"""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


SCREAMING_SNAKE_CASE__      						=  [
    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)

SCREAMING_SNAKE_CASE__      						=  logging.getLogger()





def    lowerCAmelCase__    (			)					->				Dict:





						"""simple docstring"""






						snake_case								=			argparse.ArgumentParser()
						parser.add_argument('-f'					)
						snake_case								=			parser.parse_args()
						return args.f





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[str] ,			_UpperCamelCase       :				List[str]="eval"					)					->				Tuple:





						"""simple docstring"""






						snake_case								=			os.path.join(lowerCAmelCase__ ,			f"""{split}_results.json"""					)
						if os.path.exists(lowerCAmelCase__					):
												with open(lowerCAmelCase__ ,			'r'					) as f:
																		return json.load(lowerCAmelCase__					)
						raise ValueError(f"""can't find {path}"""					)


SCREAMING_SNAKE_CASE__      						=  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_auto_remove_tmp_dir()
										snake_case								=			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(_UpperCAmelCase		,    'argv'		,    _UpperCAmelCase    ):
																run_flax_glue.main()
																snake_case								=			get_results(_UpperCAmelCase    )
																self.assertGreaterEqual(result['eval_accuracy']		,    0.75    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_auto_remove_tmp_dir()
										snake_case								=			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(_UpperCAmelCase		,    'argv'		,    _UpperCAmelCase    ):
																run_clm_flax.main()
																snake_case								=			get_results(_UpperCAmelCase    )
																self.assertLess(result['eval_perplexity']		,    1_00    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_auto_remove_tmp_dir()
										snake_case								=			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(_UpperCAmelCase		,    'argv'		,    _UpperCAmelCase    ):
																run_summarization_flax.main()
																snake_case								=			get_results(_UpperCAmelCase		,    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 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_auto_remove_tmp_dir()
										snake_case								=			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(_UpperCAmelCase		,    'argv'		,    _UpperCAmelCase    ):
																run_mlm_flax.main()
																snake_case								=			get_results(_UpperCAmelCase    )
																self.assertLess(result['eval_perplexity']		,    42    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_auto_remove_tmp_dir()
										snake_case								=			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(_UpperCAmelCase		,    'argv'		,    _UpperCAmelCase    ):
																run_ta_mlm_flax.main()
																snake_case								=			get_results(_UpperCAmelCase    )
																self.assertGreaterEqual(result['eval_accuracy']		,    0.42    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			7 if get_gpu_count() > 1 else 2

										snake_case								=			self.get_auto_remove_tmp_dir()
										snake_case								=			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(_UpperCAmelCase		,    'argv'		,    _UpperCAmelCase    ):
																run_flax_ner.main()
																snake_case								=			get_results(_UpperCAmelCase    )
																self.assertGreaterEqual(result['eval_accuracy']		,    0.75    )
																self.assertGreaterEqual(result['eval_f1']		,    0.3    )

				@slow
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.get_auto_remove_tmp_dir()
										snake_case								=			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(_UpperCAmelCase		,    'argv'		,    _UpperCAmelCase    ):
																run_qa.main()
																snake_case								=			get_results(_UpperCAmelCase    )
																self.assertGreaterEqual(result['eval_f1']		,    30    )
																self.assertGreaterEqual(result['eval_exact']		,    30    )



"""simple docstring"""
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
					raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")

# TF training parameters
SCREAMING_SNAKE_CASE__      						=  False
SCREAMING_SNAKE_CASE__      						=  False





def    lowerCAmelCase__    (			_UpperCamelCase       :				Namespace					)					->				List[str]:





						"""simple docstring"""






						return TrainCommand(_UpperCamelCase					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@staticmethod
				def 		snake_case (				lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			parser.add_parser('train'		,    help='CLI tool to train a model on a task.'    )

										train_parser.add_argument(
										    '--train_data'		,    type=lowerCAmelCase		,    required=lowerCAmelCase		,    help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'		,    )
										train_parser.add_argument(
										    '--column_label'		,    type=lowerCAmelCase		,    default=0		,    help='Column of the dataset csv file with example labels.'    )
										train_parser.add_argument(
										    '--column_text'		,    type=lowerCAmelCase		,    default=1		,    help='Column of the dataset csv file with example texts.'    )
										train_parser.add_argument(
										    '--column_id'		,    type=lowerCAmelCase		,    default=2		,    help='Column of the dataset csv file with example ids.'    )
										train_parser.add_argument(
										    '--skip_first_row'		,    action='store_true'		,    help='Skip the first row of the csv file (headers).'    )

										train_parser.add_argument('--validation_data'		,    type=lowerCAmelCase		,    default=''		,    help='path to validation dataset.'    )
										train_parser.add_argument(
										    '--validation_split'		,    type=lowerCAmelCase		,    default=0.1		,    help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.'		,    )

										train_parser.add_argument('--output'		,    type=lowerCAmelCase		,    default='./'		,    help='path to saved the trained model.'    )

										train_parser.add_argument(
										    '--task'		,    type=lowerCAmelCase		,    default='text_classification'		,    help='Task to train the model on.'    )
										train_parser.add_argument(
										    '--model'		,    type=lowerCAmelCase		,    default='bert-base-uncased'		,    help='Model\'s name or path to stored model.'    )
										train_parser.add_argument('--train_batch_size'		,    type=lowerCAmelCase		,    default=32		,    help='Batch size for training.'    )
										train_parser.add_argument('--valid_batch_size'		,    type=lowerCAmelCase		,    default=64		,    help='Batch size for validation.'    )
										train_parser.add_argument('--learning_rate'		,    type=lowerCAmelCase		,    default=3E-5		,    help='Learning rate.'    )
										train_parser.add_argument('--adam_epsilon'		,    type=lowerCAmelCase		,    default=1E-08		,    help='Epsilon for Adam optimizer.'    )
										train_parser.set_defaults(func=lowerCAmelCase    )

				def __init__(				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			logging.get_logger('transformers-cli/training'    )

										snake_case								=			'tf' if is_tf_available() else 'torch'

										os.makedirs(args.output		,    exist_ok=lowerCAmelCase    )
										snake_case								=			args.output

										snake_case								=			args.column_label
										snake_case								=			args.column_text
										snake_case								=			args.column_id

										self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
										if args.task == "text_classification":
																snake_case								=			TextClassificationPipeline.from_pretrained(args.model    )
										elif args.task == "token_classification":
																raise NotImplementedError
										elif args.task == "question_answering":
																raise NotImplementedError

										self.logger.info(F"""Loading dataset from {args.train_data}"""    )
										snake_case								=			Processor.create_from_csv(
										    args.train_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )
										snake_case								=			None
										if args.validation_data:
																self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
																snake_case								=			Processor.create_from_csv(
																    args.validation_data		,    column_label=args.column_label		,    column_text=args.column_text		,    column_id=args.column_id		,    skip_first_row=args.skip_first_row		,    )

										snake_case								=			args.validation_split
										snake_case								=			args.train_batch_size
										snake_case								=			args.valid_batch_size
										snake_case								=			args.learning_rate
										snake_case								=			args.adam_epsilon

				def 		snake_case (				self    ):






										"""simple docstring"""

										if self.framework == "tf":
																return self.run_tf()
										return self.run_torch()

				def 		snake_case (				self    ):






										"""simple docstring"""

										raise NotImplementedError

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.pipeline.fit(
										    self.train_dataset		,    validation_data=self.valid_dataset		,    validation_split=self.validation_split		,    learning_rate=self.learning_rate		,    adam_epsilon=self.adam_epsilon		,    train_batch_size=self.train_batch_size		,    valid_batch_size=self.valid_batch_size		,    )

										# Save trained pipeline
										self.pipeline.save_pretrained(self.output    )


