

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


lowerCAmelCase_			      =      logging.getLogger(__name__)


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						if metric == "rouge2":
												snake_case_  =      '''{val_avg_rouge2:.4f}-{step_count}'''
						elif metric == "bleu":
												snake_case_  =      '''{val_avg_bleu:.4f}-{step_count}'''
						elif metric == "em":
												snake_case_  =      '''{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.'''			)

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


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





class       snake_case_  (						pl.Callback   ):







			'''simple docstring'''

			def 							snake_case__(	self					:							Dict				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							List[str]				) ->List[Any]:
									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 							snake_case__(	self					:							Union[str, Any]				,  _UpperCamelCase					:							pl.Trainer				,  _UpperCamelCase					:							pl.LightningModule				,  _UpperCamelCase					:							str				,  _UpperCamelCase					:							List[str]=True				) ->None:
									logger.info(f'''***** {type_path} results at step {trainer.global_step:05d} *****'''				)
									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
									snake_case_  =      Path(pl_module.hparams.output_dir				)
									if type_path == "test":
															snake_case_  =      od / '''test_results.txt'''
															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.
															snake_case_  =      od / f'''{type_path}_results/{trainer.global_step:05d}.txt'''
															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
																					snake_case_  =      metrics[key]
																					if isinstance(_UpperCamelCase				,  torch.Tensor				):
																											snake_case_  =      val.item()
																					snake_case_  =      f'''{key}: {val:.6f}\n'''
																					writer.write(_UpperCamelCase				)

									if not save_generations:
															return

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







			@rank_zero_only
			def 							snake_case__(	self					:							List[str]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							str				) ->Optional[Any]:
									try:
															snake_case_  =      pl_module.model.model.num_parameters()
									except AttributeError:
															snake_case_  =      pl_module.model.num_parameters()

									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 							snake_case__(	self					:							Tuple				,  _UpperCamelCase					:							pl.Trainer				,  _UpperCamelCase					:							pl.LightningModule				) ->str:
									save_json(pl_module.metrics				,  pl_module.metrics_save_path				)
									return self._write_logs(_UpperCamelCase				,  _UpperCamelCase				,  '''test'''				)







			@rank_zero_only
			def 							snake_case__(	self					:							Tuple				,  _UpperCamelCase					:							pl.Trainer				,  _UpperCamelCase					:							Tuple				) ->Dict:
									save_json(pl_module.metrics				,  pl_module.metrics_save_path				)
									# Uncommenting this will save val generations
									# return self._write_logs(trainer, pl_module, "valid")






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
    import tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM

@require_tf
@require_sentencepiece
@require_tokenizers
class   lowerCAmelCase				(   unittest.TestCase     ):
 @slow
 def 	A_      ( self    : int      )     ->      Optional[Any]:
     lowerCamelCase__       : Tuple   					=						TFAutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'      )
     lowerCamelCase__       : Any   					=						AutoTokenizer.from_pretrained('google/mt5-small'      )

     lowerCamelCase__       : int   					=						tokenizer('Hello there'     ,	return_tensors='tf'      ).input_ids
     lowerCamelCase__       : int   					=						tokenizer('Hi I am'     ,	return_tensors='tf'      ).input_ids

     lowerCamelCase__       : List[str]   					=						model(UpperCAmelCase     ,	labels=UpperCAmelCase      ).loss
     lowerCamelCase__       : List[Any]   					=						-tf.math.reduce_mean(UpperCAmelCase      ).numpy()

     lowerCamelCase__       : Dict   					=						-2_1.2_2_8_1_6_8
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 2e-4      )





