






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


snake_case_   :	List[Any]							      =[
    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)

snake_case_   :	Union[str, Any]							      =logging.getLogger()



def      UpperCAmelCase						( ):


		'''simple docstring'''

		__A    =							argparse.ArgumentParser()
		parser.add_argument("-f"	)
		__A    =							parser.parse_args()
		return args.f



def      UpperCAmelCase						( lowerCAmelCase__	):


		'''simple docstring'''

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



def      UpperCAmelCase						( ):


		'''simple docstring'''

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


snake_case_   :	Union[str, Any]							      =logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class        a__      (	UpperCAmelCase_		):






		@skip("Skipping until shape inference for to_tf_dataset PR is merged."						)
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_text_classification.main()
						# Reset the mixed precision policy so we don't break other tests
						tf.keras.mixed_precision.set_global_policy("float32"						)
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertGreaterEqual(result["eval_accuracy"]							, 0.75						)






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_clm.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertLess(result["eval_perplexity"]							, 100						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_mlm.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertLess(result["eval_perplexity"]							, 42						)






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							7 if get_gpu_count() > 1 else 2

				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_ner.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertGreaterEqual(result["accuracy"]							, 0.75						)






		def 					_lowerCamelCase					(      self						)				->				str:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_squad.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertGreaterEqual(result["f1"]							, 30						)
						self.assertGreaterEqual(result["exact"]							, 30						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_swag.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertGreaterEqual(result["val_accuracy"]							, 0.8						)






		@slow
		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_summarization.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertGreaterEqual(result["rouge1"]							, 10						)
						self.assertGreaterEqual(result["rouge2"]							, 2						)
						self.assertGreaterEqual(result["rougeL"]							, 7						)
						self.assertGreaterEqual(result["rougeLsum"]							, 7						)






		@slow
		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_translation.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertGreaterEqual(result["bleu"]							, 30						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							self.get_auto_remove_tmp_dir()
				__A    =							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(SCREAMING_SNAKE_CASE_							, "argv"							, SCREAMING_SNAKE_CASE_						):
						run_image_classification.main()
						__A    =							get_results(SCREAMING_SNAKE_CASE_						)
						self.assertGreaterEqual(result["accuracy"]							, 0.7						)







'''simple docstring'''


def 					SCREAMING_SNAKE_CASE(   __lowercase	)			-> int:
     if not isinstance(__lowercase		,				__lowercase	):
          raise ValueError('''double_factorial() only accepts integral values'''	)
     if n < 0:
          raise ValueError('''double_factorial() not defined for negative values'''	)
     return 1 if n <= 1 else n * double_factorial(n - 2	)


if __name__ == "__main__":
     import doctest

     doctest.testmod()






