



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


__UpperCAmelCase    	=  [
    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)

__UpperCAmelCase    	=  logging.getLogger()





def     __lowerCamelCase							(   ):
							a__:							Tuple									=argparse.ArgumentParser()
							parser.add_argument("-f"    )
							a__:							Union[str, Any]									=parser.parse_args()
							return args.f





def     __lowerCamelCase							(   __magic_name__						:	Optional[Any]    ):
							a__:							List[Any]									={}
							a__:							int									=os.path.join(__snake_case  ,			"all_results.json"    )
							if os.path.exists(__snake_case    ):
														with open(__snake_case  ,			"r"    ) as f:
																					a__:							str									=json.load(__snake_case    )
							else:
														raise ValueError(F"can\'t find {path}"    )
							return results





def     __lowerCamelCase							(   ):
							return bool(tf.config.list_physical_devices("GPU"    )    )


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







class 						lowerCamelCase__			(		_UpperCAmelCase ):


						@skip("Skipping until shape inference for to_tf_dataset PR is merged."      )
						def 	_lowerCamelCase      (			self    :	int      ):
													a__:							Any									=self.get_auto_remove_tmp_dir()
													a__:							Dict									=F"\n            run_text_classification.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --do_train\n            --do_eval\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --max_steps=10\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

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

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				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__:							str									=get_results(_lowerCAmelCase      )
																				self.assertGreaterEqual(result["eval_accuracy"]   ,							0.7_5      )


						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													a__:							str									=self.get_auto_remove_tmp_dir()
													a__:							Optional[Any]									=F"\n            run_clm.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".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(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_clm.main()
																				a__:							Dict									=get_results(_lowerCAmelCase      )
																				self.assertLess(result["eval_perplexity"]   ,							1_0_0      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							List[Any]									=self.get_auto_remove_tmp_dir()
													a__:							Any									=F"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --max_seq_length 64\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --prediction_loss_only\n            --num_train_epochs=1\n            --learning_rate=1e-4\n        ".split()

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_mlm.main()
																				a__:							Union[str, Any]									=get_results(_lowerCAmelCase      )
																				self.assertLess(result["eval_perplexity"]   ,							4_2      )


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

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

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_ner.main()
																				a__:							Optional[Any]									=get_results(_lowerCAmelCase      )
																				self.assertGreaterEqual(result["accuracy"]   ,							0.7_5      )


						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							Union[str, Any]									=self.get_auto_remove_tmp_dir()
													a__:							Union[str, Any]									=F"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=10\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_squad.main()
																				a__:							Union[str, Any]									=get_results(_lowerCAmelCase      )
																				self.assertGreaterEqual(result["f1"]   ,							3_0      )
																				self.assertGreaterEqual(result["exact"]   ,							3_0      )


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

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_swag.main()
																				a__:							List[str]									=get_results(_lowerCAmelCase      )
																				self.assertGreaterEqual(result["val_accuracy"]   ,							0.8      )


						@slow
						def 	_lowerCamelCase      (			self    :	Any      ):
													a__:							List[str]									=self.get_auto_remove_tmp_dir()
													a__:							Tuple									=F"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_steps=50\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_summarization.main()
																				a__:							Dict									=get_results(_lowerCAmelCase      )
																				self.assertGreaterEqual(result["rouge1"]   ,							1_0      )
																				self.assertGreaterEqual(result["rouge2"]   ,							2      )
																				self.assertGreaterEqual(result["rougeL"]   ,							7      )
																				self.assertGreaterEqual(result["rougeLsum"]   ,							7      )


						@slow
						def 	_lowerCamelCase      (			self    :	Optional[Any]      ):
													a__:							str									=self.get_auto_remove_tmp_dir()
													a__:							List[Any]									=F"\n            run_translation.py\n            --model_name_or_path Rocketknight1/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --learning_rate=3e-3\n            --num_train_epochs 12\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n        ".split()

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_translation.main()
																				a__:							Optional[Any]									=get_results(_lowerCAmelCase      )
																				self.assertGreaterEqual(result["bleu"]   ,							3_0      )







						def 	_lowerCamelCase      (			self    :	str      ):
													a__:							Union[str, Any]									=self.get_auto_remove_tmp_dir()
													a__:							Any									=F"\n            run_image_classification.py\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --model_name_or_path microsoft/resnet-18\n            --do_train\n            --do_eval\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --dataloader_num_workers 16\n            --num_train_epochs 2\n            --train_val_split 0.1\n            --seed 42\n            --ignore_mismatched_sizes True\n            ".split()

													with patch.object(_lowerCAmelCase   ,							"argv"   ,							_lowerCAmelCase      ):
																				run_image_classification.main()
																				a__:							Optional[int]									=get_results(_lowerCAmelCase      )
																				self.assertGreaterEqual(result["accuracy"]   ,							0.7      )









def 				UpperCamelCase_	(					__snake_case      :    set						,      __snake_case      :    set  )->   set:




						'''simple docstring'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_a={set_a}] is not a set'''
						assert isinstance(__snake_case						,      __snake_case  ), F'''The input value of [set_b={set_b}] is not a set'''

						return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
					from doctest import testmod

					testmod()

