



'''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


_lowerCAmelCase										=     [
    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)

_lowerCAmelCase										=     logging.getLogger()
def        _SCREAMING_SNAKE_CASE  (	):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= argparse.ArgumentParser()
    parser.add_argument("""-f"""      )
    lowerCAmelCase__			:  Union[str, Any]											= parser.parse_args()
    return args.f
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase="eval"      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, 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}"""      )


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






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_					):
  '''simple docstring'''




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  List[Any]											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  Optional[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()
          lowerCAmelCase__			:  List[Any]											= get_results(__UpperCAmelCase				)
          self.assertGreaterEqual(result["""eval_accuracy"""]  ,0.7_5				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Dict:
      lowerCAmelCase__			:  str											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  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()
          lowerCAmelCase__			:  Any											= get_results(__UpperCAmelCase				)
          self.assertLess(result["""eval_perplexity"""]  ,100				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		int:
      lowerCAmelCase__			:  Any											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  Union[str, 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()
          lowerCAmelCase__			:  List[str]											= 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 							UpperCAmelCase_     (   self				)   ->		Union[str, Any]:
      lowerCAmelCase__			:  int											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  List[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()
          lowerCAmelCase__			:  List[Any]											= get_results(__UpperCAmelCase				)
          self.assertLess(result["""eval_perplexity"""]  ,42				)




  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Any:
      lowerCAmelCase__			:  Dict											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  List[str]											= 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()
          lowerCAmelCase__			:  Tuple											= get_results(__UpperCAmelCase				)
          self.assertGreaterEqual(result["""eval_accuracy"""]  ,0.4_2				)




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

      lowerCAmelCase__			:  Any											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  List[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()
          lowerCAmelCase__			:  int											= get_results(__UpperCAmelCase				)
          self.assertGreaterEqual(result["""eval_accuracy"""]  ,0.7_5				)
          self.assertGreaterEqual(result["""eval_f1"""]  ,0.3				)






  @slow
  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      lowerCAmelCase__			:  List[str]											= self.get_auto_remove_tmp_dir()
      lowerCAmelCase__			:  Optional[int]											= 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()
          lowerCAmelCase__			:  str											= 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_torch_available


_lowerCAmelCase										=     {
    '''configuration_blip_2''': [
        '''BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Blip2Config''',
        '''Blip2QFormerConfig''',
        '''Blip2VisionConfig''',
    ],
    '''processing_blip_2''': ['''Blip2Processor'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase										=     [
     '''BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''Blip2Model''',
     '''Blip2QFormerModel''',
     '''Blip2PreTrainedModel''',
     '''Blip2ForConditionalGeneration''',
     '''Blip2VisionModel''',
 ]

if TYPE_CHECKING:
 from .configuration_blip_a import (
     BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
     BlipaConfig,
     BlipaQFormerConfig,
     BlipaVisionConfig,
 )
 from .processing_blip_a import BlipaProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_blip_a import (
      BLIP_2_PRETRAINED_MODEL_ARCHIVE_LIST,
      BlipaForConditionalGeneration,
      BlipaModel,
      BlipaPreTrainedModel,
      BlipaQFormerModel,
      BlipaVisionModel,
  )

else:
 import sys

 _lowerCAmelCase										=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


