
"""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__   :						Optional[int] 							=     [
    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__   :						List[Any] 							=     logging.getLogger()



def 				a__			(					):






     '''simple docstring'''
     UpperCAmelCase__				:			Union[str, Any]							=    argparse.ArgumentParser()
     parser.add_argument("-f"		)
     UpperCAmelCase__				:			Tuple							=    parser.parse_args()
     return args.f



def 				a__			(					lowerCAmelCase						:							Union[str, Any]		):






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



def 				a__			(					):






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


A__   :						Dict 							=     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class 	_lowercase    (							lowerCAmelCase_					):


     '''simple docstring'''





     @skip("Skipping until shape inference for to_tf_dataset PR is merged."	)
     def 			lowerCAmelCase__							(   self	)->       Any:
          UpperCAmelCase__				:			Optional[int]							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			Optional[int]							=    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"	)
               UpperCAmelCase__				:			int							=    get_results(__UpperCamelCase	)
               self.assertGreaterEqual(result["eval_accuracy"]			,      0.75	)



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			List[Any]							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			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()
               UpperCAmelCase__				:			Union[str, Any]							=    get_results(__UpperCamelCase	)
               self.assertLess(result["eval_perplexity"]			,      1_00	)



     def 			lowerCAmelCase__							(   self	)->       List[Any]:
          UpperCAmelCase__				:			Union[str, Any]							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			List[Any]							=    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()
               UpperCAmelCase__				:			List[str]							=    get_results(__UpperCamelCase	)
               self.assertLess(result["eval_perplexity"]			,      42	)



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

          UpperCAmelCase__				:			Any							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			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()
               UpperCAmelCase__				:			Any							=    get_results(__UpperCamelCase	)
               self.assertGreaterEqual(result["accuracy"]			,      0.75	)



     def 			lowerCAmelCase__							(   self	)->       Tuple:
          UpperCAmelCase__				:			Optional[int]							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			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()
               UpperCAmelCase__				:			Dict							=    get_results(__UpperCamelCase	)
               self.assertGreaterEqual(result["f1"]			,      30	)
               self.assertGreaterEqual(result["exact"]			,      30	)



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          UpperCAmelCase__				:			Optional[int]							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			str							=    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()
               UpperCAmelCase__				:			int							=    get_results(__UpperCamelCase	)
               self.assertGreaterEqual(result["val_accuracy"]			,      0.8	)



     @slow
     def 			lowerCAmelCase__							(   self	)->       Optional[Any]:
          UpperCAmelCase__				:			List[Any]							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			List[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()
               UpperCAmelCase__				:			List[Any]							=    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 			lowerCAmelCase__							(   self	)->       str:
          UpperCAmelCase__				:			List[Any]							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			Union[str, Any]							=    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()
               UpperCAmelCase__				:			List[str]							=    get_results(__UpperCamelCase	)
               self.assertGreaterEqual(result["bleu"]			,      30	)



     def 			lowerCAmelCase__							(   self	)->       Any:
          UpperCAmelCase__				:			Tuple							=    self.get_auto_remove_tmp_dir()
          UpperCAmelCase__				:			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(__UpperCamelCase			,      "argv"			,      __UpperCamelCase	):
               run_image_classification.main()
               UpperCAmelCase__				:			Any							=    get_results(__UpperCamelCase	)
               self.assertGreaterEqual(result["accuracy"]			,      0.7	)





"""simple docstring"""




import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


A__   :						Optional[Any] 							=     set()
A__   :						List[str] 							=     get_logger(__name__)



def 				a__			(					lowerCAmelCase						:							Optional[str] = None		):






     '''simple docstring'''

     def decorator(lowerCAmelCase						:							Callable		):
          global _emitted_deprecation_warnings

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			Any							=    deprecated_class_or_function.__init__
               UpperCAmelCase__				:			Optional[int]							=    deprecated_class_or_function.__name__
          else:
               UpperCAmelCase__				:			str							=    deprecated_class_or_function
               UpperCAmelCase__				:			List[Any]							=    deprecated_function.__name__
               # Support deprecating __init__ class method: class name instead
               UpperCAmelCase__				:			List[str]							=    name if name != "__init__" else deprecated_function.__qualname__.split("."		)[-2]

          UpperCAmelCase__				:			Optional[Any]							=    (
              F"{name} is deprecated and will be removed in the next major version of datasets." + F" {help_message}"
              if help_message
              else ""
          )

          @wraps(lowerCAmelCase		)
          def wrapper(*lowerCAmelCase						:							int							,     **lowerCAmelCase						:							str		):
               UpperCAmelCase__				:			List[str]							=    hash(lowerCAmelCase		)
               if func_hash not in _emitted_deprecation_warnings:
                    warnings.warn(lowerCAmelCase							,     category=lowerCAmelCase							,     stacklevel=2		)
                    _emitted_deprecation_warnings.add(lowerCAmelCase		)
               return deprecated_function(*lowerCAmelCase							,     **lowerCAmelCase		)

          UpperCAmelCase__				:			List[str]							=    "deprecated"

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			List[str]							=    wrapper
               return deprecated_class_or_function
          else:
               return wrapper

     return decorator







class 	_lowercase    (							enum.EnumMeta					):


     '''simple docstring'''





     def __getattribute__(   cls			,      __UpperCamelCase	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[int]							=    super().__getattribute__(__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj



     def __getitem__(   cls			,      __UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			Union[str, Any]							=    super().__getitem__(__UpperCamelCase	)
          if member._on_access:
               member._on_access()
          return member



     def __call__(   cls			,      __UpperCamelCase			,      __UpperCamelCase=None			,      *, __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=1	)->       Dict:
          UpperCAmelCase__				:			Optional[int]							=    super().__call__(__UpperCamelCase			,      __UpperCamelCase			,      module=__UpperCamelCase			,      qualname=__UpperCamelCase			,      type=__UpperCamelCase			,      start=__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj







class 	_lowercase    (							enum.Enum						, metaclass=lowerCAmelCase_					):


     '''simple docstring'''





     def __new__(   cls			,      __UpperCamelCase	)->       Union[str, Any]:
          UpperCAmelCase__				:			int							=    object.__new__(cls	)
          UpperCAmelCase__				:			int							=    value
          UpperCAmelCase__				:			Optional[int]							=    member.deprecate
          return member



     @property
     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          return ""



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			Tuple							=    F" {self.help_message}" if self.help_message else ""
          warnings.warn(
              F"'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."
              + help_message			,      __UpperCamelCase			,      stacklevel=3			,      )



