



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


SCREAMING_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)

SCREAMING_SNAKE_CASE__       =      logging.getLogger()


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


def A						(       __UpperCamelCase ,	__UpperCamelCase="eval"					)						->      Optional[int]:
			A__       =    os.path.join(__UpperCamelCase ,	f'''{split}_results.json'''					)
			if os.path.exists(__UpperCamelCase					):
						with open(__UpperCamelCase ,	'r'					) as f:
									return json.load(__UpperCamelCase					)
			raise ValueError(f'''can\'t find {path}'''					)


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






class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__       =    self.get_auto_remove_tmp_dir()
								A__       =    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(_snake_case				,					'argv'				,					_snake_case					):
											run_flax_glue.main()
											A__       =    get_results(_snake_case					)
											self.assertGreaterEqual(result['eval_accuracy']				,					0.75					)



					@slow
					def     _a							(     self					:       List[Any]					):






								"""simple docstring"""


								A__       =    self.get_auto_remove_tmp_dir()
								A__       =    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(_snake_case				,					'argv'				,					_snake_case					):
											run_clm_flax.main()
											A__       =    get_results(_snake_case					)
											self.assertLess(result['eval_perplexity']				,					1_00					)



					@slow
					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.get_auto_remove_tmp_dir()
								A__       =    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(_snake_case				,					'argv'				,					_snake_case					):
											run_summarization_flax.main()
											A__       =    get_results(_snake_case				,					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     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_auto_remove_tmp_dir()
								A__       =    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(_snake_case				,					'argv'				,					_snake_case					):
											run_mlm_flax.main()
											A__       =    get_results(_snake_case					)
											self.assertLess(result['eval_perplexity']				,					42					)



					@slow
					def     _a							(     self					:       Dict					):






								"""simple docstring"""


								A__       =    self.get_auto_remove_tmp_dir()
								A__       =    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(_snake_case				,					'argv'				,					_snake_case					):
											run_ta_mlm_flax.main()
											A__       =    get_results(_snake_case					)
											self.assertGreaterEqual(result['eval_accuracy']				,					0.42					)



					@slow
					def     _a							(     self					:       Tuple					):






								"""simple docstring"""


								A__       =    7 if get_gpu_count() > 1 else 2

								A__       =    self.get_auto_remove_tmp_dir()
								A__       =    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(_snake_case				,					'argv'				,					_snake_case					):
											run_flax_ner.main()
											A__       =    get_results(_snake_case					)
											self.assertGreaterEqual(result['eval_accuracy']				,					0.75					)
											self.assertGreaterEqual(result['eval_f1']				,					0.3					)



					@slow
					def     _a							(     self					:       List[str]					):






								"""simple docstring"""


								A__       =    self.get_auto_remove_tmp_dir()
								A__       =    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(_snake_case				,					'argv'				,					_snake_case					):
											run_qa.main()
											A__       =    get_results(_snake_case					)
											self.assertGreaterEqual(result['eval_f1']				,					30					)
											self.assertGreaterEqual(result['eval_exact']				,					30					)









import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_rembert import RemBertTokenizer
else:
							SCREAMING_SNAKE_CASE__       =      None

SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__       =      {'''vocab_file''': '''sentencepiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__       =      {
    '''vocab_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/sentencepiece.model''',
    },
    '''tokenizer_file''': {
        '''google/rembert''': '''https://huggingface.co/google/rembert/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__       =      {
    '''google/rembert''': 2_5_6,
}

SCREAMING_SNAKE_CASE__       =      '''▁'''


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					A__				:		Any			       =    VOCAB_FILES_NAMES
					A__				:		str			       =    PRETRAINED_VOCAB_FILES_MAP
					A__				:		str			       =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A__				:		int			       =    RemBertTokenizer



					def __init__(     self					:       Union[str, Any]				,					_snake_case					:       Any=None				,					_snake_case					:       Optional[Any]=None				,					_snake_case					:       Any=True				,					_snake_case					:       Optional[int]=True				,					_snake_case					:       Dict=False				,					_snake_case					:       Dict="[CLS]"				,					_snake_case					:       List[Any]="[SEP]"				,					_snake_case					:       Union[str, Any]="<unk>"				,					_snake_case					:       List[str]="[SEP]"				,					_snake_case					:       List[str]="<pad>"				,					_snake_case					:       str="[CLS]"				,					_snake_case					:       Any="[MASK]"				,					**_snake_case					:       Any				,					):






								"""simple docstring"""


								A__       =    AddedToken(_snake_case				,					lstrip=_snake_case				,					rstrip=_snake_case					) if isinstance(_snake_case				,					_snake_case					) else mask_token

								super().__init__(
								    _snake_case				,					tokenizer_file=_snake_case				,					do_lower_case=_snake_case				,					remove_space=_snake_case				,					keep_accents=_snake_case				,					bos_token=_snake_case				,					eos_token=_snake_case				,					unk_token=_snake_case				,					sep_token=_snake_case				,					pad_token=_snake_case				,					cls_token=_snake_case				,					mask_token=_snake_case				,					**_snake_case				,					)

								A__       =    do_lower_case
								A__       =    remove_space
								A__       =    keep_accents
								A__       =    vocab_file
								A__       =    False if not self.vocab_file else True



					def     _a							(     self					:       Any				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]
								if token_ids_a is None:
											return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



					def     _a							(     self					:       Tuple				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None				,					_snake_case					:       bool = False					):






								"""simple docstring"""


								if already_has_special_tokens:
											if token_ids_a is not None:
														raise ValueError(
														    'You should not supply a second sequence if the provided sequence of '
														    'ids is already formatted with special tokens for the model.'					)
											return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

								if token_ids_a is not None:
											return [1] + ([0] * len(_snake_case					)) + [1] + ([0] * len(_snake_case					)) + [1]
								return [1] + ([0] * len(_snake_case					)) + [1]



					def     _a							(     self					:       Dict				,					_snake_case					:       List[int]				,					_snake_case					:       Optional[List[int]] = None					):






								"""simple docstring"""


								A__       =    [self.sep_token_id]
								A__       =    [self.cls_token_id]

								if token_ids_a is None:
											return len(cls + token_ids_a + sep					) * [0]
								return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]



					def     _a							(     self					:       Any				,					_snake_case					:       str				,					_snake_case					:       Optional[str] = None					):






								"""simple docstring"""


								if not os.path.isdir(_snake_case					):
											logger.error('Vocabulary path ({}) should be a directory'.format(_snake_case					)					)
											return
								A__       =    os.path.join(
								    _snake_case				,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)

								if os.path.abspath(self.vocab_file					) != os.path.abspath(_snake_case					):
											copyfile(self.vocab_file				,					_snake_case					)

								return (out_vocab_file,)




