





'''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


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)

snake_case_     =	logging.getLogger()
def 							_lowerCamelCase(  )		->  Tuple:
    A       :       Union[str, Any]							=						argparse.ArgumentParser()
    parser.add_argument('''-f'''		)
    A       :       int							=						parser.parse_args()
    return args.f
def 							_lowerCamelCase(  UpperCamelCase__    : int    ,       UpperCamelCase__    : Optional[int]="eval"		)		->  List[str]:
    A       :       Any							=						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}'''		)


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





class   _lowercase						(							a				):


   def 					snake_case				( self					):
       A       :       int							=						self.get_auto_remove_tmp_dir()
       A       :       int							=						f'''
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

       with patch.object(_UpperCAmelCase    ,   '''argv'''    ,   _UpperCAmelCase					):
           run_flax_glue.main()
           A       :       Optional[int]							=						get_results(_UpperCAmelCase					)
           self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.75					)


   @slow
   def 					snake_case				( self					):
       A       :       List[str]							=						self.get_auto_remove_tmp_dir()
       A       :       Optional[Any]							=						f'''
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

       with patch.object(_UpperCAmelCase    ,   '''argv'''    ,   _UpperCAmelCase					):
           run_clm_flax.main()
           A       :       List[Any]							=						get_results(_UpperCAmelCase					)
           self.assertLess(result['''eval_perplexity''']    ,   100					)


   @slow
   def 					snake_case				( self					):
       A       :       List[Any]							=						self.get_auto_remove_tmp_dir()
       A       :       List[Any]							=						f'''
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        '''.split()

       with patch.object(_UpperCAmelCase    ,   '''argv'''    ,   _UpperCAmelCase					):
           run_summarization_flax.main()
           A       :       int							=						get_results(_UpperCAmelCase    ,   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 					snake_case				( self					):
       A       :       Dict							=						self.get_auto_remove_tmp_dir()
       A       :       Union[str, Any]							=						f'''
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        '''.split()

       with patch.object(_UpperCAmelCase    ,   '''argv'''    ,   _UpperCAmelCase					):
           run_mlm_flax.main()
           A       :       str							=						get_results(_UpperCAmelCase					)
           self.assertLess(result['''eval_perplexity''']    ,   42					)


   @slow
   def 					snake_case				( self					):
       A       :       str							=						self.get_auto_remove_tmp_dir()
       A       :       Any							=						f'''
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            '''.split()

       with patch.object(_UpperCAmelCase    ,   '''argv'''    ,   _UpperCAmelCase					):
           run_ta_mlm_flax.main()
           A       :       int							=						get_results(_UpperCAmelCase					)
           self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.42					)


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

       A       :       int							=						self.get_auto_remove_tmp_dir()
       A       :       str							=						f'''
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        '''.split()

       with patch.object(_UpperCAmelCase    ,   '''argv'''    ,   _UpperCAmelCase					):
           run_flax_ner.main()
           A       :       Optional[int]							=						get_results(_UpperCAmelCase					)
           self.assertGreaterEqual(result['''eval_accuracy''']    ,   0.75					)
           self.assertGreaterEqual(result['''eval_f1''']    ,   0.3					)


   @slow
   def 					snake_case				( self					):
       A       :       Any							=						self.get_auto_remove_tmp_dir()
       A       :       Optional[Any]							=						f'''
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        '''.split()

       with patch.object(_UpperCAmelCase    ,   '''argv'''    ,   _UpperCAmelCase					):
           run_qa.main()
           A       :       int							=						get_results(_UpperCAmelCase					)
           self.assertGreaterEqual(result['''eval_f1''']    ,   30					)
           self.assertGreaterEqual(result['''eval_exact''']    ,   30					)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case_     =	{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case_     =	[
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     snake_case_     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
