






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


lowerCamelCase					    =	[
    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)

lowerCamelCase					    =	logging.getLogger()
def 							UpperCAmelCase   ( ):


    '''simple docstring'''



    _lowercase     			=						argparse.ArgumentParser()
    parser.add_argument('-f'   )
    _lowercase     			=						parser.parse_args()
    return args.f
def 							UpperCAmelCase   ( _lowercase   ):


    '''simple docstring'''



    _lowercase     			=						{}
    _lowercase     			=						os.path.join(_lowercase,   'all_results.json'   )
    if os.path.exists(_lowercase   ):
        with open(_lowercase,   'r'   ) as f:
            _lowercase     			=						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'   )   )


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

class        snake_case_				(							lowercase_   ):







   """simple docstring"""



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


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							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'						)
           _lowercase     			=						get_results(snake_case__						)
           self.assertGreaterEqual(result['eval_accuracy'] ,							0.75						)

   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		str:


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_clm.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertLess(result['eval_perplexity'] ,							100						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Tuple						)	->		Any:


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_mlm.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertLess(result['eval_perplexity'] ,							42						)

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


       """simple docstring"""



       _lowercase     			=						7 if get_gpu_count() > 1 else 2

       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_ner.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertGreaterEqual(result['accuracy'] ,							0.75						)

   def       SCREAMING_SNAKE_CASE__					(     self:						str						)	->		str:


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_squad.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertGreaterEqual(result['f1'] ,							30						)
           self.assertGreaterEqual(result['exact'] ,							30						)

   def       SCREAMING_SNAKE_CASE__					(     self:						Union[str, Any]						)	->		List[str]:


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_swag.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertGreaterEqual(result['val_accuracy'] ,							0.8						)

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Tuple:


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_summarization.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertGreaterEqual(result['rouge1'] ,							10						)
           self.assertGreaterEqual(result['rouge2'] ,							2						)
           self.assertGreaterEqual(result['rougeL'] ,							7						)
           self.assertGreaterEqual(result['rougeLsum'] ,							7						)

   @slow
   def       SCREAMING_SNAKE_CASE__					(     self:						int						)	->		Any:


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_translation.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertGreaterEqual(result['bleu'] ,							30						)

   def       SCREAMING_SNAKE_CASE__					(     self:						List[str]						)	->		Union[str, Any]:


       """simple docstring"""



       _lowercase     			=						self.get_auto_remove_tmp_dir()
       _lowercase     			=						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(snake_case__ ,							'argv' ,							snake_case__						):
           run_image_classification.main()
           _lowercase     			=						get_results(snake_case__						)
           self.assertGreaterEqual(result['accuracy'] ,							0.7						)








import torch

from accelerate import Accelerator, DistributedType

class        snake_case_				:







   """simple docstring"""



   def __enter__(     self:						int						)	->		Tuple:


       """simple docstring"""



       if self.enabled:
           _lowercase     			=						self.model.no_sync()
           self.model_sync_obj.__enter__()

       return self

   def __exit__(     self:						str ,							snake_case__:						Tuple ,							snake_case__:						int ,							snake_case__:						Tuple						)	->		Any:


       """simple docstring"""



       if self.enabled:
           # Average all models on exit
           self._sync_and_avg_model_params()
           self.model_sync_obj.__exit__(snake_case__ ,							snake_case__ ,							snake_case__						)

   def __init__(     self:						int ,							snake_case__:						Accelerator ,							snake_case__:						torch.nn.Module ,							snake_case__:						int ,							snake_case__:						bool = True						)	->		Optional[Any]:


       """simple docstring"""



       if accelerator.distributed_type not in [
           DistributedType.NO,
           DistributedType.MULTI_CPU,
           DistributedType.MULTI_GPU,
       ]:
           raise NotImplementedError('LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)'						)
       _lowercase     			=						enabled and accelerator.distributed_type != DistributedType.NO
       _lowercase     			=						0
       if self.enabled:
           _lowercase     			=						accelerator
           _lowercase     			=						model
           _lowercase     			=						local_sgd_steps

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


       """simple docstring"""



       self.num_steps += 1
       if not self.enabled:
           return

       if self.num_steps % self.local_sgd_steps == 0:
           self._sync_and_avg_model_params()

   def       SCREAMING_SNAKE_CASE__					(     self:						Optional[int]						)	->		Dict:


       """simple docstring"""



       self.accelerator.wait_for_everyone()
       with self.accelerator.autocast():
           for param in self.model.parameters():
               _lowercase     			=						self.accelerator.reduce(param.data ,							reduction='mean'						)
