

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      snake_case_				()      ->     int:
   __lowerCAmelCase  :      Any    =						argparse.ArgumentParser()
   parser.add_argument("""-f"""					)
   __lowerCAmelCase  :      int    =						parser.parse_args()
   return args.f





def      snake_case_				(__A					:						List[Any] ,       __A					:						Optional[int]="eval"					)      ->     int:
   __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}'''					)


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



class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  def 	SCREAMING_SNAKE_CASE   (      self			:   int   )							->     Optional[int]:






     """simple docstring"""







     __lowerCAmelCase  :      Dict    =						self.get_auto_remove_tmp_dir()
     __lowerCAmelCase  :      List[Any]    =						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(lowerCAmelCase	,							"""argv"""	,							lowerCAmelCase   ):
        run_flax_glue.main()
        __lowerCAmelCase  :      Optional[int]    =						get_results(lowerCAmelCase   )
        self.assertGreaterEqual(result["""eval_accuracy"""]	,							0.75   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Union[str, Any]   )							->     List[Any]:






     """simple docstring"""







     __lowerCAmelCase  :      List[Any]    =						self.get_auto_remove_tmp_dir()
     __lowerCAmelCase  :      int    =						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(lowerCAmelCase	,							"""argv"""	,							lowerCAmelCase   ):
        run_clm_flax.main()
        __lowerCAmelCase  :      int    =						get_results(lowerCAmelCase   )
        self.assertLess(result["""eval_perplexity"""]	,							1_00   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Tuple   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.get_auto_remove_tmp_dir()
     __lowerCAmelCase  :      Tuple    =						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(lowerCAmelCase	,							"""argv"""	,							lowerCAmelCase   ):
        run_summarization_flax.main()
        __lowerCAmelCase  :      Dict    =						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 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     Dict:






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						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(lowerCAmelCase	,							"""argv"""	,							lowerCAmelCase   ):
        run_mlm_flax.main()
        __lowerCAmelCase  :      str    =						get_results(lowerCAmelCase   )
        self.assertLess(result["""eval_perplexity"""]	,							42   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Any   )							->     List[str]:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						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(lowerCAmelCase	,							"""argv"""	,							lowerCAmelCase   ):
        run_ta_mlm_flax.main()
        __lowerCAmelCase  :      str    =						get_results(lowerCAmelCase   )
        self.assertGreaterEqual(result["""eval_accuracy"""]	,							0.42   )







  @slow
  def 	SCREAMING_SNAKE_CASE   (      self			:   Dict   )							->     str:






     """simple docstring"""







     __lowerCAmelCase  :      Any    =						7 if get_gpu_count() > 1 else 2

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







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






     """simple docstring"""







     __lowerCAmelCase  :      Tuple    =						self.get_auto_remove_tmp_dir()
     __lowerCAmelCase  :      Tuple    =						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(lowerCAmelCase	,							"""argv"""	,							lowerCAmelCase   ):
        run_qa.main()
        __lowerCAmelCase  :      Dict    =						get_results(lowerCAmelCase   )
        self.assertGreaterEqual(result["""eval_f1"""]	,							30   )
        self.assertGreaterEqual(result["""eval_exact"""]	,							30   )





import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate



@dataclass(frozen=a_			)
class SCREAMING_SNAKE_CASE     (  a_			):
  """simple docstring"""






  lowerCamelCase   :      str										=field(default="audio-classification"       ,							metadata={"include_in_asdict_even_if_is_default": True}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"audio": Audio()}			)
  lowerCamelCase   :      ClassVar[Features]										=Features({"labels": ClassLabel}			)
  lowerCamelCase   :      str										="audio"
  lowerCamelCase   :      str										="labels"







  def 	SCREAMING_SNAKE_CASE   (      self			:   List[Any]	,							lowerCAmelCase			:   Dict   )							->     Dict:






     """simple docstring"""







     if self.label_column not in features:
        raise ValueError(f'''Column {self.label_column} is not present in features.'''   )
     if not isinstance(features[self.label_column]	,							lowerCAmelCase   ):
        raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''   )
     __lowerCAmelCase  :      Tuple    =						copy.deepcopy(self   )
     __lowerCAmelCase  :      Tuple    =						self.label_schema.copy()
     __lowerCAmelCase  :      Union[str, Any]    =						features[self.label_column]
     __lowerCAmelCase  :      Any    =						label_schema
     return task_template







  @property
  def 	SCREAMING_SNAKE_CASE   (      self			:   str   )							->     Dict[str, str]:






     """simple docstring"""







     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }


