


'''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


A      :     str										= [
    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)

A      :     Optional[int]										= logging.getLogger()






def       lowerCAmelCase__       (				):
       _A     :	Union[str, Any]      =					argparse.ArgumentParser()
       parser.add_argument('-f'   )
       _A     :	Dict      =					parser.parse_args()
       return args.f






def       lowerCAmelCase__       (				lowerCamelCase     :			Tuple   ):
       _A     :	Any      =					{}
       _A     :	str      =					os.path.join(lowerCamelCase  ,'all_results.json'   )
       if os.path.exists(lowerCamelCase   ):
              with open(lowerCamelCase  ,'r'   ) as f:
                     _A     :	Optional[Any]      =					json.load(lowerCamelCase   )
       else:
              raise ValueError(F'can\'t find {path}'   )
       return results






def       lowerCAmelCase__       (				):
       return bool(tf.config.list_physical_devices('GPU'   )   )


A      :     Union[str, Any]										= 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    A    (  self			:							Union[str, Any]):
           _A     :	int      =					self.get_auto_remove_tmp_dir()
           _A     :	Optional[int]      =					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(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[str]      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertGreaterEqual(result['eval_accuracy']      ,							0.75)


    def    A    (  self			:							Optional[int]):
           _A     :	Dict      =					self.get_auto_remove_tmp_dir()
           _A     :	Optional[int]      =					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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_clm.main()
                  _A     :	Dict      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertLess(result['eval_perplexity']      ,							100)


    def    A    (  self			:							Dict):
           _A     :	List[Any]      =					self.get_auto_remove_tmp_dir()
           _A     :	Optional[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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_mlm.main()
                  _A     :	Tuple      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertLess(result['eval_perplexity']      ,							42)


    def    A    (  self			:							str):
           # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
           _A     :	int      =					7 if get_gpu_count() > 1 else 2

           _A     :	List[Any]      =					self.get_auto_remove_tmp_dir()
           _A     :	Optional[int]      =					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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_ner.main()
                  _A     :	int      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertGreaterEqual(result['accuracy']      ,							0.75)


    def    A    (  self			:							Any):
           _A     :	Optional[int]      =					self.get_auto_remove_tmp_dir()
           _A     :	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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_squad.main()
                  _A     :	int      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertGreaterEqual(result['f1']      ,							30)
                  self.assertGreaterEqual(result['exact']      ,							30)


    def    A    (  self			:							Tuple):
           _A     :	List[Any]      =					self.get_auto_remove_tmp_dir()
           _A     :	Optional[Any]      =					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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_swag.main()
                  _A     :	Union[str, Any]      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertGreaterEqual(result['val_accuracy']      ,							0.8)


    @slow
    def    A    (  self			:							Dict):
           _A     :	Union[str, Any]      =					self.get_auto_remove_tmp_dir()
           _A     :	Any      =					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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_summarization.main()
                  _A     :	Optional[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    A    (  self			:							Tuple):
           _A     :	List[str]      =					self.get_auto_remove_tmp_dir()
           _A     :	Optional[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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_translation.main()
                  _A     :	int      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertGreaterEqual(result['bleu']      ,							30)


    def    A    (  self			:							Optional[int]):
           _A     :	Optional[Any]      =					self.get_auto_remove_tmp_dir()
           _A     :	Dict      =					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(SCREAMING_SNAKE_CASE      ,							'argv'      ,							SCREAMING_SNAKE_CASE):
                  run_image_classification.main()
                  _A     :	Dict      =					get_results(SCREAMING_SNAKE_CASE)
                  self.assertGreaterEqual(result['accuracy']      ,							0.7)










'''simple docstring'''







from __future__ import annotations






def       lowerCAmelCase__       (				lowerCamelCase     :			list   ):
       _A     :	Union[str, Any]      =					sorted(lowerCamelCase   )
       _A     :	Tuple      =					len(lowerCamelCase   )
       _A     :	int      =					length >> 1
       return (
           (sorted_list[mid_index] + sorted_list[mid_index - 1]) / 2
           if length % 2 == 0
           else sorted_list[mid_index]
       )






def       lowerCAmelCase__       (				):
       import doctest

       doctest.testmod()


if __name__ == "__main__":
   main()






