


"""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_rag import save_json


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




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


A     :   List[str]			    =			logging.getLogger(__name__)


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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

			__lowerCAmelCase				    =			ModelCheckpoint(
			    dirpath=_UpperCamelCase    , filename=_UpperCamelCase    , monitor=f"val_{metric}"    , mode="max"    , save_top_k=3    , every_n_epochs=1    , )
			return checkpoint_callback


def _lowerCamelCase	(		_UpperCamelCase    , _UpperCamelCase      ):

			'''simple docstring'''




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


class   _UpperCamelCase  (   pl.Callback		):
							'''simple docstring'''







							def 			snake_case				(     self    ,   __a    ,   __a	):
										__lowerCAmelCase				    =			{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 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a=True	):
										logger.info(f"***** {type_path} results at step {trainer.global_step:05d} *****"	)
										__lowerCAmelCase				    =			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
										__lowerCAmelCase				    =			Path(pl_module.hparams.output_dir	)
										if type_path == "test":
													__lowerCAmelCase				    =			od / "test_results.txt"
													__lowerCAmelCase				    =			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.
													__lowerCAmelCase				    =			od / f"{type_path}_results/{trainer.global_step:05d}.txt"
													__lowerCAmelCase				    =			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
																__lowerCAmelCase				    =			metrics[key]
																if isinstance(__a    ,   torch.Tensor	):
																			__lowerCAmelCase				    =			val.item()
																__lowerCAmelCase				    =			f"{key}: {val:.6f}\n"
																writer.write(__a	)

										if not save_generations:
													return

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

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

										__lowerCAmelCase				    =			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 			snake_case				(     self    ,   __a    ,   __a	):
										save_json(pl_module.metrics    ,   pl_module.metrics_save_path	)
										return self._write_logs(__a    ,   __a    ,   "test"	)



							@rank_zero_only
							def 			snake_case				(     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")








"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A     :   Tuple			    =			{
    "configuration_blenderbot": [
        "BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "BlenderbotConfig",
        "BlenderbotOnnxConfig",
    ],
    "tokenization_blenderbot": ["BlenderbotTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   Dict			    =			["BlenderbotTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   str			    =			[
	    "BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "BlenderbotForCausalLM",
	    "BlenderbotForConditionalGeneration",
	    "BlenderbotModel",
	    "BlenderbotPreTrainedModel",
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "TFBlenderbotForConditionalGeneration",
	    "TFBlenderbotModel",
	    "TFBlenderbotPreTrainedModel",
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A     :   List[str]			    =			[
	    "FlaxBlenderbotForConditionalGeneration",
	    "FlaxBlenderbotModel",
	    "FlaxBlenderbotPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_blenderbot import (
	    BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotConfig,
	    BlenderbotOnnxConfig,
	)
	from .tokenization_blenderbot import BlenderbotTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_fast import BlenderbotTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot import (
		    BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotForCausalLM,
		    BlenderbotForConditionalGeneration,
		    BlenderbotModel,
		    BlenderbotPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot import (
		    TFBlenderbotForConditionalGeneration,
		    TFBlenderbotModel,
		    TFBlenderbotPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot import (
		    FlaxBlenderbotForConditionalGeneration,
		    FlaxBlenderbotModel,
		    FlaxBlenderbotPreTrainedModel,
		)

else:
	import sys

	A     :   Optional[Any]			    =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




