





"""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 		__lowercase					(       snake_case_  :  int						)       ->int:



				'''simple docstring'''







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


a_			=      logging.getLogger(__name__)







def 		__lowercase					(       snake_case_  :  Any    ,snake_case_  :  List[Any]						)       ->List[Any]:



				'''simple docstring'''







				if metric == "rouge2":
								__A   :						Union[str, Any]									=					'''{val_avg_rouge2:.4f}-{step_count}'''
				elif metric == "bleu":
								__A   :						int									=					'''{val_avg_bleu:.4f}-{step_count}'''
				elif metric == "em":
								__A   :						Dict									=					'''{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.'''						)

				__A   :						int									=					ModelCheckpoint(
				    dirpath=snake_case_    ,filename=snake_case_    ,monitor=F"""val_{metric}"""    ,mode='''max'''    ,save_top_k=3    ,every_n_epochs=1    ,)
				return checkpoint_callback







def 		__lowercase					(       snake_case_  :  Optional[Any]    ,snake_case_  :  int						)       ->List[str]:



				'''simple docstring'''







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






class 		__snake_case (		pl.Callback					):





	"""simple docstring"""


	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



					'''simple docstring'''




					__A   :						str									=					{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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase ,						__lowerCamelCase=True  ):



					'''simple docstring'''




					logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****"""  )
					__A   :						List[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
					__A   :						str									=					Path(pl_module.hparams.output_dir  )
					if type_path == "test":
									__A   :						str									=					od / '''test_results.txt'''
									__A   :						Optional[Any]									=					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.
									__A   :						List[str]									=					od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
									__A   :						List[Any]									=					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
													__A   :						int									=					metrics[key]
													if isinstance(__lowerCamelCase ,						torch.Tensor  ):
																	__A   :						Dict									=					val.item()
													__A   :						int									=					F"""{key}: {val:.6f}\n"""
													writer.write(__lowerCamelCase  )

					if not save_generations:
									return

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

	@rank_zero_only
	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



					'''simple docstring'''




					try:
									__A   :						Optional[int]									=					pl_module.model.model.num_parameters()
					except AttributeError:
									__A   :						str									=					pl_module.model.num_parameters()

					__A   :						Optional[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  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



					'''simple docstring'''




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







	@rank_zero_only
	def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase  ):



					'''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")











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a_			=      {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
       if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      ["""BloomTokenizerFast"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a_			=      [
           """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """BloomForCausalLM""",
           """BloomModel""",
           """BloomPreTrainedModel""",
           """BloomForSequenceClassification""",
           """BloomForTokenClassification""",
           """BloomForQuestionAnswering""",
       ]

if TYPE_CHECKING:
       from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

       try:
              if not is_tokenizers_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bloom_fast import BloomTokenizerFast

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_bloom import (
                  BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
                  BloomForCausalLM,
                  BloomForQuestionAnswering,
                  BloomForSequenceClassification,
                  BloomForTokenClassification,
                  BloomModel,
                  BloomPreTrainedModel,
              )

else:
       import sys

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




