






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

import tensorflow as tf

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


_a       =    [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        '''text-generation''',
        '''text-classification''',
        '''token-classification''',
        '''language-modeling''',
        '''multiple-choice''',
        '''question-answering''',
        '''summarization''',
        '''translation''',
        '''image-classification''',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm
		import run_image_classification
		import run_mlm
		import run_ner
		import run_qa as run_squad
		import run_summarization
		import run_swag
		import run_text_classification
		import run_translation


logging.basicConfig(level=logging.DEBUG)

_a       =    logging.getLogger()


def __A       ( )->    List[str]:





						"""simple docstring"""



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


def __A       ( __lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     {}
						_UpperCAmelCase							   =     os.path.join(__lowerCAmelCase					,						'all_results.json'      )
						if os.path.exists(__lowerCAmelCase      ):
												with open(__lowerCAmelCase					,						'r'      ) as f:
																		_UpperCAmelCase							   =     json.load(__lowerCAmelCase      )
						else:
												raise ValueError(F"""can't find {path}"""      )
						return results


def __A       ( )->    str:





						"""simple docstring"""



						return bool(tf.config.list_physical_devices('GPU'      )      )


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





class __lowerCamelCase					( snake_case__):





	"""simple docstring"""






	@skip('Skipping until shape inference for to_tf_dataset PR is merged.'       )
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     F"""
            run_text_classification.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --do_train
            --do_eval
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --max_steps=10
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

							if is_cuda_available():
													testargs.append('--fp16'       )

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_text_classification.main()
													# Reset the mixed precision policy so we don't break other tests
													tf.keras.mixed_precision.set_global_policy('float32'       )
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertGreaterEqual(result['eval_accuracy']			,       0.75       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     F"""
            run_clm.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 2
            --per_device_eval_batch_size 1
            --num_train_epochs 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

							if len(tf.config.list_physical_devices('GPU'       )       ) > 1:
													# Skipping because there are not enough batches to train the model + would need a drop_last to work.
													return

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_clm.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertLess(result['eval_perplexity']			,       100       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     F"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --max_seq_length 64
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --prediction_loss_only
            --num_train_epochs=1
            --learning_rate=1e-4
        """.split()

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_mlm.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertLess(result['eval_perplexity']			,       42       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     7 if get_gpu_count() > 1 else 2

							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     F"""
            run_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
            --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_ner.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertGreaterEqual(result['accuracy']			,       0.75       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     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
            --max_steps=10
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_squad.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertGreaterEqual(result['f1']			,       30       )
													self.assertGreaterEqual(result['exact']			,       30       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     F"""
            run_swag.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/swag/sample.json
            --validation_file tests/fixtures/tests_samples/swag/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=20
            --warmup_steps=2
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_swag.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertGreaterEqual(result['val_accuracy']			,       0.8       )

	@slow
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     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
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_steps=50
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_summarization.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertGreaterEqual(result['rouge1']			,       10       )
													self.assertGreaterEqual(result['rouge2']			,       2       )
													self.assertGreaterEqual(result['rougeL']			,       7       )
													self.assertGreaterEqual(result['rougeLsum']			,       7       )

	@slow
	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     F"""
            run_translation.py
            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1
            --source_lang en
            --target_lang ro
            --train_file tests/fixtures/tests_samples/wmt16/sample.json
            --validation_file tests/fixtures/tests_samples/wmt16/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --warmup_steps=8
            --do_train
            --do_eval
            --learning_rate=3e-3
            --num_train_epochs 12
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --source_lang en_XX
            --target_lang ro_RO
        """.split()

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_translation.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertGreaterEqual(result['bleu']			,       30       )

	def 			UpperCamelCase       (				self       ):
							"""simple docstring"""







							_UpperCAmelCase							   =     self.get_auto_remove_tmp_dir()
							_UpperCAmelCase							   =     F"""
            run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --model_name_or_path microsoft/resnet-18
            --do_train
            --do_eval
            --learning_rate 1e-4
            --per_device_train_batch_size 2
            --per_device_eval_batch_size 1
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --dataloader_num_workers 16
            --num_train_epochs 2
            --train_val_split 0.1
            --seed 42
            --ignore_mismatched_sizes True
            """.split()

							with patch.object(UpperCAmelCase			,       'argv'			,       UpperCAmelCase       ):
													run_image_classification.main()
													_UpperCAmelCase							   =     get_results(UpperCAmelCase       )
													self.assertGreaterEqual(result['accuracy']			,       0.7       )








from __future__ import annotations

import re


def __A       ( __lowerCAmelCase      )->    list[str]:





						"""simple docstring"""



						def alphanum_key(__lowerCAmelCase      ):
												return [int(__lowerCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'					,						__lowerCAmelCase      )]

						return sorted(__lowerCAmelCase					,						key=__lowerCAmelCase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
