code: 
  
  
  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       ():
         _A   : List[Any]       					=				argparse.ArgumentParser()
         parser.add_argument("-f"		)
         _A   : Union[str, Any]       					=				parser.parse_args()
         return args.f
  def 							_UpperCAmelCase       (UpperCamelCase__  :						Union[str, Any]		):
         _A   : List[Any]       					=				{}
         _A   : str       					=				os.path.join(UpperCamelCase__       ,							"all_results.json"		)
         if os.path.exists(UpperCamelCase__		):
                with open(UpperCamelCase__       ,							"r"		) as f:
                       _A   : Any       					=				json.load(UpperCamelCase__		)
         else:
                raise ValueError(f"can't find {path}"		)
         return results
  def 							_UpperCAmelCase       ():
         return bool(tf.config.list_physical_devices("GPU"		)		)
  
  
  lowerCAmelCase__   	=				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 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : Union[str, 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   : List[Any]       					=				get_results(__lowerCamelCase)
                     self.assertGreaterEqual(result["eval_accuracy"]				,		0.7_5)
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : Optional[int]       					=				self.get_auto_remove_tmp_dir()
              _A   : Dict       					=				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   : Optional[Any]       					=				get_results(__lowerCamelCase)
                     self.assertLess(result["eval_perplexity"]				,		1_0_0)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              _A   : Optional[Any]       					=				self.get_auto_remove_tmp_dir()
              _A   : Dict       					=				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   : Any       					=				get_results(__lowerCamelCase)
                     self.assertLess(result["eval_perplexity"]				,		4_2)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
              _A   : Optional[int]       					=				7 if get_gpu_count() > 1 else 2
  
              _A   : Tuple       					=				self.get_auto_remove_tmp_dir()
              _A   : 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   : List[Any]       					=				get_results(__lowerCamelCase)
                     self.assertGreaterEqual(result["accuracy"]				,		0.7_5)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : int       					=				self.get_auto_remove_tmp_dir()
              _A   : Optional[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(__lowerCamelCase				,		"argv"				,		__lowerCamelCase):
                     run_squad.main()
                     _A   : Optional[Any]       					=				get_results(__lowerCamelCase)
                     self.assertGreaterEqual(result["f1"]				,		3_0)
                     self.assertGreaterEqual(result["exact"]				,		3_0)
  
  
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              _A   : int       					=				self.get_auto_remove_tmp_dir()
              _A   : 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(__lowerCamelCase				,		"argv"				,		__lowerCamelCase):
                     run_swag.main()
                     _A   : Any       					=				get_results(__lowerCamelCase)
                     self.assertGreaterEqual(result["val_accuracy"]				,		0.8)
  
  
       @slow
       def 				_lowerCamelCase				(		self)    ->       Dict:
              _A   : Optional[Any]       					=				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[str]       					=				get_results(__lowerCamelCase)
                     self.assertGreaterEqual(result["rouge1"]				,		1_0)
                     self.assertGreaterEqual(result["rouge2"]				,		2)
                     self.assertGreaterEqual(result["rougeL"]				,		7)
                     self.assertGreaterEqual(result["rougeLsum"]				,		7)
  
  
       @slow
       def 				_lowerCamelCase				(		self)    ->       Optional[Any]:
              _A   : List[Any]       					=				self.get_auto_remove_tmp_dir()
              _A   : Tuple       					=				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   : Optional[int]       					=				get_results(__lowerCamelCase)
                     self.assertGreaterEqual(result["bleu"]				,		3_0)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              _A   : Optional[int]       					=				self.get_auto_remove_tmp_dir()
              _A   : Union[str, Any]       					=				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   : Dict       					=				get_results(__lowerCamelCase)
                     self.assertGreaterEqual(result["accuracy"]				,		0.7)
  
style_context: 
  
  
  import unittest
  
  import numpy as np
  
  from transformers.testing_utils import require_torch, require_vision
  from transformers.utils import is_torch_available, is_vision_available
  
  from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
  
  
  if is_torch_available():
      import torch
  
  if is_vision_available():
      from PIL import Image
  
      from transformers import MobileNetVaImageProcessor
  
  
  
  
  
  
  
  class   lowerCAmelCase__ (       unittest.TestCase):
  
  
  
  
       '''simple docstring'''
  
  
       def __init__(		self				,		__lowerCamelCase				,		__lowerCamelCase=7				,		__lowerCamelCase=3				,		__lowerCamelCase=1_8				,		__lowerCamelCase=3_0				,		__lowerCamelCase=4_0_0				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		__lowerCamelCase=True				,		__lowerCamelCase=None				,		)    ->       Tuple:
              _A   : int       					=				size if size is not None else {"shortest_edge": 2_0}
              _A   : Tuple       					=				crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
              _A   : str       					=				parent
              _A   : Any       					=				batch_size
              _A   : Dict       					=				num_channels
              _A   : int       					=				image_size
              _A   : Union[str, Any]       					=				min_resolution
              _A   : Tuple       					=				max_resolution
              _A   : Any       					=				do_resize
              _A   : Optional[int]       					=				size
              _A   : Tuple       					=				do_center_crop
              _A   : List[str]       					=				crop_size
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              return {
                  "do_resize": self.do_resize,
                  "size": self.size,
                  "do_center_crop": self.do_center_crop,
                  "crop_size": self.crop_size,
              }
  
  
  
  
  
  
  
  @require_torch
  @require_vision
  class   lowerCAmelCase__ (       a  ,					unittest.TestCase):
  
  
  
  
       '''simple docstring'''
       __SCREAMING_SNAKE_CASE						    =				MobileNetVaImageProcessor if is_vision_available() else None
  
  
       def 				_lowerCamelCase				(		self)    ->       Any:
              _A   : Dict       					=				MobileNetVaImageProcessingTester(self)
  
  
       @property
       def 				_lowerCamelCase				(		self)    ->       Dict:
              return self.image_processor_tester.prepare_image_processor_dict()
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_resize"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"size"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"do_center_crop"))
              self.assertTrue(hasattr(__lowerCamelCase				,		"center_crop"))
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              _A   : int       					=				self.image_processing_class.from_dict(self.image_processor_dict)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 2_0})
              self.assertEqual(image_processor.crop_size				,		{"height": 1_8, "width": 1_8})
  
              _A   : Any       					=				self.image_processing_class.from_dict(self.image_processor_dict				,		size=4_2				,		crop_size=8_4)
              self.assertEqual(image_processor.size				,		{"shortest_edge": 4_2})
              self.assertEqual(image_processor.crop_size				,		{"height": 8_4, "width": 8_4})
  
  
       def 				_lowerCamelCase				(		self)    ->       Union[str, Any]:
              pass
  
  
       def 				_lowerCamelCase				(		self)    ->       List[Any]:
              # Initialize image_processing
              _A   : Optional[int]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PIL images
              _A   : List[Any]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		Image.Image)
  
              # Test not batched input
              _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Dict       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       str:
              # Initialize image_processing
              _A   : List[Any]       					=				self.image_processing_class(**self.image_processor_dict)
              # create random numpy tensors
              _A   : int       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		numpify=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		np.ndarray)
  
              # Test not batched input
              _A   : Tuple       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : str       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
  
       def 				_lowerCamelCase				(		self)    ->       Tuple:
              # Initialize image_processing
              _A   : str       					=				self.image_processing_class(**self.image_processor_dict)
              # create random PyTorch tensors
              _A   : List[str]       					=				prepare_image_inputs(self.image_processor_tester				,		equal_resolution=__lowerCamelCase				,		torchify=__lowerCamelCase)
              for image in image_inputs:
                     self.assertIsInstance(__lowerCamelCase				,		torch.Tensor)
  
              # Test not batched input
              _A   : str       					=				image_processing(image_inputs[0]				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  
              # Test batched
              _A   : Any       					=				image_processing(__lowerCamelCase				,		return_tensors="pt").pixel_values
              self.assertEqual(
                  encoded_images.shape				,		(
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )				,		)
  