




'''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


snake_case__      				=  [
    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)

snake_case__      				=  logging.getLogger()




def   snake_case__			(       )      ->      List[str]:
					A_       :   Any       =							argparse.ArgumentParser()
					parser.add_argument('''-f'''	)
					A_       :   Union[str, Any]       =							parser.parse_args()
					return args.f




def   snake_case__			(       lowerCamelCase__ :      Union[str, Any]	)      ->      Tuple:
					A_       :   int       =							{}
					A_       :   Dict       =							os.path.join(_snake_case	,						'''all_results.json'''	)
					if os.path.exists(_snake_case	):
										with open(_snake_case	,						'''r'''	) as f:
															A_       :   Optional[Any]       =							json.load(_snake_case	)
					else:
										raise ValueError(f'can\'t find {path}'	)
					return results




def   snake_case__			(       )      ->      Optional[Any]:
					return bool(tf.config.list_physical_devices('''GPU'''	)	)


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






class 			UpperCamelCase_   (a__   ):







						"""simple docstring"""


						@skip('''Skipping until shape inference for to_tf_dataset PR is merged.'''			)
						def 					_a			(   self					:				Tuple			):





											"""simple docstring"""







											A_       :   Optional[Any]       =							self.get_auto_remove_tmp_dir()
											A_       :   Tuple       =							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''']							,       0.75			)







						def 					_a			(   self					:				int			):





											"""simple docstring"""







											A_       :   int       =							self.get_auto_remove_tmp_dir()
											A_       :   str       =							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_       :   List[str]       =							get_results(_lowerCamelCase			)
																self.assertLess(result['''eval_perplexity''']							,       100			)







						def 					_a			(   self					:				int			):





											"""simple docstring"""







											A_       :   Optional[Any]       =							self.get_auto_remove_tmp_dir()
											A_       :   List[str]       =							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_       :   List[Any]       =							get_results(_lowerCamelCase			)
																self.assertLess(result['''eval_perplexity''']							,       42			)







						def 					_a			(   self					:				Optional[int]			):





											"""simple docstring"""







											A_       :   Union[str, Any]       =							7 if get_gpu_count() > 1 else 2

											A_       :   str       =							self.get_auto_remove_tmp_dir()
											A_       :   Optional[Any]       =							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_       :   Dict       =							get_results(_lowerCamelCase			)
																self.assertGreaterEqual(result['''accuracy''']							,       0.75			)







						def 					_a			(   self					:				Dict			):





											"""simple docstring"""







											A_       :   Dict       =							self.get_auto_remove_tmp_dir()
											A_       :   Optional[int]       =							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_       :   Optional[Any]       =							get_results(_lowerCamelCase			)
																self.assertGreaterEqual(result['''f1''']							,       30			)
																self.assertGreaterEqual(result['''exact''']							,       30			)







						def 					_a			(   self					:				Optional[int]			):





											"""simple docstring"""







											A_       :   int       =							self.get_auto_remove_tmp_dir()
											A_       :   str       =							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 					_a			(   self					:				Optional[int]			):





											"""simple docstring"""







											A_       :   List[str]       =							self.get_auto_remove_tmp_dir()
											A_       :   List[str]       =							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_       :   List[Any]       =							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 					_a			(   self					:				Union[str, Any]			):





											"""simple docstring"""







											A_       :   Tuple       =							self.get_auto_remove_tmp_dir()
											A_       :   Dict       =							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_       :   Any       =							get_results(_lowerCamelCase			)
																self.assertGreaterEqual(result['''bleu''']							,       30			)







						def 					_a			(   self					:				Optional[int]			):





											"""simple docstring"""







											A_       :   str       =							self.get_auto_remove_tmp_dir()
											A_       :   Optional[int]       =							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[Any]       =							get_results(_lowerCamelCase			)
																self.assertGreaterEqual(result['''accuracy''']							,       0.7			)











"""simple docstring"""







import sys

def _a (		_snake_case      ,       _snake_case							):







      """simple docstring"""






      if not numbers:
            return 0
      if target == 0 and target in numbers:
            return 0
      if not isinstance(_snake_case      ,       (list, tuple)							) or not all(
          isinstance(_snake_case      ,       _snake_case							) for number in numbers							):
            raise ValueError("""numbers must be an iterable of integers"""							)

      UpperCAmelCase							=       UpperCAmelCase							=       UpperCAmelCase							=       0
      UpperCAmelCase							=       sys.maxsize

      while right < len(_snake_case							):
            curr_sum += numbers[right]
            while curr_sum >= target and left <= right:
                  UpperCAmelCase							=       min(_snake_case      ,       right - left + 1							)
                  curr_sum -= numbers[left]
                  left += 1
            right += 1

      return 0 if min_len == sys.maxsize else min_len



