






'''simple docstring'''

import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__     =       [
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        'text-classification',
        'language-modeling',
        'summarization',
        'token-classification',
        'question-answering',
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
     import run_clm_flax
     import run_flax_glue
     import run_flax_ner
     import run_mlm_flax
     import run_qa
     import run_summarization_flax
     import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__     =       logging.getLogger()







def    lowercase__			(				)->			str:
      UpperCamelCase      = argparse.ArgumentParser()
      parser.add_argument("""-f"""   )
      UpperCamelCase      = parser.parse_args()
      return args.f







def    lowercase__			(				__UpperCamelCase		,						__UpperCamelCase="eval"   )->			List[Any]:
      UpperCamelCase      = os.path.join(__UpperCamelCase		,						F"{split}_results.json"   )
      if os.path.exists(__UpperCamelCase   ):
            with open(__UpperCamelCase		,						"""r"""   ) as f:
                  return json.load(__UpperCamelCase   )
      raise ValueError(F"can't find {path}"   )


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






class     a_						(      lowerCamelCase    ):

  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = F"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

        with patch.object(_SCREAMING_SNAKE_CASE      ,		"""argv"""      ,		_SCREAMING_SNAKE_CASE				):
              run_flax_glue.main()
              UpperCamelCase      = get_results(_SCREAMING_SNAKE_CASE				)
              self.assertGreaterEqual(result["""eval_accuracy"""]      ,		0.7_5				)

  @slow
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = F"\n            run_clm_flax.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 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(_SCREAMING_SNAKE_CASE      ,		"""argv"""      ,		_SCREAMING_SNAKE_CASE				):
              run_clm_flax.main()
              UpperCamelCase      = get_results(_SCREAMING_SNAKE_CASE				)
              self.assertLess(result["""eval_perplexity"""]      ,		100				)

  @slow
  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = 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            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

        with patch.object(_SCREAMING_SNAKE_CASE      ,		"""argv"""      ,		_SCREAMING_SNAKE_CASE				):
              run_summarization_flax.main()
              UpperCamelCase      = get_results(_SCREAMING_SNAKE_CASE      ,		split="""test"""				)
              self.assertGreaterEqual(result["""test_rouge1"""]      ,		10				)
              self.assertGreaterEqual(result["""test_rouge2"""]      ,		2				)
              self.assertGreaterEqual(result["""test_rougeL"""]      ,		7				)
              self.assertGreaterEqual(result["""test_rougeLsum"""]      ,		7				)

  @slow
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = 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            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

        with patch.object(_SCREAMING_SNAKE_CASE      ,		"""argv"""      ,		_SCREAMING_SNAKE_CASE				):
              run_mlm_flax.main()
              UpperCamelCase      = get_results(_SCREAMING_SNAKE_CASE				)
              self.assertLess(result["""eval_perplexity"""]      ,		42				)

  @slow
  def   A__						(				self				)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = F"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(_SCREAMING_SNAKE_CASE      ,		"""argv"""      ,		_SCREAMING_SNAKE_CASE				):
              run_ta_mlm_flax.main()
              UpperCamelCase      = get_results(_SCREAMING_SNAKE_CASE				)
              self.assertGreaterEqual(result["""eval_accuracy"""]      ,		0.4_2				)

  @slow
  def   A__						(				self				)  ->				str:



        """simple docstring"""






        UpperCamelCase      = 7 if get_gpu_count() > 1 else 2

        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = F"\n            run_flax_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            --logging_steps 2 --eval_steps 2\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(_SCREAMING_SNAKE_CASE      ,		"""argv"""      ,		_SCREAMING_SNAKE_CASE				):
              run_flax_ner.main()
              UpperCamelCase      = get_results(_SCREAMING_SNAKE_CASE				)
              self.assertGreaterEqual(result["""eval_accuracy"""]      ,		0.7_5				)
              self.assertGreaterEqual(result["""eval_f1"""]      ,		0.3				)


  @slow
  def   A__						(				self				)  ->				Optional[int]:



        """simple docstring"""






        UpperCamelCase      = self.get_auto_remove_tmp_dir()
        UpperCamelCase      = 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            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(_SCREAMING_SNAKE_CASE      ,		"""argv"""      ,		_SCREAMING_SNAKE_CASE				):
              run_qa.main()
              UpperCamelCase      = get_results(_SCREAMING_SNAKE_CASE				)
              self.assertGreaterEqual(result["""eval_f1"""]      ,		30				)
              self.assertGreaterEqual(result["""eval_exact"""]      ,		30				)








'''simple docstring'''

import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE__     =       'scheduler_config.json'






class     a_						(      lowerCamelCase    ):
  lowercase							=				1
  lowercase							=				2
  lowercase							=				3
  lowercase							=				4
  lowercase							=				5
  lowercase							=				6
  lowercase							=				7
  lowercase							=				8
  lowercase							=				9
  lowercase							=				10
  lowercase							=				11
  lowercase							=				12
  lowercase							=				13
  lowercase							=				14






@dataclass
class     a_						(      lowerCamelCase    ):
  lowercase							=				42






class     a_						:
  lowercase							=				SCHEDULER_CONFIG_NAME
  lowercase							=				[]
  lowercase							=				True

  @classmethod
  def   A__						(				cls      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE = None      ,		_SCREAMING_SNAKE_CASE=False      ,		**_SCREAMING_SNAKE_CASE      ,		)  ->				Optional[Any]:



        """simple docstring"""






        UpperCamelCase  ,UpperCamelCase  ,UpperCamelCase      = cls.load_config(
            pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE      ,		subfolder=_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		return_commit_hash=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE      ,		)
        return cls.from_config(_SCREAMING_SNAKE_CASE      ,		return_unused_kwargs=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  def   A__						(				self      ,		_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE = False      ,		**_SCREAMING_SNAKE_CASE				)  ->				Dict:



        """simple docstring"""






        self.save_config(save_directory=_SCREAMING_SNAKE_CASE      ,		push_to_hub=_SCREAMING_SNAKE_CASE      ,		**_SCREAMING_SNAKE_CASE				)

  @property
  def   A__						(				self				)  ->				Tuple:



        """simple docstring"""






        return self._get_compatibles()


  @classmethod
  def   A__						(				cls				)  ->				List[Any]:



        """simple docstring"""






        UpperCamelCase      = list(set([cls.__name__] + cls._compatibles				)				)
        UpperCamelCase      = importlib.import_module(__name__.split("""."""				)[0]				)
        UpperCamelCase      = [
            getattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				) for c in compatible_classes_str if hasattr(_SCREAMING_SNAKE_CASE      ,		_SCREAMING_SNAKE_CASE				)
        ]
        return compatible_classes
