

"""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      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
					"""simple docstring"""
					A    =      filter(lambda lowercase__   : p.requires_grad    ,							model.parameters()   )
					A    =      sum([np.prod(p.size()   ) for p in model_parameters]   )
					return params


__A  :					Optional[Any]												=	logging.getLogger(__name__)





class     __UpperCamelCase				(     pl.Callback		):


		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Dict):
							A    =      {F"""lr_group_{i}""": param["lr"] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups)}
							pl_module.logger.log_metrics(__SCREAMING_SNAKE_CASE)


		@rank_zero_only
		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	pl.LightningModule  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=True):
							logger.info(F"""***** {type_path} results at step {trainer.global_step:05d} *****""")
							A    =      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    =      Path(pl_module.hparams.output_dir)
							if type_path == "test":
												A    =      od / "test_results.txt"
												A    =      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    =      od / F"""{type_path}_results/{trainer.global_step:05d}.txt"""
												A    =      od / F"""{type_path}_generations/{trainer.global_step:05d}.txt"""
												results_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE)
												generations_file.parent.mkdir(exist_ok=__SCREAMING_SNAKE_CASE)
							with open(__SCREAMING_SNAKE_CASE  ,  "a+") as writer:
												for key in sorted(__SCREAMING_SNAKE_CASE):
																	if key in ["log", "progress_bar", "preds"]:
																						continue
																	A    =      metrics[key]
																	if isinstance(__SCREAMING_SNAKE_CASE  ,  torch.Tensor):
																						A    =      val.item()
																	A    =      F"""{key}: {val:.6f}\n"""
																	writer.write(__SCREAMING_SNAKE_CASE)

							if not save_generations:
												return

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


		@rank_zero_only
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]):
							try:
												A    =      pl_module.model.model.num_parameters()
							except AttributeError:
												A    =      pl_module.model.num_parameters()

							A    =      count_trainable_parameters(__SCREAMING_SNAKE_CASE)
							# 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				:	str  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	pl.LightningModule):
							save_json(pl_module.metrics  ,  pl_module.metrics_save_path)
							return self._write_logs(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE  ,  "test")


		@rank_zero_only
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	pl.Trainer  ,  __SCREAMING_SNAKE_CASE				:	str):
							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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__=1    ,							lowercase__=False   ):
					"""simple docstring"""
					if metric == "rouge2":
										A    =      "{val_avg_rouge2:.4f}-{step_count}"
					elif metric == "bleu":
										A    =      "{val_avg_bleu:.4f}-{step_count}"
					elif metric == "loss":
										A    =      "{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."   )

					A    =      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      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__   ):
					"""simple docstring"""
					return EarlyStopping(
					    monitor=F"""val_{metric}"""    ,							mode="min" if "loss" in metric else "max"    ,							patience=lowercase__    ,							verbose=lowercase__    ,							)








"""simple docstring"""




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


__A  :					List[str]												=	logging.get_logger(__name__)





class     __UpperCamelCase				(     _A		):
		SCREAMING_SNAKE_CASE              =					"mt5"
		SCREAMING_SNAKE_CASE              =					["past_key_values"]


		def __init__(self				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=2_5_0_1_1_2  ,  __SCREAMING_SNAKE_CASE				:	int=5_1_2  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6_4  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]=1_0_2_4  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=8  ,  __SCREAMING_SNAKE_CASE				:	Any=None  ,  __SCREAMING_SNAKE_CASE				:	Tuple=6  ,  __SCREAMING_SNAKE_CASE				:	int=3_2  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1_2_8  ,  __SCREAMING_SNAKE_CASE				:	Tuple=0.1  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1E-6  ,  __SCREAMING_SNAKE_CASE				:	List[Any]=1.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]="gated-gelu"  ,  __SCREAMING_SNAKE_CASE				:	List[str]=True  ,  __SCREAMING_SNAKE_CASE				:	str=True  ,  __SCREAMING_SNAKE_CASE				:	int="T5Tokenizer"  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]=False  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]=0  ,  __SCREAMING_SNAKE_CASE				:	Tuple=1  ,  __SCREAMING_SNAKE_CASE				:	List[str]=0  ,  **__SCREAMING_SNAKE_CASE				:	str  ,  ):
							super().__init__(
							    is_encoder_decoder=__SCREAMING_SNAKE_CASE  ,  tokenizer_class=__SCREAMING_SNAKE_CASE  ,  tie_word_embeddings=__SCREAMING_SNAKE_CASE  ,  pad_token_id=__SCREAMING_SNAKE_CASE  ,  eos_token_id=__SCREAMING_SNAKE_CASE  ,  decoder_start_token_id=__SCREAMING_SNAKE_CASE  ,  **__SCREAMING_SNAKE_CASE  ,  )
							A    =      vocab_size
							A    =      d_model
							A    =      d_kv
							A    =      d_ff
							A    =      num_layers
							A    =      (
							    num_decoder_layers if num_decoder_layers is not None else self.num_layers
							)  # default = symmetry
							A    =      num_heads
							A    =      relative_attention_num_buckets
							A    =      relative_attention_max_distance
							A    =      dropout_rate
							A    =      layer_norm_epsilon
							A    =      initializer_factor
							A    =      feed_forward_proj
							A    =      use_cache

							A    =      self.feed_forward_proj.split("-")
							A    =      act_info[-1]
							A    =      act_info[0] == "gated"

							if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
												raise ValueError(
												    F"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
												    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
												    "'gated-gelu' or 'relu'")

							# for backwards compatibility
							if feed_forward_proj == "gated-gelu":
												A    =      "gelu_new"


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	List[Any]):
							return self.d_model


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							return self.num_heads


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	Optional[int]):
							return self.num_layers





class     __UpperCamelCase				(     _A		):


		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
		def       SCREAMING_SNAKE_CASE__     (self				:	int):
							A    =      {
							    "input_ids": {0: "batch", 1: "encoder_sequence"},
							    "attention_mask": {0: "batch", 1: "encoder_sequence"},
							}
							if self.use_past:
												A    =      "past_encoder_sequence + sequence"
												A    =      {0: "batch"}
												A    =      {0: "batch", 1: "past_decoder_sequence + sequence"}
							else:
												A    =      {0: "batch", 1: "decoder_sequence"}
												A    =      {0: "batch", 1: "decoder_sequence"}

							if self.use_past:
												self.fill_with_past_key_values_(__SCREAMING_SNAKE_CASE  ,  direction="inputs")

							return common_inputs


		@property
		# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
		def       SCREAMING_SNAKE_CASE__     (self				:	Union[str, Any]):
							return 1_3


		@property
		def       SCREAMING_SNAKE_CASE__     (self				:	int):
							return 5E-4





