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


lowercase_  : Union[str, Any]								=					[
    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)

lowercase_  : Union[str, Any]								=					logging.getLogger()







def       A__   (						):
 SCREAMING_SNAKE_CASE__: int=   argparse.ArgumentParser()
 parser.add_argument('''-f'''   )
 SCREAMING_SNAKE_CASE__: str=   parser.parse_args()
 return args.f







def       A__   (						snake_case_			:		str							,					snake_case_			:		Optional[Any]="eval"   ):
 SCREAMING_SNAKE_CASE__: List[str]=   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}'   )


lowercase_  : Optional[int]								=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 		_lowerCamelCase		( UpperCamelCase_     ):
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: str=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Tuple=   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()
      SCREAMING_SNAKE_CASE__: List[str]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.75 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Dict=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Dict=   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()
      SCREAMING_SNAKE_CASE__: Optional[Any]=   get_results(lowerCAmelCase )
      self.assertLess(result['''eval_perplexity''']			,			100 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Tuple=   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()
      SCREAMING_SNAKE_CASE__: Optional[int]=   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     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: str=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   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()
      SCREAMING_SNAKE_CASE__: List[Any]=   get_results(lowerCAmelCase )
      self.assertLess(result['''eval_perplexity''']			,			42 )
    @slow
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Dict=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: 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()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.42 )
    @slow
    def     UpperCamelCase_					(	self )							->       List[str]:
     # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   7 if get_gpu_count() > 1 else 2

     SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: int=   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()
      SCREAMING_SNAKE_CASE__: List[Any]=   get_results(lowerCAmelCase )
      self.assertGreaterEqual(result['''eval_accuracy''']			,			0.75 )
      self.assertGreaterEqual(result['''eval_f1''']			,			0.3 )



    @slow
    def     UpperCamelCase_					(	self )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: Optional[int]=   self.get_auto_remove_tmp_dir()
     SCREAMING_SNAKE_CASE__: str=   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()
      SCREAMING_SNAKE_CASE__: Optional[int]=   get_results(lowerCAmelCase )
      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_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase				:  Tuple    		=      {
    'configuration_distilbert': [
        'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'DistilBertConfig',
        'DistilBertOnnxConfig',
    ],
    'tokenization_distilbert': ['DistilBertTokenizer'],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      ['DistilBertTokenizerFast']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  List[str]    		=      [
     'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'DistilBertForMaskedLM',
     'DistilBertForMultipleChoice',
     'DistilBertForQuestionAnswering',
     'DistilBertForSequenceClassification',
     'DistilBertForTokenClassification',
     'DistilBertModel',
     'DistilBertPreTrainedModel',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  Union[str, Any]    		=      [
     'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFDistilBertForMaskedLM',
     'TFDistilBertForMultipleChoice',
     'TFDistilBertForQuestionAnswering',
     'TFDistilBertForSequenceClassification',
     'TFDistilBertForTokenClassification',
     'TFDistilBertMainLayer',
     'TFDistilBertModel',
     'TFDistilBertPreTrainedModel',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase				:  str    		=      [
     'FlaxDistilBertForMaskedLM',
     'FlaxDistilBertForMultipleChoice',
     'FlaxDistilBertForQuestionAnswering',
     'FlaxDistilBertForSequenceClassification',
     'FlaxDistilBertForTokenClassification',
     'FlaxDistilBertModel',
     'FlaxDistilBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_distilbert import (
     DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
     DistilBertConfig,
     DistilBertOnnxConfig,
 )
 from .tokenization_distilbert import DistilBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_distilbert_fast import DistilBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_distilbert import (
      DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      DistilBertForMaskedLM,
      DistilBertForMultipleChoice,
      DistilBertForQuestionAnswering,
      DistilBertForSequenceClassification,
      DistilBertForTokenClassification,
      DistilBertModel,
      DistilBertPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_distilbert import (
      TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFDistilBertForMaskedLM,
      TFDistilBertForMultipleChoice,
      TFDistilBertForQuestionAnswering,
      TFDistilBertForSequenceClassification,
      TFDistilBertForTokenClassification,
      TFDistilBertMainLayer,
      TFDistilBertModel,
      TFDistilBertPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_distilbert import (
      FlaxDistilBertForMaskedLM,
      FlaxDistilBertForMultipleChoice,
      FlaxDistilBertForQuestionAnswering,
      FlaxDistilBertForSequenceClassification,
      FlaxDistilBertForTokenClassification,
      FlaxDistilBertModel,
      FlaxDistilBertPreTrainedModel,
  )

else:
 import sys

 lowerCamelCase				:  int    		=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




