


'''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


__A					:  List[str]											=      [
    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)

__A					:  Any											=      logging.getLogger()







def 				UpperCamelCase_			(    ):
  '''simple docstring'''





  lowerCAmelCase_			:      List[str]			=   argparse.ArgumentParser()
  parser.add_argument("""-f"""					)
  lowerCAmelCase_			:      Tuple			=   parser.parse_args()
  return args.f







def 				UpperCamelCase_			(    A__					:   List[str]							,    A__					:   int="eval"					):
  '''simple docstring'''





  lowerCAmelCase_			:      str			=   os.path.join(A__							,    f'{split}_results.json'					)
  if os.path.exists(A__					):
    with open(A__							,    """r"""					) as f:
      return json.load(A__					)
  raise ValueError(f'can\'t find {path}'					)


__A					:  List[str]											=      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    def __lowercase   (   self    :  Any		)   ->  str:
      lowerCAmelCase_			:      int			=   self.get_auto_remove_tmp_dir()
      lowerCAmelCase_			:      int			=   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(lowerCamelCase    ,						"""argv"""    ,						lowerCamelCase		):
        run_flax_glue.main()
        lowerCAmelCase_			:      Optional[int]			=   get_results(lowerCamelCase		)
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.75		)

    @slow
    def __lowercase   (   self    :  int		)   ->  Tuple:
      lowerCAmelCase_			:      List[Any]			=   self.get_auto_remove_tmp_dir()
      lowerCAmelCase_			:      Union[str, Any]			=   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(lowerCamelCase    ,						"""argv"""    ,						lowerCamelCase		):
        run_clm_flax.main()
        lowerCAmelCase_			:      Optional[int]			=   get_results(lowerCamelCase		)
        self.assertLess(result["""eval_perplexity"""]    ,						1_00		)

    @slow
    def __lowercase   (   self    :  Tuple		)   ->  Optional[int]:
      lowerCAmelCase_			:      int			=   self.get_auto_remove_tmp_dir()
      lowerCAmelCase_			:      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            --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(lowerCamelCase    ,						"""argv"""    ,						lowerCamelCase		):
        run_summarization_flax.main()
        lowerCAmelCase_			:      Optional[Any]			=   get_results(lowerCamelCase    ,						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 __lowercase   (   self    :  List[Any]		)   ->  List[Any]:
      lowerCAmelCase_			:      int			=   self.get_auto_remove_tmp_dir()
      lowerCAmelCase_			:      str			=   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(lowerCamelCase    ,						"""argv"""    ,						lowerCamelCase		):
        run_mlm_flax.main()
        lowerCAmelCase_			:      Tuple			=   get_results(lowerCamelCase		)
        self.assertLess(result["""eval_perplexity"""]    ,						42		)

    @slow
    def __lowercase   (   self    :  List[Any]		)   ->  Union[str, Any]:
      lowerCAmelCase_			:      Dict			=   self.get_auto_remove_tmp_dir()
      lowerCAmelCase_			:      Optional[Any]			=   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(lowerCamelCase    ,						"""argv"""    ,						lowerCamelCase		):
        run_ta_mlm_flax.main()
        lowerCAmelCase_			:      Dict			=   get_results(lowerCamelCase		)
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.42		)

    @slow
    def __lowercase   (   self    :  Any		)   ->  Dict:
      # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
      lowerCAmelCase_			:      int			=   7 if get_gpu_count() > 1 else 2

      lowerCAmelCase_			:      Optional[Any]			=   self.get_auto_remove_tmp_dir()
      lowerCAmelCase_			:      List[str]			=   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(lowerCamelCase    ,						"""argv"""    ,						lowerCamelCase		):
        run_flax_ner.main()
        lowerCAmelCase_			:      int			=   get_results(lowerCamelCase		)
        self.assertGreaterEqual(result["""eval_accuracy"""]    ,						0.75		)
        self.assertGreaterEqual(result["""eval_f1"""]    ,						0.3		)





    @slow
    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      lowerCAmelCase_			:      str			=   self.get_auto_remove_tmp_dir()
      lowerCAmelCase_			:      Dict			=   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(lowerCamelCase    ,						"""argv"""    ,						lowerCamelCase		):
        run_qa.main()
        lowerCAmelCase_			:      Optional[Any]			=   get_results(lowerCamelCase		)
        self.assertGreaterEqual(result["""eval_f1"""]    ,						30		)
        self.assertGreaterEqual(result["""eval_exact"""]    ,						30		)







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



