






"""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__     (     ):



  '''simple docstring'''
  _a  :       Tuple             =	argparse.ArgumentParser()
  parser.add_argument("""-f"""				)
  _a  :       str             =	parser.parse_args()
  return args.f

def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__="eval"				):



  '''simple docstring'''
  _a  :       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}"""				)


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






class        UpperCamelCase			(	__snake_case      ):


 def _lowercase	(     self		:						List[str]							)					->       Optional[Any]:
   _a  :       List[Any]             =	self.get_auto_remove_tmp_dir()
   _a  :       int             =	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()
     _a  :       Any             =	get_results(UpperCAmelCase__							)
     self.assertGreaterEqual(result["""eval_accuracy"""]							,						0.7_5							)


 @slow
 def _lowercase	(     self		:						int							)					->       Union[str, Any]:
   _a  :       str             =	self.get_auto_remove_tmp_dir()
   _a  :       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(UpperCAmelCase__							,						"""argv"""							,						UpperCAmelCase__							):
     run_clm_flax.main()
     _a  :       Optional[Any]             =	get_results(UpperCAmelCase__							)
     self.assertLess(result["""eval_perplexity"""]							,						100							)


 @slow
 def _lowercase	(     self		:						Dict							)					->       Any:
   _a  :       int             =	self.get_auto_remove_tmp_dir()
   _a  :       Optional[Any]             =	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()
     _a  :       Any             =	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 _lowercase	(     self		:						str							)					->       Union[str, Any]:
   _a  :       int             =	self.get_auto_remove_tmp_dir()
   _a  :       Optional[int]             =	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()
     _a  :       str             =	get_results(UpperCAmelCase__							)
     self.assertLess(result["""eval_perplexity"""]							,						42							)


 @slow
 def _lowercase	(     self		:						Optional[int]							)					->       Optional[Any]:
   _a  :       Optional[Any]             =	self.get_auto_remove_tmp_dir()
   _a  :       List[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(UpperCAmelCase__							,						"""argv"""							,						UpperCAmelCase__							):
     run_ta_mlm_flax.main()
     _a  :       Optional[Any]             =	get_results(UpperCAmelCase__							)
     self.assertGreaterEqual(result["""eval_accuracy"""]							,						0.4_2							)


 @slow
 def _lowercase	(     self		:						str							)					->       str:
   _a  :       List[str]             =	7 if get_gpu_count() > 1 else 2

   _a  :       Optional[int]             =	self.get_auto_remove_tmp_dir()
   _a  :       Tuple             =	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()
     _a  :       str             =	get_results(UpperCAmelCase__							)
     self.assertGreaterEqual(result["""eval_accuracy"""]							,						0.7_5							)
     self.assertGreaterEqual(result["""eval_f1"""]							,						0.3							)







 @slow
 def _lowercase	(     self		:						int							)					->       Dict:
   _a  :       Any             =	self.get_auto_remove_tmp_dir()
   _a  :       List[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(UpperCAmelCase__							,						"""argv"""							,						UpperCAmelCase__							):
     run_qa.main()
     _a  :       List[Any]             =	get_results(UpperCAmelCase__							)
     self.assertGreaterEqual(result["""eval_f1"""]							,						30							)
     self.assertGreaterEqual(result["""eval_exact"""]							,						30							)



'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase			:				Union[str, Any]    		=			logging.get_logger(__name__)

lowercase			:				str    		=			{
    'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/config.json',
    'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/config.json',
    'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/config.json',
    'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/config.json',
    'bert-base-multilingual-uncased': 'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/config.json',
    'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/config.json',
    'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/config.json',
    'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/config.json',
    'bert-large-uncased-whole-word-masking': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking': (
        'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/config.json'
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/config.json'
    ),
    'bert-base-cased-finetuned-mrpc': 'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/config.json',
    'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/config.json',
    'bert-base-german-dbmdz-uncased': 'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese': 'https://huggingface.co/cl-tohoku/bert-base-japanese/resolve/main/config.json',
    'cl-tohoku/bert-base-japanese-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-whole-word-masking/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char/resolve/main/config.json'
    ),
    'cl-tohoku/bert-base-japanese-char-whole-word-masking': (
        'https://huggingface.co/cl-tohoku/bert-base-japanese-char-whole-word-masking/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-cased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/config.json'
    ),
    'TurkuNLP/bert-base-finnish-uncased-v1': (
        'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/config.json'
    ),
    'wietsedv/bert-base-dutch-cased': 'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/config.json',
    # See all BERT models at https://huggingface.co/models?filter=bert
}





class  A				(							__snake_case						):
     __magic_name__						=		'''bert'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=30522						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=512						,			SCREAMING_SNAKE_CASE=2						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=0						,			SCREAMING_SNAKE_CASE="absolute"						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=None						,			**SCREAMING_SNAKE_CASE						,			) ->				Optional[int]:






         """simple docstring"""






         super().__init__(pad_token_id=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE	)

         A       :   Optional[int]					=						vocab_size
         A       :   Optional[Any]					=						hidden_size
         A       :   List[Any]					=						num_hidden_layers
         A       :   List[str]					=						num_attention_heads
         A       :   Dict					=						hidden_act
         A       :   Optional[Any]					=						intermediate_size
         A       :   List[Any]					=						hidden_dropout_prob
         A       :   List[Any]					=						attention_probs_dropout_prob
         A       :   Optional[Any]					=						max_position_embeddings
         A       :   List[str]					=						type_vocab_size
         A       :   Dict					=						initializer_range
         A       :   str					=						layer_norm_eps
         A       :   int					=						position_embedding_type
         A       :   Dict					=						use_cache
         A       :   str					=						classifier_dropout





class  A				(							__snake_case						):





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         if self.task == "multiple-choice":
             A       :   Optional[Any]					=						{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
         else:
             A       :   Optional[int]					=						{0: '''batch''', 1: '''sequence'''}
         return OrderedDict(
             [
                 ('''input_ids''', dynamic_axis),
                 ('''attention_mask''', dynamic_axis),
                 ('''token_type_ids''', dynamic_axis),
             ]	)




