


import json
import os
import unittest

from transformers.models.roc_bert.tokenization_roc_bert import (
    VOCAB_FILES_NAMES,
    RoCBertBasicTokenizer,
    RoCBertTokenizer,
    RoCBertWordpieceTokenizer,
    _is_control,
    _is_punctuation,
    _is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   unittest.TestCase      ):
      snake_case__    :					str					       =						RoCBertTokenizer
      snake_case__    :					Union[str, Any]					       =						None
      snake_case__    :					Union[str, Any]					       =						False
      snake_case__    :					Optional[Any]					       =						True
      snake_case__    :					Union[str, Any]					       =						filter_non_english
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      str:
             super().setUp()

             a_ :		Dict							=  ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', '你', '好', '是', '谁', 'a', 'b', 'c', 'd']
             a_ :		Any							=  {}
             a_ :		List[Any]							=  {}
             for i, value in enumerate(SCREAMING_SNAKE_CASE__					):
                    a_ :		int							=  i
                    a_ :		Tuple							=  i
             a_ :		List[str]							=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['vocab_file']					)
             a_ :		Tuple							=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['word_shape_file']					)
             a_ :		List[str]							=  os.path.join(self.tmpdirname					,							VOCAB_FILES_NAMES['word_pronunciation_file']					)
             with open(self.vocab_file					,							'w'					,							encoding='utf-8'					) as vocab_writer:
                    vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]					)					)
             with open(self.word_shape_file					,							'w'					,							encoding='utf-8'					) as word_shape_writer:
                    json.dump(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							ensure_ascii=SCREAMING_SNAKE_CASE__					)
             with open(self.word_pronunciation_file					,							'w'					,							encoding='utf-8'					) as word_pronunciation_writer:
                    json.dump(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							ensure_ascii=SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Optional[int]:
             a_ :		Dict							=  self.tokenizer_class(self.vocab_file					,							self.word_shape_file					,							self.word_pronunciation_file					)

             a_ :		Tuple							=  tokenizer.tokenize('你好[SEP]你是谁'					)
             self.assertListEqual(SCREAMING_SNAKE_CASE__					,							['你', '好', '[SEP]', '你', '是', '谁']					)
             self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__					)					,							[5, 6, 2, 5, 7, 8]					)
             self.assertListEqual(tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__					)					,							[5, 6, 2, 5, 7, 8]					)
             self.assertListEqual(tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__					)					,							[5, 6, 2, 5, 7, 8]					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      str:
             a_ :		Tuple							=  RoCBertBasicTokenizer()

             self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz'					)					,							['ah', '\u535A', '\u63A8', 'zz']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Any:
             a_ :		List[Any]							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)					,							['hello', '!', 'how', 'are', 'you', '?']					)
             self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)					,							['hello']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Optional[int]:
             a_ :		Optional[Any]							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					,							strip_accents=SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)					,							['hällo', '!', 'how', 'are', 'you', '?']					)
             self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)					,							['h\u00E9llo']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Optional[int]							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					,							strip_accents=SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)					,							['hallo', '!', 'how', 'are', 'you', '?']					)
             self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)					,							['hello']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Tuple:
             a_ :		int							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)					,							['hallo', '!', 'how', 'are', 'you', '?']					)
             self.assertListEqual(tokenizer.tokenize('H\u00E9llo'					)					,							['hello']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Any:
             a_ :		Dict							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHeLLo!how  \n Are yoU?  '					)					,							['HeLLo', '!', 'how', 'Are', 'yoU', '?']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Optional[Any]:
             a_ :		Optional[Any]							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					,							strip_accents=SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)					,							['HäLLo', '!', 'how', 'Are', 'yoU', '?']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Optional[int]:
             a_ :		str							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					,							strip_accents=SCREAMING_SNAKE_CASE__					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHäLLo!how  \n Are yoU?  '					)					,							['HaLLo', '!', 'how', 'Are', 'yoU', '?']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      int:
             a_ :		Tuple							=  RoCBertBasicTokenizer(do_lower_case=SCREAMING_SNAKE_CASE__					,							never_split=['[UNK]']					)

             self.assertListEqual(
                 tokenizer.tokenize(' \tHeLLo!how  \n Are yoU? [UNK]'					)					,							['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      int:
             a_ :		int							=  ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']

             a_ :		Any							=  {}
             for i, token in enumerate(SCREAMING_SNAKE_CASE__					):
                    a_ :		List[str]							=  i
             a_ :		List[Any]							=  RoCBertWordpieceTokenizer(vocab=SCREAMING_SNAKE_CASE__					,							unk_token='[UNK]'					)

             self.assertListEqual(tokenizer.tokenize(''					)					,							[]					)

             self.assertListEqual(tokenizer.tokenize('unwanted running'					)					,							['un', '##want', '##ed', 'runn', '##ing']					)

             self.assertListEqual(tokenizer.tokenize('unwantedX running'					)					,							['[UNK]', 'runn', '##ing']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Any:
             self.assertTrue(_is_whitespace(' '					)					)
             self.assertTrue(_is_whitespace('\t'					)					)
             self.assertTrue(_is_whitespace('\r'					)					)
             self.assertTrue(_is_whitespace('\n'					)					)
             self.assertTrue(_is_whitespace('\u00A0'					)					)

             self.assertFalse(_is_whitespace('A'					)					)
             self.assertFalse(_is_whitespace('-'					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[Any]:
             self.assertTrue(_is_control('\u0005'					)					)

             self.assertFalse(_is_control('A'					)					)
             self.assertFalse(_is_control(' '					)					)
             self.assertFalse(_is_control('\t'					)					)
             self.assertFalse(_is_control('\r'					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	int					)		->      Union[str, Any]:
             self.assertTrue(_is_punctuation('-'					)					)
             self.assertTrue(_is_punctuation('$'					)					)
             self.assertTrue(_is_punctuation('`'					)					)
             self.assertTrue(_is_punctuation('.'					)					)

             self.assertFalse(_is_punctuation('A'					)					)
             self.assertFalse(_is_punctuation(' '					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Any:
             a_ :		Any							=  self.get_tokenizer()

             # Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
             self.assertListEqual([tokenizer.tokenize(SCREAMING_SNAKE_CASE__					) for t in ['Test', '\xad', 'test']]					,							[['[UNK]'], [], ['[UNK]']]					)

             if self.test_rust_tokenizer:
                    a_ :		Optional[Any]							=  self.get_rust_tokenizer()
                    self.assertListEqual(
                        [rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__					) for t in ['Test', '\xad', 'test']]					,							[['[UNK]'], [], ['[UNK]']]					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                           a_ :		Dict							=  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

                           a_ :		Union[str, Any]							=  F"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
                           a_ :		Union[str, Any]							=  tokenizer_r.encode_plus(
                               SCREAMING_SNAKE_CASE__					,							return_attention_mask=SCREAMING_SNAKE_CASE__					,							return_token_type_ids=SCREAMING_SNAKE_CASE__					,							return_offsets_mapping=SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					,							)

                           a_ :		Dict							=  tokenizer_r.do_lower_case if hasattr(SCREAMING_SNAKE_CASE__					,							'do_lower_case'					) else False
                           a_ :		Optional[int]							=  (
                               [
                                   ((0, 0), tokenizer_r.cls_token),
                                   ((0, 1), 'A'),
                                   ((1, 2), ','),
                                   ((3, 5), 'na'),
                                   ((5, 6), '##ï'),
                                   ((6, 8), '##ve'),
                                   ((9, 1_5), tokenizer_r.mask_token),
                                   ((1_6, 2_1), 'Allen'),
                                   ((2_1, 2_3), '##NL'),
                                   ((2_3, 2_4), '##P'),
                                   ((2_5, 3_3), 'sentence'),
                                   ((3_3, 3_4), '.'),
                                   ((0, 0), tokenizer_r.sep_token),
                               ]
                               if not do_lower_case
                               else [
                                   ((0, 0), tokenizer_r.cls_token),
                                   ((0, 1), 'a'),
                                   ((1, 2), ','),
                                   ((3, 8), 'naive'),
                                   ((9, 1_5), tokenizer_r.mask_token),
                                   ((1_6, 2_1), 'allen'),
                                   ((2_1, 2_3), '##nl'),
                                   ((2_3, 2_4), '##p'),
                                   ((2_5, 3_3), 'sentence'),
                                   ((3_3, 3_4), '.'),
                                   ((0, 0), tokenizer_r.sep_token),
                               ]
                           )

                           self.assertEqual(
                               [e[1] for e in expected_results]					,							tokenizer_r.convert_ids_to_tokens(tokens['input_ids']					)					)
                           self.assertEqual([e[0] for e in expected_results]					,							tokens['offset_mapping']					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Any:
             a_ :		str							=  ['的', '人', '有']
             a_ :		List[str]							=  ''.join(SCREAMING_SNAKE_CASE__					)
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                    with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""					):
                           a_ :		Dict							=  True
                           a_ :		Union[str, Any]							=  self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
                           a_ :		str							=  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

                           a_ :		List[str]							=  tokenizer_p.encode(SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)
                           a_ :		int							=  tokenizer_r.encode(SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

                           a_ :		Optional[Any]							=  tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__					)
                           a_ :		Optional[int]							=  tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__					)

                           # it is expected that each Chinese character is not preceded by "##"
                           self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                           self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

                           a_ :		int							=  False
                           a_ :		Tuple							=  self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)
                           a_ :		int							=  self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__					,							**SCREAMING_SNAKE_CASE__					)

                           a_ :		str							=  tokenizer_r.encode(SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)
                           a_ :		List[Any]							=  tokenizer_p.encode(SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

                           a_ :		Union[str, Any]							=  tokenizer_r.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__					)
                           a_ :		List[str]							=  tokenizer_p.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__					)

                           # it is expected that only the first Chinese character is not preceded by "##".
                           a_ :		Dict							=  [
                               F"""##{token}""" if idx != 0 else token for idx, token in enumerate(SCREAMING_SNAKE_CASE__					)
                           ]
                           self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
                           self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Optional[Any]:
             a_ :		int							=  self.tokenizer_class(self.vocab_file					,							self.word_shape_file					,							self.word_pronunciation_file					)

             a_ :		Union[str, Any]							=  tokenizer.encode('你好'					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)
             a_ :		List[Any]							=  tokenizer.encode('你是谁'					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

             a_ :		int							=  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__					)
             a_ :		List[str]							=  tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

             assert encoded_sentence == [1] + text + [2]
             assert encoded_pair == [1] + text + [2] + text_a + [2]

      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      Dict:
             a_ :		Tuple							=  self.get_tokenizers(do_lower_case=SCREAMING_SNAKE_CASE__					)
             for tokenizer in tokenizers:
                    with self.subTest(F"""{tokenizer.__class__.__name__}"""					):
                           a_ :		Any							=  '你好，你是谁'
                           a_ :		int							=  tokenizer.tokenize(SCREAMING_SNAKE_CASE__					)
                           a_ :		Dict							=  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__					)
                           a_ :		List[Any]							=  tokenizer.convert_tokens_to_shape_ids(SCREAMING_SNAKE_CASE__					)
                           a_ :		Any							=  tokenizer.convert_tokens_to_pronunciation_ids(SCREAMING_SNAKE_CASE__					)
                           a_ :		Any							=  tokenizer.prepare_for_model(
                               SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

                           a_ :		int							=  tokenizer.encode_plus(SCREAMING_SNAKE_CASE__					,							add_special_tokens=SCREAMING_SNAKE_CASE__					)

                           self.assertEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)






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_				:			str   =	[
    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_				:			Optional[int]   =	logging.getLogger()







def 	SCREAMING_SNAKE_CASE_						(      )     ->		Any:

       """simple docstring"""

       a_ :		Optional[int]							=  argparse.ArgumentParser()
       parser.add_argument('-f'    )
       a_ :		Optional[Any]							=  parser.parse_args()
       return args.f







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Optional[int]  ,   __A			:      List[Any]="eval"    )     ->		Optional[int]:

       """simple docstring"""

       a_ :		List[Any]							=  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_				:			str   =	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class 			SCREAMING_SNAKE_CASE__  (    lowercase__      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Tuple:
             a_ :		Optional[Any]							=  self.get_auto_remove_tmp_dir()
             a_ :		List[str]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_glue.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		Union[str, Any]							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_clm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							1_0_0					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      int:
             a_ :		Tuple							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_summarization_flax.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					,							split='test'					)
                    self.assertGreaterEqual(result['test_rouge1']					,							1_0					)
                    self.assertGreaterEqual(result['test_rouge2']					,							2					)
                    self.assertGreaterEqual(result['test_rougeL']					,							7					)
                    self.assertGreaterEqual(result['test_rougeLsum']					,							7					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Optional[Any]:
             a_ :		int							=  self.get_auto_remove_tmp_dir()
             a_ :		Dict							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_mlm_flax.main()
                    a_ :		List[Any]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertLess(result['eval_perplexity']					,							4_2					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	Tuple					)		->      List[str]:
             a_ :		str							=  self.get_auto_remove_tmp_dir()
             a_ :		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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_ta_mlm_flax.main()
                    a_ :		Dict							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.42					)
      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      List[str]:
             # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
             a_ :		int							=  7 if get_gpu_count() > 1 else 2

             a_ :		Dict							=  self.get_auto_remove_tmp_dir()
             a_ :		Tuple							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_flax_ner.main()
                    a_ :		List[str]							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_accuracy']					,							0.75					)
                    self.assertGreaterEqual(result['eval_f1']					,							0.3					)

      @slow
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      int:
             a_ :		List[str]							=  self.get_auto_remove_tmp_dir()
             a_ :		int							=  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(SCREAMING_SNAKE_CASE__					,							'argv'					,							SCREAMING_SNAKE_CASE__					):
                    run_qa.main()
                    a_ :		str							=  get_results(SCREAMING_SNAKE_CASE__					)
                    self.assertGreaterEqual(result['eval_f1']					,							3_0					)
                    self.assertGreaterEqual(result['eval_exact']					,							3_0					)


