
"""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        lowerCAmelCase_       (		snake_case_		:      Dict      )       ->str:
			lowerCamelCase__     :					str		=filter(lambda snake_case_      : p.requires_grad							,   model.parameters()      )
			lowerCamelCase__     :					Optional[Any]		=sum([np.prod(p.size()      ) for p in model_parameters]      )
			return params


lowerCAmelCase								=						logging.getLogger(__name__)

class    A_     (					pl.Callback       ):



							"""simple docstring"""






							def 				UpperCAmelCase__							(  self  :List[Any]					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Optional[Any]	):





										"""simple docstring"""

										lowerCamelCase__     :					Dict		={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  :Dict					,							lowerCamelCase_  :pl.Trainer					,							lowerCamelCase_  :pl.LightningModule					,							lowerCamelCase_  :str					,							lowerCamelCase_  :Any=True	):





										"""simple docstring"""

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

										if not save_generations:
													return

										if "preds" in metrics:
													lowerCamelCase__     :					Optional[Any]		='\n'.join(metrics['preds']	)
													generations_file.open('w+'	).write(lowerCamelCase_	)





							@rank_zero_only
							def 				UpperCAmelCase__							(  self  :Optional[int]					,							lowerCamelCase_  :Optional[int]					,							lowerCamelCase_  :Dict	):





										"""simple docstring"""

										try:
													lowerCamelCase__     :					Union[str, Any]		=pl_module.model.model.num_parameters()
										except AttributeError:
													lowerCamelCase__     :					Union[str, Any]		=pl_module.model.num_parameters()

										lowerCamelCase__     :					Tuple		=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  :Any					,							lowerCamelCase_  :pl.Trainer					,							lowerCamelCase_  :pl.LightningModule	):





										"""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  :Tuple					,							lowerCamelCase_  :pl.Trainer					,							lowerCamelCase_  :Tuple	):





										"""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        lowerCAmelCase_       (		snake_case_		:      str							,   snake_case_		:      str							,   snake_case_		:      Any=1							,   snake_case_		:      Union[str, Any]=False      )       ->List[Any]:
			if metric == "rouge2":
						lowerCamelCase__     :					Tuple		='{val_avg_rouge2:.4f}-{step_count}'
			elif metric == "bleu":
						lowerCamelCase__     :					List[str]		='{val_avg_bleu:.4f}-{step_count}'
			elif metric == "loss":
						lowerCamelCase__     :					str		='{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.'      )

			lowerCamelCase__     :					List[Any]		=ModelCheckpoint(
			    dirpath=snake_case_							,   filename=snake_case_							,   monitor=f"""val_{metric}"""							,   mode='min' if 'loss' in metric else 'max'							,   save_top_k=snake_case_							,   )
			return checkpoint_callback






def        lowerCAmelCase_       (		snake_case_		:      Tuple							,   snake_case_		:      List[str]      )       ->List[Any]:
			return EarlyStopping(
			    monitor=f"""val_{metric}"""							,   mode='min' if 'loss' in metric else 'max'							,   patience=snake_case_							,   verbose=snake_case_							,   )

"""simple docstring"""


from typing import TYPE_CHECKING

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


lowerCAmelCase								=						{
    """configuration_deberta_v2""": ["""DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DebertaV2Config""", """DebertaV2OnnxConfig"""],
    """tokenization_deberta_v2""": ["""DebertaV2Tokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						["""DebertaV2TokenizerFast"""]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFDebertaV2ForMaskedLM""",
							    """TFDebertaV2ForQuestionAnswering""",
							    """TFDebertaV2ForSequenceClassification""",
							    """TFDebertaV2ForTokenClassification""",
							    """TFDebertaV2Model""",
							    """TFDebertaV2PreTrainedModel""",
							]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase								=						[
							    """DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """DebertaV2ForMaskedLM""",
							    """DebertaV2ForMultipleChoice""",
							    """DebertaV2ForQuestionAnswering""",
							    """DebertaV2ForSequenceClassification""",
							    """DebertaV2ForTokenClassification""",
							    """DebertaV2Model""",
							    """DebertaV2PreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_deberta_va import (
							    DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    DebertaVaConfig,
							    DebertaVaOnnxConfig,
							)
							from .tokenization_deberta_va import DebertaVaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_va_fast import DebertaVaTokenizerFast

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta_va import (
														    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaVaForMaskedLM,
														    TFDebertaVaForQuestionAnswering,
														    TFDebertaVaForSequenceClassification,
														    TFDebertaVaForTokenClassification,
														    TFDebertaVaModel,
														    TFDebertaVaPreTrainedModel,
														)

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta_va import (
														    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaVaForMaskedLM,
														    DebertaVaForMultipleChoice,
														    DebertaVaForQuestionAnswering,
														    DebertaVaForSequenceClassification,
														    DebertaVaForTokenClassification,
														    DebertaVaModel,
														    DebertaVaPreTrainedModel,
														)

else:
							import sys

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