warmup,
}







class 		lowercase							(     UpperCamelCase__	):

     def __init__(						self	,  _a=None	,  _a=None	,  *_a	,  **_a       )		->							Optional[int]:
           super().__init__(*_a	,  **_a       )

           if config is None:
                 assert isinstance(self.model	,  _a       ), (
                     "If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
                     F''' {self.model.__class__}'''
                 )
                 _A							:  Optional[Any]    =		self.model.config
           else:
                 _A							:  int    =		config

           _A							:  Optional[Any]    =		data_args
           _A							:  int    =		self.config.tgt_vocab_size if isinstance(self.config	,  _a       ) else self.config.vocab_size

           if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
                 assert self.config.pad_token_id is not None, (
                     "Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
                     " calculation or doing label smoothing."
                 )

           if self.config.pad_token_id is None and self.config.eos_token_id is not None:
                 logger.warning(
                     F'''The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for'''
                     """ padding.."""       )

           if self.args.label_smoothing == 0:
                 _A							:  Optional[Any]    =		torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id       )
           else:
                 # dynamically import label_smoothed_nll_loss
                 from utils import label_smoothed_nll_loss

                 _A							:  Union[str, Any]    =		label_smoothed_nll_loss

     def       a__			(						self	,  _a       )		->							int:
           if self.optimizer is None:
                 _A							:  List[str]    =		["""bias""", """LayerNorm.weight"""]
                 _A							:  str    =		[
                     {
                         """params""": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay       )],
                         """weight_decay""": self.args.weight_decay,
                     },
                     {
                         """params""": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay       )],
                         """weight_decay""": 0.0,
                     },
                 ]
                 _A							:  Optional[Any]    =		Adafactor if self.args.adafactor else AdamW
                 if self.args.adafactor:
                       _A							:  Dict    =		Adafactor
                       _A							:  int    =		{"""scale_parameter""": False, """relative_step""": False}
                 else:
                       _A							:  int    =		AdamW
                       _A							:  Any    =		{
                           """betas""": (self.args.adam_betaa, self.args.adam_betaa),
                           """eps""": self.args.adam_epsilon,
                       }
                 _A							:  List[str]    =		self.args.learning_rate
                 if self.sharded_ddp:
                       _A							:  List[str]    =		OSS(
                           params=_a	,  optim=_a	,  **_a	,  )
                 else:
                       _A							:  Tuple    =		optimizer_cls(_a	,  **_a       )

           if self.lr_scheduler is None:
                 _A							:  Union[str, Any]    =		self._get_lr_scheduler(_a       )
           else:  # ignoring --lr_scheduler
                 logger.warning("""scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored."""       )

     def       a__			(						self	,  _a       )		->							Dict:
           _A							:  List[Any]    =		arg_to_scheduler[self.args.lr_scheduler]
           if self.args.lr_scheduler == "constant":
                 _A							:  Optional[Any]    =		schedule_func(self.optimizer       )
           elif self.args.lr_scheduler == "constant_w_warmup":
                 _A							:  Optional[int]    =		schedule_func(self.optimizer	,  num_warmup_steps=self.args.warmup_steps       )
           else:
                 _A							:  List[Any]    =		schedule_func(
                     self.optimizer	,  num_warmup_steps=self.args.warmup_steps	,  num_training_steps=_a       )
           return scheduler

     def       a__			(						self       )		->							Optional[torch.utils.data.Sampler]:
           if isinstance(self.train_dataset	,  torch.utils.data.IterableDataset       ):
                 return None
           elif is_torch_tpu_available():
                 return get_tpu_sampler(self.train_dataset       )
           else:
                 if self.args.sortish_sampler:
                       self.train_dataset.make_sortish_sampler(
                           self.args.per_device_train_batch_size	,  distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED)	,  )

                 return (
                     RandomSampler(self.train_dataset       )
                     if self.args.local_rank == -1
                     else DistributedSampler(self.train_dataset       )
                 )

     def       a__			(						self	,  _a	,  _a	,  _a       )		->							List[str]:
           if self.args.label_smoothing == 0:
                 if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
                       # force training to ignore pad token
                       _A							:  List[str]    =		model(**_a	,  use_cache=_a       )[0]
                       _A							:  Dict    =		self.loss_fn(logits.view(-1	,  logits.shape[-1]       )	,  labels.view(-1       )       )
                 else:
                       # compute usual loss via models
                       _A    ,     _A							:  str    =		model(**_a	,  labels=_a	,  use_cache=_a       )[:2]
           else:
                 # compute label smoothed loss
                 _A							:  Any    =		model(**_a	,  use_cache=_a       )[0]
                 _A							:  Union[str, Any]    =		torch.nn.functional.log_softmax(_a	,  dim=-1       )
                 _A    ,     _A							:  List[str]    =		self.loss_fn(_a	,  _a	,  self.args.label_smoothing	,  ignore_index=self.config.pad_token_id       )
           return loss, logits

     def       a__			(						self	,  _a	,  _a       )		->							List[Any]:
           _A							:  Optional[int]    =		inputs.pop("""labels"""       )
           _A    ,     _A							:  Dict    =		self._compute_loss(_a	,  _a	,  _a       )
           return loss

     def       a__			(						self	,  _a	,  _a	,  _a	,  _a = None	,  )		->							Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:
           _A							:  int    =		self._prepare_inputs(_a       )

           _A							:  Dict    =		{
               """max_length""": self.data_args.val_max_target_length
               if self.data_args is not None
               else self.config.max_length,
               """num_beams""": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
           }

           if self.args.predict_with_generate and not self.args.prediction_loss_only:
                 _A							:  List[str]    =		self.model.generate(
                     inputs["""input_ids"""]	,  attention_mask=inputs["""attention_mask"""]	,  **_a	,  )
                 # in case the batch is shorter than max length, the output should be padded
                 if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
                       _A							:  str    =		self._pad_tensors_to_max_len(_a	,  gen_kwargs["""max_length"""]       )

           _A							:  Any    =		inputs.pop("""labels"""       )
           with torch.no_grad():
                 # compute loss on predict data
                 _A    ,     _A							:  Tuple    =		self._compute_loss(_a	,  _a	,  _a       )

           _A							:  Any    =		loss.mean().detach()
           if self.args.prediction_loss_only:
                 return (loss, None, None)

           _A							:  List[Any]    =		generated_tokens if self.args.predict_with_generate else logits

           if labels.shape[-1] < gen_kwargs["max_length"]:
                 _A							:  Any    =		self._pad_tensors_to_max_len(_a	,  gen_kwargs["""max_length"""]       )

           return (loss, logits, labels)



     def       a__			(						self	,  _a	,  _a       )		->							Union[str, Any]:
           # If PAD token is not defined at least EOS token has to be defined
           _A							:  Optional[Any]    =		self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id

           if pad_token_id is None:
                 raise ValueError(
                     """Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"""
                     F''' padded to `max_length`={max_length}'''       )

           _A							:  Dict    =		pad_token_id * torch.ones(
               (tensor.shape[0], max_length)	,  dtype=tensor.dtype	,  device=tensor.device       )
           _A							:  Dict    =		tensor
           return padded_tensor

