
'''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__ :		List[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)

A__ :		Optional[int]					=logging.getLogger()







def 							A_  (    )					->							List[Any]:





  """simple docstring"""

  __A		:  List[str]       				=		argparse.ArgumentParser()
  parser.add_argument("""-f"""			)
  __A		:  Union[str, Any]       				=		parser.parse_args()
  return args.f







def 							A_  (    __SCREAMING_SNAKE_CASE    :	List[str]			)					->							Optional[Any]:





  """simple docstring"""

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







def 							A_  (    )					->							Dict:





  """simple docstring"""

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


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







class 			__A					(			_SCREAMING_SNAKE_CASE      ):
 @skip("""Skipping until shape inference for to_tf_dataset PR is merged."""							)
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  str       				=		self.get_auto_remove_tmp_dir()
   __A		:  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(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		:  Optional[int]       				=		get_results(lowerCamelCase							)
     self.assertGreaterEqual(result["""eval_accuracy"""]					, 0.75							)
 def 		lowercase_( self      :     List[str]							):



   """simple docstring"""





   __A		:  Tuple       				=		self.get_auto_remove_tmp_dir()
   __A		:  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()
     __A		:  int       				=		get_results(lowerCamelCase							)
     self.assertLess(result["""eval_perplexity"""]					, 1_00							)
 def 		lowercase_( self      :     Optional[Any]							):



   """simple docstring"""





   __A		:  Optional[Any]       				=		self.get_auto_remove_tmp_dir()
   __A		:  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()
     __A		:  Optional[Any]       				=		get_results(lowerCamelCase							)
     self.assertLess(result["""eval_perplexity"""]					, 42							)
 def 		lowercase_( self      :     int							):



   """simple docstring"""





   __A		:  int       				=		7 if get_gpu_count() > 1 else 2

   __A		:  Optional[Any]       				=		self.get_auto_remove_tmp_dir()
   __A		:  Optional[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"""]					, 0.75							)
 def 		lowercase_( self      :     Optional[int]							):



   """simple docstring"""





   __A		:  Tuple       				=		self.get_auto_remove_tmp_dir()
   __A		:  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		:  Dict       				=		get_results(lowerCamelCase							)
     self.assertGreaterEqual(result["""f1"""]					, 30							)
     self.assertGreaterEqual(result["""exact"""]					, 30							)
 def 		lowercase_( self      :     Union[str, Any]							):



   """simple docstring"""





   __A		:  Union[str, Any]       				=		self.get_auto_remove_tmp_dir()
   __A		:  Dict       				=		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		:  int       				=		get_results(lowerCamelCase							)
     self.assertGreaterEqual(result["""val_accuracy"""]					, 0.8							)
 @slow
 def 		lowercase_( self      :     Any							):



   """simple docstring"""





   __A		:  Tuple       				=		self.get_auto_remove_tmp_dir()
   __A		:  List[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()
     __A		:  Any       				=		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 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Optional[int]       				=		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		:  int       				=		get_results(lowerCamelCase							)
     self.assertGreaterEqual(result["""bleu"""]					, 30							)






 def 		lowercase_( self      :     Tuple							):



   """simple docstring"""





   __A		:  Tuple       				=		self.get_auto_remove_tmp_dir()
   __A		:  Tuple       				=		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		:  Optional[int]       				=		get_results(lowerCamelCase							)
     self.assertGreaterEqual(result["""accuracy"""]					, 0.7							)





'''simple docstring'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



