




'''simple docstring'''


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 					SCREAMING_SNAKE_CASE(   )			-> str:
     A:		List[str]          =		argparse.ArgumentParser()
     parser.add_argument('''-f'''	)
     A:		Union[str, Any]          =		parser.parse_args()
     return args.f
def 					SCREAMING_SNAKE_CASE(   __lowercase	)			-> Optional[int]:
     A:		int          =		{}
     A:		Optional[int]          =		os.path.join(__lowercase		,				'''all_results.json'''	)
     if os.path.exists(__lowercase	):
          with open(__lowercase		,				'''r'''	) as f:
               A:		List[Any]          =		json.load(__lowercase	)
     else:
          raise ValueError(F"""can't find {path}"""	)
     return results
def 					SCREAMING_SNAKE_CASE(   )			-> int:
     return bool(tf.config.list_physical_devices('''GPU'''	)	)


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






class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  @skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''	)
  def 					_snake_case     ( self				:							str	)			->		Optional[int]:







       '''simple docstring'''



       A:		List[Any]          =		self.get_auto_remove_tmp_dir()
       A:		Dict          =		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:		List[Any]          =		get_results(SCREAMING_SNAKE_CASE_	)
            self.assertGreaterEqual(result['''eval_accuracy''']					,  0.75	)



  def 					_snake_case     ( self				:							Dict	)			->		Union[str, Any]:







       '''simple docstring'''



       A:		List[Any]          =		self.get_auto_remove_tmp_dir()
       A:		Union[str, Any]          =		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:		Tuple          =		get_results(SCREAMING_SNAKE_CASE_	)
            self.assertLess(result['''eval_perplexity''']					,  1_00	)



  def 					_snake_case     ( self				:							List[Any]	)			->		Optional[Any]:







       '''simple docstring'''



       A:		List[Any]          =		self.get_auto_remove_tmp_dir()
       A:		Optional[Any]          =		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:		Dict          =		get_results(SCREAMING_SNAKE_CASE_	)
            self.assertLess(result['''eval_perplexity''']					,  42	)



  def 					_snake_case     ( self				:							Optional[int]	)			->		Optional[Any]:







       '''simple docstring'''



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

       A:		List[str]          =		self.get_auto_remove_tmp_dir()
       A:		Union[str, Any]          =		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:		int          =		get_results(SCREAMING_SNAKE_CASE_	)
            self.assertGreaterEqual(result['''accuracy''']					,  0.75	)



  def 					_snake_case     ( self				:							Any	)			->		str:







       '''simple docstring'''



       A:		List[str]          =		self.get_auto_remove_tmp_dir()
       A:		Optional[int]          =		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:		List[Any]          =		get_results(SCREAMING_SNAKE_CASE_	)
            self.assertGreaterEqual(result['''f1''']					,  30	)
            self.assertGreaterEqual(result['''exact''']					,  30	)



  def 					_snake_case     ( self				:							int	)			->		Optional[int]:







       '''simple docstring'''



       A:		Any          =		self.get_auto_remove_tmp_dir()
       A:		Any          =		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:		Optional[int]          =		get_results(SCREAMING_SNAKE_CASE_	)
            self.assertGreaterEqual(result['''val_accuracy''']					,  0.8	)



  @slow
  def 					_snake_case     ( self				:							Dict	)			->		Optional[int]:







       '''simple docstring'''



       A:		Dict          =		self.get_auto_remove_tmp_dir()
       A:		List[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
            --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:		Any          =		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 					_snake_case     ( self				:							int	)			->		Tuple:







       '''simple docstring'''



       A:		List[str]          =		self.get_auto_remove_tmp_dir()
       A:		int          =		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:		List[str]          =		get_results(SCREAMING_SNAKE_CASE_	)
            self.assertGreaterEqual(result['''bleu''']					,  30	)




  def 					_snake_case     ( self				:							Optional[Any]	)			->		List[str]:







       '''simple docstring'''



       A:		Union[str, Any]          =		self.get_auto_remove_tmp_dir()
       A:		List[str]          =		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:		Tuple          =		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()






