



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


_SCREAMING_SNAKE_CASE      	=		logging.getLogger(__name__)


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   ):







	if metric == "rouge2":
		snake_case_		:			Tuple											=	"{val_avg_rouge2:.4f}-{step_count}"
	elif metric == "bleu":
		snake_case_		:			List[str]											=	"{val_avg_bleu:.4f}-{step_count}"
	elif metric == "em":
		snake_case_		:			List[Any]											=	"{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_		:			List[str]											=	ModelCheckpoint(
	    dirpath=__a   , filename=__a   , monitor=f"""val_{metric}"""   , mode='max'   , save_top_k=3   , every_n_epochs=1   , )
	return checkpoint_callback


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


class SCREAMING_SNAKE_CASE_						(					pl.Callback					):






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      List[str]		,   _A      :      Union[str, Any]	)							->    Dict:







							"""simple docstring"""


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






						@rank_zero_only
						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Optional[int]		,   _A      :      List[str]		,   _A      :      Union[str, Any]		,   _A      :      Tuple=True	)							->    Dict:







							"""simple docstring"""


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

							if not save_generations:
								return

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






						@rank_zero_only
						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Any		,   _A      :      Any	)							->    List[str]:







							"""simple docstring"""


							try:
								snake_case_		:			Tuple											=	pl_module.model.model.num_parameters()
							except AttributeError:
								snake_case_		:			Tuple											=	pl_module.model.num_parameters()

							snake_case_		:			List[Any]											=	count_trainable_parameters(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 UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      Union[str, Any]	)							->    str:







							"""simple docstring"""


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






						@rank_zero_only
						def UpperCAmelCase_				(      self      :      Any		,   _A      :      List[Any]		,   _A      :      List[Any]	)							->    Optional[Any]:







							"""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")








# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path


_SCREAMING_SNAKE_CASE      	=		Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))

import dataclasses  # noqa
import io  # noqa
import itertools  # noqa
import json  # noqa
import os  # noqa
import unittest  # noqa
from copy import deepcopy  # noqa

from parameterized import parameterized  # noqa
from transformers import TrainingArguments, is_torch_available  # noqa
from transformers.deepspeed import is_deepspeed_available  # noqa
from transformers.file_utils import WEIGHTS_NAME  # noqa
from transformers.testing_utils import (  # noqa
    CaptureLogger,
    ExtendSysPath,
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    mockenv_context,
    require_deepspeed,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)
from transformers.trainer_utils import set_seed  # noqa


set_seed(42)

_SCREAMING_SNAKE_CASE      	=		{"""base""": """patrickvonplaten/wav2vec2_tiny_random""", """robust""": """patrickvonplaten/wav2vec2_tiny_random_robust"""}

_SCREAMING_SNAKE_CASE      	=		"""zero2"""
_SCREAMING_SNAKE_CASE      	=		"""zero3"""
_SCREAMING_SNAKE_CASE      	=		[ZEROa, ZEROa]


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	# customize the test name generator function as we want both params to appear in the sub-test
	# name, as by default it shows only the first param
	snake_case_		:			Tuple											=	parameterized.to_safe_name('_'.join(str(__a   ) for x in param.args   )   )
	return f"""{func.__name__}_{param_based_name}"""


# Cartesian-product of zero stages with models to test
_SCREAMING_SNAKE_CASE      	=		list(itertools.product(stages, models.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Tuple		,   _A      :      Optional[Any]		,   _A      :      Optional[int]	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Any		,   _A      :      int	)							->    Dict:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      str		,   _A      :      List[str]		,   _A      :      str	)							->    Tuple:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						@require_torch_multi_gpu
						@parameterized.expand(_A		,   name_func=_A	)
						def UpperCAmelCase_				(      self      :      Optional[int]		,   _A      :      int		,   _A      :      Union[str, Any]	)							->    List[Any]:







							"""simple docstring"""


							self.run_and_check(
							    stage=_A		,   model=_A		,   distributed=_A		,   fpaa=_A		,   )






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      Union[str, Any]	)							->    Any:







							"""simple docstring"""


							pass






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      bool = True		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Any:







							"""simple docstring"""


							snake_case_		:			Dict											=	models[model]

							snake_case_		:			str											=	self.run_trainer(
							    stage=_A		,   model_name=_A		,   eval_steps=_A		,   num_train_epochs=1		,   distributed=_A		,   fpaa=_A		,   )

							self.do_checks(_A	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Any		,   _A      :      str		,   _A      :      str		,   _A      :      int = 10		,   _A      :      int = 1		,   _A      :      bool = True		,   _A      :      bool = True		,   )							->    Dict:







							"""simple docstring"""


							snake_case_		:			str											=	self.get_auto_remove_tmp_dir('./xxx'		,   after=_A	)
							snake_case_		:			Tuple											=	F"""
            --model_name_or_path {model_name}
            --dataset_name hf-internal-testing/librispeech_asr_dummy
            --dataset_config_name clean
            --train_split_name validation
            --validation_split_name validation
            --output_dir {output_dir}
            --num_train_epochs {str(_A	)}
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 2
            --evaluation_strategy steps
            --learning_rate 5e-4
            --warmup_steps 8
            --orthography timit
            --preprocessing_num_workers 1
            --group_by_length
            --freeze_feature_extractor
            --report_to none
            --save_steps 0
            --eval_steps {eval_steps}
            --report_to none
        """.split()

							if fpaa:
								args.extend(['--fp16']	)

							# currently ds_config_wav2vec2_zero.json requires "zero_optimization.find_unused_parameters": true,
							# hence the separate config files
							snake_case_		:			Any											=	F"""--deepspeed {self.test_file_dir_str}/ds_config_wav2vec2_{stage}.json""".split()
							snake_case_		:			str											=	[F"""{self.examples_dir_str}/research_projects/wav2vec2/run_asr.py"""]
							snake_case_		:			int											=	self.get_launcher(_A	)

							snake_case_		:			List[str]											=	launcher + script + args + ds_args
							# keep for quick debug
							# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
							execute_subprocess_async(_A		,   env=self.get_env()	)

							return output_dir






						def UpperCAmelCase_				(      self      :      Optional[Any]		,   _A      :      Optional[Any]=False	)							->    List[str]:







							"""simple docstring"""


							snake_case_		:			str											=	min(2		,   get_gpu_count()	) if distributed else 1
							return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus}""".split()

