


"""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						(						)     ->						Tuple:


    """simple docstring"""





    snake_case   :				List[Any]				 =   argparse.ArgumentParser()
    parser.add_argument("-f"			)
    snake_case   :				Tuple				 =   parser.parse_args()
    return args.f







def   __lowerCAmelCase						(						lowercase  :  List[str]						,							lowercase  :  Any="eval"			)     ->						List[Any]:


    """simple docstring"""





    snake_case   :				List[Any]				 =   os.path.join(lowercase						,							F'{split}_results.json'			)
    if os.path.exists(lowercase			):
        with open(lowercase						,							"r"			) as f:
            return json.load(lowercase			)
    raise ValueError(F'can\'t find {path}'			)


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







class _lowerCAmelCase    (				snake_case_     ):







   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				List[Any]				 =   self.get_auto_remove_tmp_dir()
       snake_case   :				Optional[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()
           snake_case   :				Optional[Any]				 =   get_results(UpperCamelCase__						)
           self.assertGreaterEqual(result["eval_accuracy"]						,     0.75						)







   @slow
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       snake_case   :				Dict				 =   self.get_auto_remove_tmp_dir()
       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(UpperCamelCase__						,     "argv"						,     UpperCamelCase__						):
           run_clm_flax.main()
           snake_case   :				Optional[Any]				 =   get_results(UpperCamelCase__						)
           self.assertLess(result["eval_perplexity"]						,     100						)







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

       '''simple docstring'''




       snake_case   :				Any				 =   self.get_auto_remove_tmp_dir()
       snake_case   :				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()
           snake_case   :				int				 =   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 			lowerCamelCase		(     self						)     ->		Optional[Any]:

       '''simple docstring'''




       snake_case   :				Dict				 =   self.get_auto_remove_tmp_dir()
       snake_case   :				Tuple				 =   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   :				Dict				 =   get_results(UpperCamelCase__						)
           self.assertLess(result["eval_perplexity"]						,     42						)







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

       '''simple docstring'''




       snake_case   :				List[str]				 =   self.get_auto_remove_tmp_dir()
       snake_case   :				Union[str, 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()
           snake_case   :				Optional[int]				 =   get_results(UpperCamelCase__						)
           self.assertGreaterEqual(result["eval_accuracy"]						,     0.42						)







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

       '''simple docstring'''




       snake_case   :				Optional[int]				 =   7 if get_gpu_count() > 1 else 2

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







   @slow
   def 			lowerCamelCase		(     self						)     ->		Optional[int]:

       '''simple docstring'''




       snake_case   :				str				 =   self.get_auto_remove_tmp_dir()
       snake_case   :				int				 =   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   :				Any				 =   get_results(UpperCamelCase__						)
           self.assertGreaterEqual(result["eval_f1"]						,     30						)
           self.assertGreaterEqual(result["eval_exact"]						,     30						)









"""simple docstring"""



import json
import os
import unittest

from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class _lowerCAmelCase    (				snake_case_   ,							unittest.TestCase     ):
   __UpperCAmelCase			:	List[Any]							  =  MgpstrTokenizer
   __UpperCAmelCase			:	Optional[int]							  =  False
   __UpperCAmelCase			:	Optional[int]							  =  {}
   __UpperCAmelCase			:	List[str]							  =  False







   def 			lowerCamelCase		(     self						)     ->		int:

       '''simple docstring'''




       super().setUp()

       # fmt: off
       snake_case   :				List[str]				 =   ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
       # fmt: on
       snake_case   :				Union[str, Any]				 =   dict(zip(UpperCamelCase__						,     range(len(UpperCamelCase__						)						)						)						)

       snake_case   :				int				 =   os.path.join(self.tmpdirname						,     VOCAB_FILES_NAMES["vocab_file"]						)
       with open(self.vocab_file						,     "w"						,     encoding="utf-8"						) as fp:
           fp.write(json.dumps(UpperCamelCase__						) + "\n"						)







   def 			lowerCamelCase		(     self						,     **UpperCamelCase__						)     ->		List[str]:

       '''simple docstring'''




       return MgpstrTokenizer.from_pretrained(self.tmpdirname						,     **UpperCamelCase__						)







   def 			lowerCamelCase		(     self						,     UpperCamelCase__						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				int				 =   "tester"
       snake_case   :				List[str]				 =   "tester"
       return input_text, output_text







   @unittest.skip("MGP-STR always lower cases letters."						)
   def 			lowerCamelCase		(     self						)     ->		str:

       '''simple docstring'''




       pass







   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       snake_case   :				int				 =   self.get_tokenizers(do_lower_case=UpperCamelCase__						)
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'						):
               snake_case   :				List[Any]				 =   "[SPECIAL_TOKEN]"

               tokenizer.add_special_tokens({"cls_token": special_token}						)
               snake_case   :				str				 =   tokenizer.encode([special_token]						,     add_special_tokens=UpperCamelCase__						)
               self.assertEqual(len(UpperCamelCase__						)						,     1						)

               snake_case   :				List[Any]				 =   tokenizer.decode(UpperCamelCase__						,     skip_special_tokens=UpperCamelCase__						)
               self.assertTrue(special_token not in decoded						)







   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       snake_case   :				List[str]				 =   self.get_tokenizers()
       for tokenizer in tokenizers:
           with self.subTest(F'{tokenizer.__class__.__name__}'						):
               snake_case ,snake_case   :				Union[str, Any]				 =   self.get_input_output_texts(UpperCamelCase__						)

               snake_case   :				Dict				 =   tokenizer.tokenize(UpperCamelCase__						)
               snake_case   :				Tuple				 =   tokenizer.convert_tokens_to_ids(UpperCamelCase__						)
               snake_case   :				List[Any]				 =   tokenizer.encode(UpperCamelCase__						,     add_special_tokens=UpperCamelCase__						)
               self.assertListEqual(UpperCamelCase__						,     UpperCamelCase__						)

               snake_case   :				Dict				 =   tokenizer.convert_ids_to_tokens(UpperCamelCase__						)
               self.assertNotEqual(len(UpperCamelCase__						)						,     0						)
               snake_case   :				Tuple				 =   tokenizer.decode(UpperCamelCase__						)
               self.assertIsInstance(UpperCamelCase__						,     UpperCamelCase__						)

               self.assertEqual(text_a.replace(" "						,     ""						)						,     UpperCamelCase__						)







   @unittest.skip("MGP-STR tokenizer only handles one sequence."						)
   def 			lowerCamelCase		(     self						)     ->		List[Any]:

       '''simple docstring'''




       pass







   @unittest.skip("inputs cannot be pretokenized in MgpstrTokenizer"						)
   def 			lowerCamelCase		(     self						)     ->		Tuple:

       '''simple docstring'''




       pass





