



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_       (  )     ->    Tuple:
   _A						     =					argparse.ArgumentParser()
   parser.add_argument('''-f'''							)
   _A						     =					parser.parse_args()
   return args.f

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :str							)     ->    Tuple:
   _A						     =					{}
   _A						     =					os.path.join(_snake_case  ,      '''all_results.json'''							)
   if os.path.exists(_snake_case							):
      with open(_snake_case  ,      '''r'''							) as f:
         _A						     =					json.load(_snake_case							)
   else:
      raise ValueError(F'''can\'t find {path}'''							)
   return results

def   SCREAMING_SNAKE_CASE_       (  )     ->    str:
   return bool(tf.config.list_physical_devices('''GPU'''							)							)


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


class   lowerCamelCase__  (					_A):



 """simple docstring"""

 @skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''			)
 def 						snake_case_			(						self				:				Optional[int]			)      ->  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(__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						     =					get_results(__lowerCAmelCase			)
       self.assertGreaterEqual(result['''eval_accuracy'''] ,	0.75			)

 def 						snake_case_			(						self				:				List[Any]			)      ->  Dict:
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_clm.main()
       _A						     =					get_results(__lowerCAmelCase			)
       self.assertLess(result['''eval_perplexity'''] ,	1_00			)

 def 						snake_case_			(						self				:				Dict			)      ->  Dict:
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_mlm.main()
       _A						     =					get_results(__lowerCAmelCase			)
       self.assertLess(result['''eval_perplexity'''] ,	42			)

 def 						snake_case_			(						self				:				Union[str, Any]			)      ->  Tuple:
    # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_ner.main()
       _A						     =					get_results(__lowerCAmelCase			)
       self.assertGreaterEqual(result['''accuracy'''] ,	0.75			)

 def 						snake_case_			(						self				:				int			)      ->  int:
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_squad.main()
       _A						     =					get_results(__lowerCAmelCase			)
       self.assertGreaterEqual(result['''f1'''] ,	30			)
       self.assertGreaterEqual(result['''exact'''] ,	30			)

 def 						snake_case_			(						self				:				str			)      ->  List[Any]:
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_swag.main()
       _A						     =					get_results(__lowerCAmelCase			)
       self.assertGreaterEqual(result['''val_accuracy'''] ,	0.8			)

 @slow
 def 						snake_case_			(						self				:				Tuple			)      ->  Union[str, Any]:
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_summarization.main()
       _A						     =					get_results(__lowerCAmelCase			)
       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				:				Optional[int]			)      ->  Optional[int]:
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_translation.main()
       _A						     =					get_results(__lowerCAmelCase			)
       self.assertGreaterEqual(result['''bleu'''] ,	30			)

 def 						snake_case_			(						self				:				str			)      ->  int:
    _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(__lowerCAmelCase ,	'''argv''' ,	__lowerCAmelCase			):
       run_image_classification.main()
       _A						     =					get_results(__lowerCAmelCase			)
       self.assertGreaterEqual(result['''accuracy'''] ,	0.7			)








def   SCREAMING_SNAKE_CASE_       (  _snake_case  :list							)     ->    list:
   if len(_snake_case							) <= 1:
      return data
   else:
      return [
          *quick_sort([e for e in data[1:] if e <= data[0]]							),
          data[0],
          *quick_sort([e for e in data[1:] if e > data[0]]							),
      ]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



