


import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
			from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase_				:			Optional[int]   =	get_tests_dir('fixtures/test_sentencepiece.model')

UpperCAmelCase_				:			Any   =	{'target_lang': 'fi', 'source_lang': 'en'}
UpperCAmelCase_				:			Tuple   =	'>>zh<<'
UpperCAmelCase_				:			Optional[int]   =	'Helsinki-NLP/'

if is_torch_available():
			UpperCAmelCase_				:			Optional[int]   =	'pt'
elif is_tf_available():
			UpperCAmelCase_				:			Dict   =	'tf'
else:
			UpperCAmelCase_				:			List[str]   =	'jax'
@require_sentencepiece
class 			SCREAMING_SNAKE_CASE__  (    lowercase__						,   unittest.TestCase      ):
						snake_case__    :					List[str]					       =						MarianTokenizer
						snake_case__    :					Tuple					       =						False
						snake_case__    :					Optional[Any]					       =						True
						def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Optional[Any]:
													super().setUp()
													a_ :		int							=  ['</s>', '<unk>', '▁This', '▁is', '▁a', '▁t', 'est', '\u0120', '<pad>']
													a_ :		Dict							=  dict(zip(SCREAMING_SNAKE_CASE__					,							range(len(SCREAMING_SNAKE_CASE__					)					)					)					)
													a_ :		List[str]							=  Path(self.tmpdirname					)
													save_json(SCREAMING_SNAKE_CASE__					,							save_dir / VOCAB_FILES_NAMES['vocab']					)
													save_json(SCREAMING_SNAKE_CASE__					,							save_dir / VOCAB_FILES_NAMES['tokenizer_config_file']					)
													if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
																				copyfile(SCREAMING_SNAKE_CASE__					,							save_dir / VOCAB_FILES_NAMES['source_spm']					)
																				copyfile(SCREAMING_SNAKE_CASE__					,							save_dir / VOCAB_FILES_NAMES['target_spm']					)

													a_ :		Any							=  MarianTokenizer.from_pretrained(self.tmpdirname					)
													tokenizer.save_pretrained(self.tmpdirname					)
						def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					,							**SCREAMING_SNAKE_CASE__    :	Optional[Any]					)		->      MarianTokenizer:
													return MarianTokenizer.from_pretrained(self.tmpdirname					,							**SCREAMING_SNAKE_CASE__					)
						def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	List[Any]					)		->      Any:
													return (
													    "This is a test",
													    "This is a test",
													)
						def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					)		->      Tuple:
													a_ :		int							=  '</s>'
													a_ :		int							=  0

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__					)					,							SCREAMING_SNAKE_CASE__					)
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__					)					,							SCREAMING_SNAKE_CASE__					)
						def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      str:
													a_ :		str							=  list(self.get_tokenizer().get_vocab().keys()					)

													self.assertEqual(vocab_keys[0]					,							'</s>'					)
													self.assertEqual(vocab_keys[1]					,							'<unk>'					)
													self.assertEqual(vocab_keys[-1]					,							'<pad>'					)
													self.assertEqual(len(SCREAMING_SNAKE_CASE__					)					,							9					)
						def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[Any]					)		->      Any:
													self.assertEqual(self.get_tokenizer().vocab_size					,							9					)
						def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Optional[int]:
													a_ :		str							=  MarianTokenizer.from_pretrained(F"""{ORG_NAME}opus-mt-en-de"""					)
													a_ :		Any							=  en_de_tokenizer(['I am a small frog']					,							return_tensors=SCREAMING_SNAKE_CASE__					)
													self.assertIsInstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													a_ :		int							=  [3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
													self.assertListEqual(SCREAMING_SNAKE_CASE__					,							batch.input_ids[0]					)

													a_ :		str							=  tempfile.mkdtemp()
													en_de_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE__					)
													a_ :		int							=  [x.name for x in Path(SCREAMING_SNAKE_CASE__					).glob('*'					)]
													self.assertIn('source.spm'					,							SCREAMING_SNAKE_CASE__					)
													MarianTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__					)
						def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      Tuple:
													a_ :		Optional[Any]							=  self.get_tokenizer()

													a_ :		str							=  tok(
													    ['I am a small frog' * 1_0_0_0, 'I am a small frog']					,							padding=SCREAMING_SNAKE_CASE__					,							truncation=SCREAMING_SNAKE_CASE__					,							return_tensors=SCREAMING_SNAKE_CASE__					)
													self.assertIsInstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													self.assertEqual(batch.input_ids.shape					,							(2, 5_1_2)					)
						def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      List[str]:
													a_ :		Union[str, Any]							=  self.get_tokenizer()
													a_ :		Dict							=  tok(['I am a tiny frog', 'I am a small frog']					,							padding=SCREAMING_SNAKE_CASE__					,							return_tensors=SCREAMING_SNAKE_CASE__					)
													self.assertIsInstance(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)
													self.assertEqual(batch_smaller.input_ids.shape					,							(2, 1_0)					)
						@slow
						def 	SCREAMING_SNAKE_CASE							(	self    :	Any					)		->      Union[str, Any]:
													# fmt: off
													a_ :		Union[str, Any]							=  {'input_ids': [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
													# fmt: on

													self.tokenizer_integration_test_util(
													    expected_encoding=SCREAMING_SNAKE_CASE__					,							model_name='Helsinki-NLP/opus-mt-en-de'					,							revision='1a8c2263da11e68e50938f97e10cd57820bd504c'					,							decode_kwargs={'use_source_tokenizer': True}					,							)

						def 	SCREAMING_SNAKE_CASE							(	self    :	Optional[int]					)		->      Dict:
													a_ :		Optional[Any]							=  MarianTokenizer.from_pretrained('hf-internal-testing/test-marian-two-vocabs'					)

													a_ :		str							=  'Tämä on testi'
													a_ :		List[Any]							=  'This is a test'

													a_ :		Optional[int]							=  [7_6, 7, 2_0_4_7, 2]
													a_ :		Dict							=  [6_9, 1_2, 1_1, 9_4_0, 2]

													a_ :		Optional[int]							=  tokenizer(SCREAMING_SNAKE_CASE__					).input_ids
													self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

													a_ :		int							=  tokenizer(text_target=SCREAMING_SNAKE_CASE__					).input_ids
													self.assertListEqual(SCREAMING_SNAKE_CASE__					,							SCREAMING_SNAKE_CASE__					)

													a_ :		int							=  tokenizer.decode(SCREAMING_SNAKE_CASE__					,							skip_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					)


