


'''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


UpperCAmelCase    =					[
    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)

UpperCAmelCase    =					logging.getLogger()






def  __lowerCAmelCase					()->						Dict:




      """simple docstring"""

      snake_case_ =   argparse.ArgumentParser()
      parser.add_argument('''-f'''      )
      snake_case_ =   parser.parse_args()
      return args.f






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE="eval"      )->						Union[str, Any]:




      """simple docstring"""

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


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







class lowerCAmelCase_		(				_A ):

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.get_auto_remove_tmp_dir()
       snake_case_ =   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(_UpperCAmelCase	,   '''argv'''	,   _UpperCAmelCase      ):
             run_flax_glue.main()
             snake_case_ =   get_results(_UpperCAmelCase      )
             self.assertGreaterEqual(result['''eval_accuracy''']	,   0.75      )



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.get_auto_remove_tmp_dir()
       snake_case_ =   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(_UpperCAmelCase	,   '''argv'''	,   _UpperCAmelCase      ):
             run_clm_flax.main()
             snake_case_ =   get_results(_UpperCAmelCase      )
             self.assertLess(result['''eval_perplexity''']	,   1_00      )



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.get_auto_remove_tmp_dir()
       snake_case_ =   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(_UpperCAmelCase	,   '''argv'''	,   _UpperCAmelCase      ):
             run_summarization_flax.main()
             snake_case_ =   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      ):
       snake_case_ =   self.get_auto_remove_tmp_dir()
       snake_case_ =   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(_UpperCAmelCase	,   '''argv'''	,   _UpperCAmelCase      ):
             run_mlm_flax.main()
             snake_case_ =   get_results(_UpperCAmelCase      )
             self.assertLess(result['''eval_perplexity''']	,   42      )



 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.get_auto_remove_tmp_dir()
       snake_case_ =   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(_UpperCAmelCase	,   '''argv'''	,   _UpperCAmelCase      ):
             run_ta_mlm_flax.main()
             snake_case_ =   get_results(_UpperCAmelCase      )
             self.assertGreaterEqual(result['''eval_accuracy''']	,   0.42      )



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

       snake_case_ =   self.get_auto_remove_tmp_dir()
       snake_case_ =   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(_UpperCAmelCase	,   '''argv'''	,   _UpperCAmelCase      ):
             run_flax_ner.main()
             snake_case_ =   get_results(_UpperCAmelCase      )
             self.assertGreaterEqual(result['''eval_accuracy''']	,   0.75      )
             self.assertGreaterEqual(result['''eval_f1''']	,   0.3      )





 @slow
 def 		UpperCamelCase__  (			self      ):
       snake_case_ =   self.get_auto_remove_tmp_dir()
       snake_case_ =   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(_UpperCAmelCase	,   '''argv'''	,   _UpperCAmelCase      ):
             run_qa.main()
             snake_case_ =   get_results(_UpperCAmelCase      )
             self.assertGreaterEqual(result['''eval_f1''']	,   30      )
             self.assertGreaterEqual(result['''eval_exact''']	,   30      )



from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


try:
   if not (is_transformers_available() and is_torch_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
   from .notes_encoder import SpectrogramNotesEncoder
   from .continous_encoder import SpectrogramContEncoder
   from .pipeline_spectrogram_diffusion import (
       SpectrogramContEncoder,
       SpectrogramDiffusionPipeline,
       TaFilmDecoder,
   )

try:
   if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   from ...utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
   from .midi_utils import MidiProcessor