






'''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     						= [
    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     						= logging.getLogger()




def 							_SCREAMING_SNAKE_CASE	(					)     ->			Any:







   """simple docstring"""
   __snake_case					:   Any  				=			argparse.ArgumentParser()
   parser.add_argument('-f'	)
   __snake_case					:   List[str]  				=			parser.parse_args()
   return args.f




def 							_SCREAMING_SNAKE_CASE	(					A	:							int	)     ->			Dict:







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




def 							_SCREAMING_SNAKE_CASE	(					)     ->			List[Any]:







   """simple docstring"""
   return bool(tf.config.list_physical_devices('GPU'	)	)


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





class a_     (     UpperCamelCase_				):







   @skip('Skipping until shape inference for to_tf_dataset PR is merged.')
   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   int  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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')
         __snake_case					:   List[Any]  				=			get_results(_lowerCAmelCase)
         self.assertGreaterEqual(result['eval_accuracy']       , 0.75)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   str  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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()
         __snake_case					:   List[str]  				=			get_results(_lowerCAmelCase)
         self.assertLess(result['eval_perplexity']       , 1_0_0)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   Tuple  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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()
         __snake_case					:   int  				=			get_results(_lowerCAmelCase)
         self.assertLess(result['eval_perplexity']       , 4_2)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[Any]:
      """simple docstring"""






      __snake_case					:   Tuple  				=			7 if get_gpu_count() > 1 else 2

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







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   Optional[Any]  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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()
         __snake_case					:   Optional[Any]  				=			get_results(_lowerCAmelCase)
         self.assertGreaterEqual(result['f1']       , 3_0)
         self.assertGreaterEqual(result['exact']       , 3_0)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   Tuple:
      """simple docstring"""






      __snake_case					:   str  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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()
         __snake_case					:   Dict  				=			get_results(_lowerCAmelCase)
         self.assertGreaterEqual(result['val_accuracy']       , 0.8)







   @slow
   def      SCREAMING_SNAKE_CASE__       (self)      ->   Optional[int]:
      """simple docstring"""






      __snake_case					:   List[str]  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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()
         __snake_case					:   Dict  				=			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      SCREAMING_SNAKE_CASE__       (self)      ->   List[Any]:
      """simple docstring"""






      __snake_case					:   Union[str, Any]  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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()
         __snake_case					:   str  				=			get_results(_lowerCAmelCase)
         self.assertGreaterEqual(result['bleu']       , 3_0)







   def      SCREAMING_SNAKE_CASE__       (self)      ->   str:
      """simple docstring"""






      __snake_case					:   List[Any]  				=			self.get_auto_remove_tmp_dir()
      __snake_case					:   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()
         __snake_case					:   Optional[int]  				=			get_results(_lowerCAmelCase)
         self.assertGreaterEqual(result['accuracy']       , 0.7)





import argparse
import logging
import math
import time

import torch

from transformers import TransfoXLCorpus, TransfoXLLMHeadModel


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
_UpperCAmelCase						:    Dict      =  logging.getLogger(__name__)



def    UpperCAmelCase__	(  ):
  lowercase     :int   						=    argparse.ArgumentParser(description="PyTorch Transformer Language Model" )
  parser.add_argument("--model_name",      type=lowerCamelCase,      default="transfo-xl-wt103",      help="pretrained model name" )
  parser.add_argument(
      "--split",      type=lowerCamelCase,      default="test",      choices=["all", "valid", "test"],      help="which split to evaluate" )
  parser.add_argument("--batch_size",      type=lowerCamelCase,      default=10,      help="batch size" )
  parser.add_argument("--tgt_len",      type=lowerCamelCase,      default=128,      help="number of tokens to predict" )
  parser.add_argument("--ext_len",      type=lowerCamelCase,      default=0,      help="length of the extended context" )
  parser.add_argument("--mem_len",      type=lowerCamelCase,      default=1600,      help="length of the retained previous heads" )
  parser.add_argument("--clamp_len",      type=lowerCamelCase,      default=1000,      help="max positional embedding index" )
  parser.add_argument("--no_cuda",      action="store_true",      help="Do not use CUDA even though CUA is available" )
  parser.add_argument("--work_dir",      type=lowerCamelCase,      required=lowerCamelCase,      help="path to the work_dir" )
  parser.add_argument("--no_log",      action="store_true",      help="do not log the eval result" )
  parser.add_argument("--same_length",      action="store_true",      help="set same length attention with masking" )
  parser.add_argument("--server_ip",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  parser.add_argument("--server_port",      type=lowerCamelCase,      default="",      help="Can be used for distant debugging." )
  lowercase     :Any   						=    parser.parse_args()
  assert args.ext_len >= 0, "extended context length must be non-negative"

  if args.server_ip and args.server_port:
    # Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
    import ptvsd

    print("Waiting for debugger attach" )
    ptvsd.enable_attach(address=(args.server_ip, args.server_port),      redirect_output=lowerCamelCase )
    ptvsd.wait_for_attach()

  lowercase     :Any   						=    torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
  logger.info("device: {}".format(lowerCamelCase ) )

  # Load a pre-processed dataset
  # You can also build the corpus yourself using TransfoXLCorpus methods
  # The pre-processing involve computing word frequencies to prepare the Adaptive input and SoftMax
  # and tokenizing the dataset
  # The pre-processed corpus is a convertion (using the conversion script )
  lowercase     :Dict   						=    TransfoXLCorpus.from_pretrained(args.model_name )

  lowercase     :Optional[int]   						=    corpus.get_iterator("valid",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )
  lowercase     :Union[str, Any]   						=    corpus.get_iterator("test",      args.batch_size,      args.tgt_len,      device=lowerCamelCase,      ext_len=args.ext_len )

  # Load a pre-trained model
  lowercase     :List[Any]   						=    TransfoXLLMHeadModel.from_pretrained(args.model_name )
  model.to(lowerCamelCase )

  logger.info(
      "Evaluating with bsz {} tgt_len {} ext_len {} mem_len {} clamp_len {}".format(
          args.batch_size,      args.tgt_len,      args.ext_len,      args.mem_len,      args.clamp_len ) )

  model.reset_memory_length(args.mem_len )
  if args.clamp_len > 0:
    lowercase     :Tuple   						=    args.clamp_len
  if args.same_length:
    lowercase     :Tuple   						=    True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(lowerCamelCase ):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    lowercase						,     lowercase     :Optional[Any]   						=    0, 0.0
    lowercase     :Optional[Any]   						=    time.time()
    with torch.no_grad():
      lowercase     :Dict   						=    None
      for idx, (data, target, seq_len) in enumerate(lowerCamelCase ):
        lowercase     :Optional[Any]   						=    model(lowerCamelCase,      lm_labels=lowerCamelCase,      mems=lowerCamelCase )
        lowercase						,     lowercase						,     lowercase     :Optional[int]   						=    ret
        lowercase     :Optional[Any]   						=    loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      lowercase     :List[Any]   						=    time.time() - start_time
    logger.info("Time : {:.2f}s, {:.2f}ms/segment".format(lowerCamelCase,      1000 * total_time / (idx + 1) ) )
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    lowercase     :Union[str, Any]   						=    evaluate(lowerCamelCase )
    lowercase     :List[str]   						=    evaluate(lowerCamelCase )
  elif args.split == "valid":
    lowercase     :Optional[int]   						=    evaluate(lowerCamelCase )
    lowercase     :Dict   						=    None
  elif args.split == "test":
    lowercase     :int   						=    evaluate(lowerCamelCase )
    lowercase     :Optional[Any]   						=    None

  def format_log(lowerCamelCase,      lowerCamelCase ):
    lowercase     :List[str]   						=    "| {0} loss {1:5.2f} | {0} ppl {2:9.3f} ".format(lowerCamelCase,      lowerCamelCase,      math.exp(lowerCamelCase ) )
    return log_str

  lowercase     :Optional[int]   						=    ""
  if valid_loss is not None:
    log_str += format_log(lowerCamelCase,      "valid" )
  if test_loss is not None:
    log_str += format_log(lowerCamelCase,      "test" )

  logger.info("=" * 100 )
  logger.info(lowerCamelCase )
  logger.info("=" * 100 )


if __name__ == "__main__":
     main()



