
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				(     UpperCAmelCase      )->		str:







					'''simple docstring'''



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


A_														=      logging.getLogger(__name__)

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

			def _lowercase (	self     :     Tuple							,							lowerCAmelCase_     :     Union[str, Any]							,							lowerCAmelCase_     :     Any					)					->					List[Any]:




								"""simple docstring"""






								SCREAMING_SNAKE_CASE_	      =       {F'''lr_group_{i}''': param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups					)}
								pl_module.logger.log_metrics(UpperCamelCase__					)

			@rank_zero_only
			def _lowercase (	self     :     str							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     int=True					)					->					None:




								"""simple docstring"""






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

								if not save_generations:
													return

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

			@rank_zero_only
			def _lowercase (	self     :     List[Any]							,							lowerCAmelCase_     :     Union[str, Any]							,							lowerCAmelCase_     :     Union[str, Any]					)					->					int:




								"""simple docstring"""






								try:
													SCREAMING_SNAKE_CASE_	      =       pl_module.model.model.num_parameters()
								except AttributeError:
													SCREAMING_SNAKE_CASE_	      =       pl_module.model.num_parameters()

								SCREAMING_SNAKE_CASE_	      =       count_trainable_parameters(UpperCamelCase__					)
								# 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 _lowercase (	self     :     List[Any]							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     pl.LightningModule					)					->					Dict:




								"""simple docstring"""






								save_json(pl_module.metrics							,							pl_module.metrics_save_path					)
								return self._write_logs(UpperCamelCase__							,							UpperCamelCase__							,							'''test'''					)




			@rank_zero_only
			def _lowercase (	self     :     int							,							lowerCAmelCase_     :     pl.Trainer							,							lowerCAmelCase_     :     Any					)					->					str:




								"""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 							UpperCAmelCase				(     UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase=1				,UpperCAmelCase=False      )->		int:







					'''simple docstring'''



					if metric == "rouge2":
										SCREAMING_SNAKE_CASE_	      =       '''{val_avg_rouge2:.4f}-{step_count}'''
					elif metric == "bleu":
										SCREAMING_SNAKE_CASE_	      =       '''{val_avg_bleu:.4f}-{step_count}'''
					elif metric == "loss":
										SCREAMING_SNAKE_CASE_	      =       '''{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_	      =       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				(     UpperCAmelCase				,UpperCAmelCase      )->		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__				,)






from typing import TYPE_CHECKING

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


A_														=      {
    "configuration_switch_transformers": [
        "SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwitchTransformersConfig",
        "SwitchTransformersOnnxConfig",
    ]
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A_														=      [
		    "SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "SwitchTransformersEncoderModel",
		    "SwitchTransformersForConditionalGeneration",
		    "SwitchTransformersModel",
		    "SwitchTransformersPreTrainedModel",
		    "SwitchTransformersTop1Router",
		    "SwitchTransformersSparseMLP",
		]


if TYPE_CHECKING:
		from .configuration_switch_transformers import (
		    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    SwitchTransformersConfig,
		    SwitchTransformersOnnxConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
				    SwitchTransformersEncoderModel,
				    SwitchTransformersForConditionalGeneration,
				    SwitchTransformersModel,
				    SwitchTransformersPreTrainedModel,
				    SwitchTransformersSparseMLP,
				    SwitchTransformersTopaRouter,
				)


else:
		import sys

		A_														=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


