


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 							UpperCAmelCase_   (				)	->     List[Any]:







 """simple docstring"""


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







 """simple docstring"""


 _lowercase				 ={}
 _lowercase				 =os.path.join(__snake_case	,		'''all_results.json'''		)
 if os.path.exists(__snake_case		):
  with open(__snake_case	,		'''r'''		) as f:
   _lowercase				 =json.load(__snake_case		)
 else:
  raise ValueError(F"can't find {path}"		)
 return results
def 							UpperCAmelCase_   (				)	->     Optional[int]:







 """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 	__A   (self			)	->  Any:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertGreaterEqual(result['''eval_accuracy''']						,	0.75			)





     def 	__A   (self			)	->  int:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertLess(result['''eval_perplexity''']						,	1_0_0			)





     def 	__A   (self			)	->  Tuple:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertLess(result['''eval_perplexity''']						,	4_2			)





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

      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertGreaterEqual(result['''accuracy''']						,	0.75			)





     def 	__A   (self			)	->  str:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertGreaterEqual(result['''f1''']						,	3_0			)
       self.assertGreaterEqual(result['''exact''']						,	3_0			)





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertGreaterEqual(result['''val_accuracy''']						,	0.8			)





     @slow
     def 	__A   (self			)	->  str:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertGreaterEqual(result['''rouge1''']						,	1_0			)
       self.assertGreaterEqual(result['''rouge2''']						,	2			)
       self.assertGreaterEqual(result['''rougeL''']						,	7			)
       self.assertGreaterEqual(result['''rougeLsum''']						,	7			)





     @slow
     def 	__A   (self			)	->  List[str]:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertGreaterEqual(result['''bleu''']						,	3_0			)





     def 	__A   (self			)	->  str:
      _lowercase				 =self.get_auto_remove_tmp_dir()
      _lowercase				 =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				 =get_results(UpperCAmelCase			)
       self.assertGreaterEqual(result['''accuracy''']						,	0.7			)





from __future__ import annotations
def 							UpperCAmelCase_   (				__snake_case		)	->     list[int]:







 """simple docstring"""


 if not list_of_ints:
  return []

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 return _msd_radix_sort(__snake_case	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case		)	->     list[int]:







 """simple docstring"""


 if bit_position == 0 or len(__snake_case		) in [0, 1]:
  return list_of_ints

 _lowercase				 =[]
 _lowercase				 =[]
 # Split numbers based on bit at bit_position from the right
 for number in list_of_ints:
  if (number >> (bit_position - 1)) & 1:
   # number has a one at bit bit_position
   ones.append(__snake_case		)
  else:
   # number has a zero at bit bit_position
   zeros.append(__snake_case		)

    # recursively split both lists further
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)
 _lowercase				 =_msd_radix_sort(__snake_case	,		bit_position - 1		)

 # recombine lists
 _lowercase				 =zeros
 res.extend(__snake_case		)

 return res
def 							UpperCAmelCase_   (				__snake_case		)	->     List[str]:







 """simple docstring"""


 _lowercase				 =len(__snake_case		)
 if not list_of_ints or length == 1:
  return

 if min(__snake_case		) < 0:
  raise ValueError('''All numbers must be positive'''		)

 _lowercase				 =max(len(bin(__snake_case		)[2:]		) for x in list_of_ints		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		0	,		__snake_case		)
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)	->     Any:







 """simple docstring"""


 if bit_position == 0 or end_index - begin_index <= 1:
  return

 bit_position -= 1

 _lowercase				 =begin_index
 _lowercase				 =end_index - 1
 while i <= j:
  _lowercase				 =False
  if not (list_of_ints[i] >> bit_position) & 1:
   # found zero at the beginning
   i += 1
   _lowercase				 =True
  if (list_of_ints[j] >> bit_position) & 1:
   # found one at the end
   j -= 1
   _lowercase				 =True

  if changed:
   continue

  _lowercase ,       _lowercase				 =list_of_ints[j], list_of_ints[i]
  j -= 1
  if j != i:
   i += 1

 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)
 _msd_radix_sort_inplace(__snake_case	,		__snake_case	,		__snake_case	,		__snake_case		)


if __name__ == "__main__":
  import doctest

  doctest.testmod()

