




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						:    Dict      =  [
    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						:    int      =  logging.getLogger()



def    UpperCAmelCase__	(  ):
  lowercase     :Any   						=    argparse.ArgumentParser()
  parser.add_argument("-f" )
  lowercase     :List[str]   						=    parser.parse_args()
  return args.f



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



def    UpperCAmelCase__	(  ):
  return bool(tf.config.list_physical_devices("GPU" ) )


_UpperCAmelCase						:    int      =  logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class  __lowerCAmelCase							(				lowerCAmelCase):






       @skip("Skipping until shape inference for to_tf_dataset PR is merged."					)
       def        SCREAMING_SNAKE_CASE     ( self:       Tuple					):
         lowercase     :int   						=    self.get_auto_remove_tmp_dir()
         lowercase     :Dict   						=    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"					)
           lowercase     :List[Any]   						=    get_results(_lowerCAmelCase					)
           self.assertGreaterEqual(result["eval_accuracy"]							,       0.75					)






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]					):
         lowercase     :str   						=    self.get_auto_remove_tmp_dir()
         lowercase     :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(_lowerCAmelCase							,       "argv"							,       _lowerCAmelCase					):
           run_clm.main()
           lowercase     :List[str]   						=    get_results(_lowerCAmelCase					)
           self.assertLess(result["eval_perplexity"]							,       1_00					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :Tuple   						=    self.get_auto_remove_tmp_dir()
         lowercase     :Optional[int]   						=    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()
           lowercase     :int   						=    get_results(_lowerCAmelCase					)
           self.assertLess(result["eval_perplexity"]							,       42					)






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[int]					):
         # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
         lowercase     :Tuple   						=    7 if get_gpu_count() > 1 else 2

         lowercase     :Union[str, Any]   						=    self.get_auto_remove_tmp_dir()
         lowercase     :Dict   						=    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()
           lowercase     :Optional[int]   						=    get_results(_lowerCAmelCase					)
           self.assertGreaterEqual(result["accuracy"]							,       0.75					)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase     :Optional[Any]   						=    self.get_auto_remove_tmp_dir()
         lowercase     :str   						=    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()
           lowercase     :Optional[Any]   						=    get_results(_lowerCAmelCase					)
           self.assertGreaterEqual(result["f1"]							,       30					)
           self.assertGreaterEqual(result["exact"]							,       30					)






       def        SCREAMING_SNAKE_CASE     ( self:       int					):
         lowercase     :str   						=    self.get_auto_remove_tmp_dir()
         lowercase     :Optional[int]   						=    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()
           lowercase     :Dict   						=    get_results(_lowerCAmelCase					)
           self.assertGreaterEqual(result["val_accuracy"]							,       0.8					)






       @slow
       def        SCREAMING_SNAKE_CASE     ( self:       Any					):
         lowercase     :List[str]   						=    self.get_auto_remove_tmp_dir()
         lowercase     :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(_lowerCAmelCase							,       "argv"							,       _lowerCAmelCase					):
           run_summarization.main()
           lowercase     :Dict   						=    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        SCREAMING_SNAKE_CASE     ( self:       int					):
         lowercase     :Union[str, Any]   						=    self.get_auto_remove_tmp_dir()
         lowercase     :int   						=    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()
           lowercase     :str   						=    get_results(_lowerCAmelCase					)
           self.assertGreaterEqual(result["bleu"]							,       30					)






       def        SCREAMING_SNAKE_CASE     ( self:       Optional[Any]					):
         lowercase     :List[Any]   						=    self.get_auto_remove_tmp_dir()
         lowercase     :List[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()
           lowercase     :Optional[int]   						=    get_results(_lowerCAmelCase					)
           self.assertGreaterEqual(result["accuracy"]							,       0.7					)









import os

from apache_beam.io.filesystems import FileSystems
from apache_beam.pipeline import Pipeline

from .logging import get_logger


_UpperCAmelCase						:    List[str]      =  2 << 20  # 2mb
_UpperCAmelCase						:    Optional[int]      =  get_logger(__name__)




class  __lowerCAmelCase							(				lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :str   						=    self._options.get_all_options().get("runner"					)
         return runner in [None, "DirectRunner", "PortableRunner"]







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False ):
  lowercase     :str   						=    FileSystems
  if fs.exists(lowerCamelCase ):
    if force_upload:
      logger.info(F"Remote path already exist: {remote_file_path}. Overwriting it as force_upload=True." )
    else:
      logger.info(F"Remote path already exist: {remote_file_path}. Skipping it as force_upload=False." )
      return
  with fs.create(lowerCamelCase ) as remote_file:
    with open(lowerCamelCase,      "rb" ) as local_file:
      lowercase     :int   						=    local_file.read(lowerCamelCase )
      while chunk:
        remote_file.write(lowerCamelCase )
        lowercase     :List[Any]   						=    local_file.read(lowerCamelCase )







def    UpperCAmelCase__	(  lowerCamelCase,      lowerCamelCase,      lowerCamelCase=False ):
  lowercase     :Union[str, Any]   						=    FileSystems
  if os.path.exists(lowerCamelCase ):
    if force_download:
      logger.info(F"Local path already exist: {remote_file_path}. Overwriting it as force_upload=True." )
    else:
      logger.info(F"Local path already exist: {remote_file_path}. Skipping it as force_upload=False." )
      return
  with fs.open(lowerCamelCase ) as remote_file:
    with open(lowerCamelCase,      "wb" ) as local_file:
      lowercase     :Optional[Any]   						=    remote_file.read(lowerCamelCase )
      while chunk:
        local_file.write(lowerCamelCase )
        lowercase     :Dict   						=    remote_file.read(lowerCamelCase )



