




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__    :							Optional[Any]       		=     logging.getLogger(__name__)


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





  _lowercase:			int      =     argparse.ArgumentParser(description='PyTorch Transformer Language Model'				)
  parser.add_argument('--model_name'       ,  type=UpperCamelCase       ,  default='transfo-xl-wt103'       ,  help='pretrained model name'				)
  parser.add_argument(
      '--split'       ,  type=UpperCamelCase       ,  default='test'       ,  choices=['all', 'valid', 'test']       ,  help='which split to evaluate'				)
  parser.add_argument('--batch_size'       ,  type=UpperCamelCase       ,  default=10       ,  help='batch size'				)
  parser.add_argument('--tgt_len'       ,  type=UpperCamelCase       ,  default=128       ,  help='number of tokens to predict'				)
  parser.add_argument('--ext_len'       ,  type=UpperCamelCase       ,  default=0       ,  help='length of the extended context'				)
  parser.add_argument('--mem_len'       ,  type=UpperCamelCase       ,  default=1_600       ,  help='length of the retained previous heads'				)
  parser.add_argument('--clamp_len'       ,  type=UpperCamelCase       ,  default=1_000       ,  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=UpperCamelCase       ,  required=UpperCamelCase       ,  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=UpperCamelCase       ,  default=''       ,  help='Can be used for distant debugging.'				)
  parser.add_argument('--server_port'       ,  type=UpperCamelCase       ,  default=''       ,  help='Can be used for distant debugging.'				)
  _lowercase:			Tuple      =     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=UpperCamelCase				)
    ptvsd.wait_for_attach()

  _lowercase:			Optional[Any]      =     torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu'				)
  logger.info('device: {}'.format(UpperCamelCase				)				)

  # 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:			Optional[Any]      =     TransfoXLCorpus.from_pretrained(args.model_name				)

  _lowercase:			Optional[int]      =     corpus.get_iterator('valid'       ,  args.batch_size       ,  args.tgt_len       ,  device=UpperCamelCase       ,  ext_len=args.ext_len				)
  _lowercase:			int      =     corpus.get_iterator('test'       ,  args.batch_size       ,  args.tgt_len       ,  device=UpperCamelCase       ,  ext_len=args.ext_len				)

  # Load a pre-trained model
  _lowercase:			Tuple      =     TransfoXLLMHeadModel.from_pretrained(args.model_name				)
  model.to(UpperCamelCase				)

  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:			Union[str, Any]      =     args.clamp_len
  if args.same_length:
    _lowercase:			Union[str, Any]      =     True

  ###############################################################################
  # Evaluation code
  ###############################################################################
  def evaluate(UpperCamelCase				):
    # Turn on evaluation mode which disables dropout.
    model.eval()
    _lowercase  ,       _lowercase:			Tuple      =     0, 0.0
    _lowercase:			List[str]      =     time.time()
    with torch.no_grad():
      _lowercase:			Tuple      =     None
      for idx, (data, target, seq_len) in enumerate(UpperCamelCase				):
        _lowercase:			Optional[Any]      =     model(UpperCamelCase       ,  lm_labels=UpperCamelCase       ,  mems=UpperCamelCase				)
        _lowercase  ,       _lowercase  ,       _lowercase:			Any      =     ret
        _lowercase:			Any      =     loss.mean()
        total_loss += seq_len * loss.item()
        total_len += seq_len
      _lowercase:			int      =     time.time() - start_time
    logger.info('Time : {:.2f}s, {:.2f}ms/segment'.format(UpperCamelCase       ,  1_000 * total_time / (idx + 1)				)				)
    return total_loss / total_len

  # Run on test data.
  if args.split == "all":
    _lowercase:			int      =     evaluate(UpperCamelCase				)
    _lowercase:			List[Any]      =     evaluate(UpperCamelCase				)
  elif args.split == "valid":
    _lowercase:			int      =     evaluate(UpperCamelCase				)
    _lowercase:			Any      =     None
  elif args.split == "test":
    _lowercase:			Tuple      =     evaluate(UpperCamelCase				)
    _lowercase:			Tuple      =     None

  def format_log(UpperCamelCase       ,  UpperCamelCase				):
    _lowercase:			str      =     '| {0} loss {1:5.2f} | {0} ppl {2:9.3f} '.format(UpperCamelCase       ,  UpperCamelCase       ,  math.exp(UpperCamelCase				)				)
    return log_str

  _lowercase:			Any      =     ''
  if valid_loss is not None:
    log_str += format_log(UpperCamelCase       ,  'valid'				)
  if test_loss is not None:
    log_str += format_log(UpperCamelCase       ,  'test'				)

  logger.info('=' * 100				)
  logger.info(UpperCamelCase				)
  logger.info('=' * 100				)


if __name__ == "__main__":
   main()






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__    :							Any       		=     [
    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__    :							Union[str, Any]       		=     logging.getLogger()


def 		_lowerCamelCase					(       )      ->		str:





  """simple docstring"""





  _lowercase:			int      =     argparse.ArgumentParser()
  parser.add_argument('-f'				)
  _lowercase:			Optional[Any]      =     parser.parse_args()
  return args.f


def 		_lowerCamelCase					(       UpperCamelCase				)      ->		Dict:





  """simple docstring"""





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


def 		_lowerCamelCase					(       )      ->		Any:





  """simple docstring"""





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


UpperCAmelCase__    :							List[Any]       		=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class   UpperCAmelCase    (      A ):






   @skip('Skipping until shape inference for to_tf_dataset PR is merged.'  )
   def 			_a    (						self :							Union[str, Any]  )->      Optional[Any]:
     _lowercase:			List[str]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     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'  )
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['eval_accuracy']    ,       0.75  )






   def 			_a    (						self :							Optional[Any]  )->      int:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_clm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       1_00  )






   def 			_a    (						self :							Union[str, Any]  )->      Optional[int]:
     _lowercase:			int      =     self.get_auto_remove_tmp_dir()
     _lowercase:			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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_mlm.main()
       _lowercase:			str      =     get_results(UpperCamelCase_  )
       self.assertLess(result['eval_perplexity']    ,       42  )






   def 			_a    (						self :							int  )->      Any:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     _lowercase:			List[Any]      =     7 if get_gpu_count() > 1 else 2

     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Optional[int]      =     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()
       _lowercase:			Optional[Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.75  )






   def 			_a    (						self :							Tuple  )->      Optional[int]:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_squad.main()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['f1']    ,       30  )
       self.assertGreaterEqual(result['exact']    ,       30  )






   def 			_a    (						self :							List[str]  )->      Tuple:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_swag.main()
       _lowercase:			List[str]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['val_accuracy']    ,       0.8  )






   @slow
   def 			_a    (						self :							Dict  )->      Tuple:
     _lowercase:			Dict      =     self.get_auto_remove_tmp_dir()
     _lowercase:			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(UpperCamelCase_    ,       'argv'    ,       UpperCamelCase_  ):
       run_summarization.main()
       _lowercase:			List[str]      =     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 			_a    (						self :							Tuple  )->      str:
     _lowercase:			List[Any]      =     self.get_auto_remove_tmp_dir()
     _lowercase:			List[str]      =     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()
       _lowercase:			Optional[int]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['bleu']    ,       30  )






   def 			_a    (						self :							int  )->      List[Any]:
     _lowercase:			str      =     self.get_auto_remove_tmp_dir()
     _lowercase:			Dict      =     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()
       _lowercase:			Union[str, Any]      =     get_results(UpperCamelCase_  )
       self.assertGreaterEqual(result['accuracy']    ,       0.7  )
