



"""simple docstring"""






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


__snake_case				:  int =       [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
	import run_clm_flax
	import run_flax_glue
	import run_flax_ner
	import run_mlm_flax
	import run_qa
	import run_summarization_flax
	import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

__snake_case				:  List[Any] =       logging.getLogger()


def      _lowercase							(    )		->  List[Any]:
			__lowerCAmelCase :    Union[str, Any]				=			argparse.ArgumentParser()
			parser.add_argument("-f"      )
			__lowerCAmelCase :    int				=			parser.parse_args()
			return args.f


def      _lowercase							(    __snake_case   ,__snake_case="eval"      )		->  List[Any]:
			__lowerCAmelCase :    List[Any]				=			os.path.join(UpperCamelCase__   ,F"""{split}_results.json"""      )
			if os.path.exists(UpperCamelCase__      ):
						with open(UpperCamelCase__   ,"r"      ) as f:
									return json.load(UpperCamelCase__      )
			raise ValueError(F"""can't find {path}"""      )


__snake_case				:  str =       logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.get_auto_remove_tmp_dir()
				__lowerCAmelCase :    List[str]				=			F"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

				with patch.object(__a			,     "argv"			,     __a):
							run_flax_glue.main()
							__lowerCAmelCase :    List[Any]				=			get_results(__a)
							self.assertGreaterEqual(result["eval_accuracy"]			,     0.75)

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.get_auto_remove_tmp_dir()
				__lowerCAmelCase :    int				=			F"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

				with patch.object(__a			,     "argv"			,     __a):
							run_clm_flax.main()
							__lowerCAmelCase :    Optional[Any]				=			get_results(__a)
							self.assertLess(result["eval_perplexity"]			,     100)

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			self.get_auto_remove_tmp_dir()
				__lowerCAmelCase :    Union[str, Any]				=			F"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

				with patch.object(__a			,     "argv"			,     __a):
							run_summarization_flax.main()
							__lowerCAmelCase :    int				=			get_results(__a			,     split="test")
							self.assertGreaterEqual(result["test_rouge1"]			,     10)
							self.assertGreaterEqual(result["test_rouge2"]			,     2)
							self.assertGreaterEqual(result["test_rougeL"]			,     7)
							self.assertGreaterEqual(result["test_rougeLsum"]			,     7)

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			self.get_auto_remove_tmp_dir()
				__lowerCAmelCase :    Tuple				=			F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

				with patch.object(__a			,     "argv"			,     __a):
							run_mlm_flax.main()
							__lowerCAmelCase :    Optional[Any]				=			get_results(__a)
							self.assertLess(result["eval_perplexity"]			,     42)

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.get_auto_remove_tmp_dir()
				__lowerCAmelCase :    Tuple				=			F"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

				with patch.object(__a			,     "argv"			,     __a):
							run_ta_mlm_flax.main()
							__lowerCAmelCase :    str				=			get_results(__a)
							self.assertGreaterEqual(result["eval_accuracy"]			,     0.42)

	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			7 if get_gpu_count() > 1 else 2

				__lowerCAmelCase :    str				=			self.get_auto_remove_tmp_dir()
				__lowerCAmelCase :    Optional[Any]				=			F"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

				with patch.object(__a			,     "argv"			,     __a):
							run_flax_ner.main()
							__lowerCAmelCase :    int				=			get_results(__a)
							self.assertGreaterEqual(result["eval_accuracy"]			,     0.75)
							self.assertGreaterEqual(result["eval_f1"]			,     0.3)







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			self.get_auto_remove_tmp_dir()
				__lowerCAmelCase :    str				=			F"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

				with patch.object(__a			,     "argv"			,     __a):
							run_qa.main()
							__lowerCAmelCase :    Tuple				=			get_results(__a)
							self.assertGreaterEqual(result["eval_f1"]			,     30)
							self.assertGreaterEqual(result["eval_exact"]			,     30)
'''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    lowerCAmelCase       (		UpperCamelCase__					: Tuple							):


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


__lowerCAmelCase						:							List[str]    							=						logging.getLogger(__name__)





def    lowerCAmelCase       (		UpperCamelCase__					: Any     ,       UpperCamelCase__					: str							):


	"""simple docstring"""
	if metric == "rouge2":
		__UpperCAmelCase      					=      '''{val_avg_rouge2:.4f}-{step_count}'''
	elif metric == "bleu":
		__UpperCAmelCase      					=      '''{val_avg_bleu:.4f}-{step_count}'''
	elif metric == "em":
		__UpperCAmelCase      					=      '''{val_avg_em:.4f}-{step_count}'''
	elif metric == "loss":
		__UpperCAmelCase      					=      '''{val_avg_loss:.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.'''							)

	__UpperCAmelCase      					=      ModelCheckpoint(
	    dirpath=UpperCamelCase__     ,       filename=UpperCamelCase__     ,       monitor=f"""val_{metric}"""     ,       mode='''max'''     ,       save_top_k=1     ,       every_n_epochs=1     ,       )

	return checkpoint_callback





def    lowerCAmelCase       (		UpperCamelCase__					: Tuple     ,       UpperCamelCase__					: Optional[Any]							):


	"""simple docstring"""
	return EarlyStopping(
	    monitor=f"""val_{metric}"""     ,       mode='''min''' if '''loss''' in metric else '''max'''     ,       patience=UpperCamelCase__     ,       verbose=UpperCamelCase__     ,       )






class     A ( pl.Callback ):





							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				int  ,		__a					:				Any						)				->						Optional[Any]:
								__UpperCAmelCase      					=      {f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups						)}
								pl_module.logger.log_metrics(__a						)





							@rank_zero_only
							def    snake_case__    (   self					:				Union[str, Any]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule  ,		__a					:				str  ,		__a					:				Any=True						)				->						None:
								logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****"""						)
								__UpperCAmelCase      					=      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
								__UpperCAmelCase      					=      Path(pl_module.hparams.output_dir						)
								if type_path == "test":
									__UpperCAmelCase      					=      od / '''test_results.txt'''
									__UpperCAmelCase      					=      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.
									__UpperCAmelCase      					=      od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
									__UpperCAmelCase      					=      od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
									results_file.parent.mkdir(exist_ok=__a						)
									generations_file.parent.mkdir(exist_ok=__a						)
								with open(__a  ,		'''a+'''						) as writer:
									for key in sorted(__a						):
										if key in ["log", "progress_bar", "preds"]:
											continue
										__UpperCAmelCase      					=      metrics[key]
										if isinstance(__a  ,		torch.Tensor						):
											__UpperCAmelCase      					=      val.item()
										__UpperCAmelCase      					=      f"""{key}: {val:.6f}\n"""
										writer.write(__a						)

								if not save_generations:
									return

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





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

								__UpperCAmelCase      					=      count_trainable_parameters(__a						)
								# 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					:				List[str]  ,		__a					:				pl.Trainer  ,		__a					:				pl.LightningModule						)				->						Optional[int]:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								return self._write_logs(__a  ,		__a  ,		'''test'''						)






							@rank_zero_only
							def    snake_case__    (   self					:				Any  ,		__a					:				pl.Trainer  ,		__a					:				Union[str, Any]						)				->						Tuple:
								save_json(pl_module.metrics  ,		pl_module.metrics_save_path						)
								# Uncommenting this will save val generations
								# return self._write_logs(trainer, pl_module, "valid")
