




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    lowerCamelCase__			(		):





       '''simple docstring'''



       __lowerCamelCase  =		argparse.ArgumentParser()
       parser.add_argument("""-f"""      )
       __lowerCamelCase  =		parser.parse_args()
       return args.f




def    lowerCamelCase__			(		A__			:   List[str]      ):





       '''simple docstring'''



       __lowerCamelCase  =		{}
       __lowerCamelCase  =		os.path.join(A__    ,     """all_results.json"""      )
       if os.path.exists(A__      ):
              with open(A__    ,     """r"""      ) as f:
                     __lowerCamelCase  =		json.load(A__      )
       else:
              raise ValueError(f'can\'t find {path}'      )
       return results




def    lowerCamelCase__			(		):





       '''simple docstring'''



       return bool(tf.config.list_physical_devices("""GPU"""      )      )


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





class        lowerCamelCase__(   __lowerCamelCase):
      @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""  )
      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_text_classification.main()
                    # Reset the mixed precision policy so we don't break other tests
                    tf.keras.mixed_precision.set_global_policy("""float32"""  )
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertGreaterEqual(result["""eval_accuracy"""]       ,			0.75  )
      def      lowerCAmelCase__		(							self:					Optional[Any]  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_clm.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertLess(result["""eval_perplexity"""]       ,			1_00  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_mlm.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertLess(result["""eval_perplexity"""]       ,			42  )
      def      lowerCAmelCase__		(							self:					int  ):
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             __lowerCamelCase  =		7 if get_gpu_count() > 1 else 2

             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_ner.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertGreaterEqual(result["""accuracy"""]       ,			0.75  )
      def      lowerCAmelCase__		(							self:					Union[str, Any]  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_squad.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertGreaterEqual(result["""f1"""]       ,			30  )
                    self.assertGreaterEqual(result["""exact"""]       ,			30  )
      def      lowerCAmelCase__		(							self:					List[Any]  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_swag.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertGreaterEqual(result["""val_accuracy"""]       ,			0.8  )
      @slow
      def      lowerCAmelCase__		(							self:					List[str]  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_summarization.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertGreaterEqual(result["""rouge1"""]       ,			10  )
                    self.assertGreaterEqual(result["""rouge2"""]       ,			2  )
                    self.assertGreaterEqual(result["""rougeL"""]       ,			7  )
                    self.assertGreaterEqual(result["""rougeLsum"""]       ,			7  )
      @slow
      def      lowerCAmelCase__		(							self:					Optional[int]  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_translation.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertGreaterEqual(result["""bleu"""]       ,			30  )




      def      lowerCAmelCase__		(							self:					Tuple  ):
             __lowerCamelCase  =		self.get_auto_remove_tmp_dir()
             __lowerCamelCase  =		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(UpperCamelCase_       ,			"""argv"""       ,			UpperCamelCase_  ):
                    run_image_classification.main()
                    __lowerCamelCase  =		get_results(UpperCamelCase_  )
                    self.assertGreaterEqual(result["""accuracy"""]       ,			0.7  )







from typing import Callable




def    lowerCamelCase__			(		A__			:   Callable      ):





       '''simple docstring'''



       def wrapper(A__			:   int      ):
              __lowerCamelCase  =		function_with_docstring.__doc__
              return target_function

       return wrapper

