

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


lowerCAmelCase							:  List[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)

lowerCAmelCase							:  List[str]            =						logging.getLogger()







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	argparse.ArgumentParser()
						parser.add_argument("-f"   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	parser.parse_args()
						return args.f







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase="eval"   ):
						SCREAMING_SNAKE_CASE_:			List[str]  		=	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}"   )


lowerCAmelCase							:  Tuple            =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE_:			Dict  		=	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(lowerCAmelCase__       ,						"argv"       ,						lowerCAmelCase__):
															run_flax_glue.main()
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	get_results(lowerCAmelCase__)
															self.assertGreaterEqual(result["eval_accuracy"]       ,						0.75)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	self.get_auto_remove_tmp_dir()
									SCREAMING_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(lowerCAmelCase__       ,						"argv"       ,						lowerCAmelCase__):
															run_clm_flax.main()
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_results(lowerCAmelCase__)
															self.assertLess(result["eval_perplexity"]       ,						100)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE_:			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(lowerCAmelCase__       ,						"argv"       ,						lowerCAmelCase__):
															run_summarization_flax.main()
															SCREAMING_SNAKE_CASE_:			Tuple  		=	get_results(lowerCAmelCase__       ,						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 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			int  		=	self.get_auto_remove_tmp_dir()
									SCREAMING_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(lowerCAmelCase__       ,						"argv"       ,						lowerCAmelCase__):
															run_mlm_flax.main()
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	get_results(lowerCAmelCase__)
															self.assertLess(result["eval_perplexity"]       ,						42)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Tuple):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE_:			Dict  		=	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(lowerCAmelCase__       ,						"argv"       ,						lowerCAmelCase__):
															run_ta_mlm_flax.main()
															SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	get_results(lowerCAmelCase__)
															self.assertGreaterEqual(result["eval_accuracy"]       ,						0.42)



			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					str):
									# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
									SCREAMING_SNAKE_CASE_:			Dict  		=	7 if get_gpu_count() > 1 else 2

									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE_:			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(lowerCAmelCase__       ,						"argv"       ,						lowerCAmelCase__):
															run_flax_ner.main()
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	get_results(lowerCAmelCase__)
															self.assertGreaterEqual(result["eval_accuracy"]       ,						0.75)
															self.assertGreaterEqual(result["eval_f1"]       ,						0.3)




			@slow
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			str  		=	self.get_auto_remove_tmp_dir()
									SCREAMING_SNAKE_CASE_:			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(lowerCAmelCase__       ,						"argv"       ,						lowerCAmelCase__):
															run_qa.main()
															SCREAMING_SNAKE_CASE_:			Dict  		=	get_results(lowerCAmelCase__)
															self.assertGreaterEqual(result["eval_f1"]       ,						30)
															self.assertGreaterEqual(result["eval_exact"]       ,						30)









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowerCAmelCase							:  Any            =						logging.get_logger(__name__)

lowerCAmelCase							:  Dict            =						{"""vocab_file""": """vocab.txt"""}

lowerCAmelCase							:  List[str]            =						{
    """vocab_file""": {
        """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt""",
        """YituTech/conv-bert-medium-small""": (
            """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"""
        ),
        """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt""",
    }
}

lowerCAmelCase							:  List[Any]            =						{
    """YituTech/conv-bert-base""": 512,
    """YituTech/conv-bert-medium-small""": 512,
    """YituTech/conv-bert-small""": 512,
}


lowerCAmelCase							:  Tuple            =						{
    """YituTech/conv-bert-base""": {"""do_lower_case""": True},
    """YituTech/conv-bert-medium-small""": {"""do_lower_case""": True},
    """YituTech/conv-bert-small""": {"""do_lower_case""": True},
}



class   __lowercase    (			UpperCAmelCase_     ):




			"""simple docstring"""







			_UpperCAmelCase       :							str      							=					VOCAB_FILES_NAMES
			_UpperCAmelCase       :							List[str]      							=					PRETRAINED_VOCAB_FILES_MAP
			_UpperCAmelCase       :							List[Any]      							=					PRETRAINED_INIT_CONFIGURATION
			_UpperCAmelCase       :							Optional[Any]      							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			_UpperCAmelCase       :							Dict      							=					ConvBertTokenizer



			def __init__(			self  :					Optional[Any]       ,						lowerCAmelCase__  :					Optional[int]=None       ,						lowerCAmelCase__  :					List[str]=None       ,						lowerCAmelCase__  :					str=True       ,						lowerCAmelCase__  :					Any="[UNK]"       ,						lowerCAmelCase__  :					Optional[Any]="[SEP]"       ,						lowerCAmelCase__  :					Any="[PAD]"       ,						lowerCAmelCase__  :					Dict="[CLS]"       ,						lowerCAmelCase__  :					Dict="[MASK]"       ,						lowerCAmelCase__  :					Tuple=True       ,						lowerCAmelCase__  :					str=None       ,						**lowerCAmelCase__  :					Dict       ,						):
									super().__init__(
									    lowerCAmelCase__       ,						tokenizer_file=lowerCAmelCase__       ,						do_lower_case=lowerCAmelCase__       ,						unk_token=lowerCAmelCase__       ,						sep_token=lowerCAmelCase__       ,						pad_token=lowerCAmelCase__       ,						cls_token=lowerCAmelCase__       ,						mask_token=lowerCAmelCase__       ,						tokenize_chinese_chars=lowerCAmelCase__       ,						strip_accents=lowerCAmelCase__       ,						**lowerCAmelCase__       ,						)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	json.loads(self.backend_tokenizer.normalizer.__getstate__())
									if (
									    normalizer_state.get("lowercase"       ,						lowerCAmelCase__) != do_lower_case
									    or normalizer_state.get("strip_accents"       ,						lowerCAmelCase__) != strip_accents
									    or normalizer_state.get("handle_chinese_chars"       ,						lowerCAmelCase__) != tokenize_chinese_chars
									):
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	getattr(lowerCAmelCase__       ,						normalizer_state.pop("type"))
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	do_lower_case
															SCREAMING_SNAKE_CASE_:			List[str]  		=	strip_accents
															SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenize_chinese_chars
															SCREAMING_SNAKE_CASE_:			Optional[int]  		=	normalizer_class(**lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	do_lower_case



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					Optional[int]       ,						lowerCAmelCase__  :					Union[str, Any]=None):
									SCREAMING_SNAKE_CASE_:			List[str]  		=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

									if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

									return output



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]       ,						lowerCAmelCase__  :					List[int]       ,						lowerCAmelCase__  :					Optional[List[int]] = None):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[self.sep_token_id]
									SCREAMING_SNAKE_CASE_:			int  		=	[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 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					str       ,						lowerCAmelCase__  :					Optional[str] = None):
									SCREAMING_SNAKE_CASE_:			Any  		=	self._tokenizer.model.save(lowerCAmelCase__       ,						name=lowerCAmelCase__)
									return tuple(lowerCAmelCase__)






