





"""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 		lowerCAmelCase_	(  lowercase_   :							Optional[Any]		):

 '''simple docstring'''






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


_lowerCamelCase       =	logging.getLogger(__name__)





class 			snake_case		(   pl.Callback   ):

      def 						SCREAMING_SNAKE_CASE_			(			self   :List[Any]						,							_lowerCamelCase   :List[str]						,							_lowerCamelCase   :Any						):
       __SCREAMING_SNAKE_CASE :			Dict												=       {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 						SCREAMING_SNAKE_CASE_			(			self   :Dict						,							_lowerCamelCase   :pl.Trainer						,							_lowerCamelCase   :pl.LightningModule						,							_lowerCamelCase   :str						,							_lowerCamelCase   :Tuple=True						):
       logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''						)
       __SCREAMING_SNAKE_CASE :			Any												=       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 :			int												=       Path(pl_module.hparams.output_dir						)
       if type_path == "test":
        __SCREAMING_SNAKE_CASE :			List[str]												=       od / '''test_results.txt'''
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       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 :			int												=       od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
        __SCREAMING_SNAKE_CASE :			List[str]												=       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 :			Union[str, Any]												=       metrics[key]
         if isinstance(_lowerCamelCase						,							torch.Tensor						):
          __SCREAMING_SNAKE_CASE :			str												=       val.item()
         __SCREAMING_SNAKE_CASE :			Dict												=       f'''{key}: {val:.6f}\n'''
         writer.write(_lowerCamelCase						)

       if not save_generations:
        return

       if "preds" in metrics:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       '''\n'''.join(metrics['''preds''']						)
        generations_file.open('''w+'''						).write(_lowerCamelCase						)

      @rank_zero_only
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :Union[str, Any]						,							_lowerCamelCase   :Dict						):
       try:
        __SCREAMING_SNAKE_CASE :			Optional[int]												=       pl_module.model.model.num_parameters()
       except AttributeError:
        __SCREAMING_SNAKE_CASE :			str												=       pl_module.model.num_parameters()

       __SCREAMING_SNAKE_CASE :			Union[str, Any]												=       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 						SCREAMING_SNAKE_CASE_			(			self   :Dict						,							_lowerCamelCase   :pl.Trainer						,							_lowerCamelCase   :pl.LightningModule						):
       save_json(pl_module.metrics						,							pl_module.metrics_save_path						)
       return self._write_logs(_lowerCamelCase						,							_lowerCamelCase						,							'''test'''						)



      @rank_zero_only
      def 						SCREAMING_SNAKE_CASE_			(			self   :int						,							_lowerCamelCase   :pl.Trainer						,							_lowerCamelCase   :Any						):
       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_	(  lowercase_   :							str ,      lowercase_   :							Any ,      lowercase_   :							List[str]=1 ,      lowercase_   :							Dict=False		):

 '''simple docstring'''






 if metric == "rouge2":
  __SCREAMING_SNAKE_CASE :			Dict												=       '''{val_avg_rouge2:.4f}-{step_count}'''
 elif metric == "bleu":
  __SCREAMING_SNAKE_CASE :			str												=       '''{val_avg_bleu:.4f}-{step_count}'''
 elif metric == "loss":
  __SCREAMING_SNAKE_CASE :			Any												=       '''{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 :			Any												=       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 		lowerCAmelCase_	(  lowercase_   :							int ,      lowercase_   :							Optional[Any]		):

 '''simple docstring'''






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


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


UpperCAmelCase							= get_logger(__name__)


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



