


'''simple docstring'''







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 _UpperCamelCase			(	__A       )				->						str:


      '''simple docstring'''



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


a__    :   int								=			logging.getLogger(__name__)
class    lowercase_	(       pl.Callback  ):


    def   __a				(	self    ,   a    ,   a       ):
          UpperCamelCase__      =							{f'''lr_group_{i}''': param['lr'] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups       )}
          pl_module.logger.log_metrics(_a       )


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

          if not save_generations:
                return

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


    @rank_zero_only
    def   __a				(	self    ,   a    ,   a       ):
          try:
                UpperCamelCase__      =							pl_module.model.model.num_parameters()
          except AttributeError:
                UpperCamelCase__      =							pl_module.model.num_parameters()

          UpperCamelCase__      =							count_trainable_parameters(_a       )
          # 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   __a				(	self    ,   a    ,   a       ):
          save_json(pl_module.metrics    ,   pl_module.metrics_save_path       )
          return self._write_logs(_a    ,   _a    ,   "test"       )


    @rank_zero_only
    def   __a				(	self    ,   a    ,   a       ):
          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 _UpperCamelCase			(	__A ,     __A ,     __A=1 ,     __A=False       )				->						Optional[int]:


      '''simple docstring'''



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

      UpperCamelCase__      =							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 _UpperCamelCase			(	__A ,     __A       )				->						Tuple:


      '''simple docstring'''



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






'''simple docstring'''




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


_A										= get_logger(__name__)

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     if version.parse(SCREAMING_SNAKE_CASE_				) < version.parse(SCREAMING_SNAKE_CASE_				):
          if "dev" in min_version:
               lowercase_       :							Tuple  					=       (
                   'This example requires a source install from HuggingFace diffusers (see '
                   '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
               )
          else:
               lowercase_       :							List[Any]  					=       f'''This example requires a minimum version of {min_version},'''
          error_message += f''' but the version found is {__version__}.\n'''
          raise ImportError(SCREAMING_SNAKE_CASE_				)




