





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


A    :					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)

A    :					str =      logging.getLogger()







def    __lowerCamelCase					(							)		->				Dict:






		"""simple docstring"""

		A__        =   argparse.ArgumentParser()
		parser.add_argument("""-f"""    )
		A__        =   parser.parse_args()
		return args.f







def    __lowerCamelCase					(							__a   :Union[str, Any]    ,     __a   :str="eval"    )		->				Any:






		"""simple docstring"""

		A__        =   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}'    )


A    :					Optional[int] =      logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			def    a_       (			self   :    int   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.get_auto_remove_tmp_dir()
					A__        =   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()
							A__        =   get_results(__lowerCAmelCase   )
							self.assertGreaterEqual(result["""eval_accuracy"""]							,	0.7_5   )






			@slow
			def    a_       (			self   :    Union[str, Any]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.get_auto_remove_tmp_dir()
					A__        =   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()
							A__        =   get_results(__lowerCAmelCase   )
							self.assertLess(result["""eval_perplexity"""]							,	1_00   )






			@slow
			def    a_       (			self   :    Dict   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_auto_remove_tmp_dir()
					A__        =   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()
							A__        =   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    a_       (			self   :    List[str]   )	->	Union[str, Any]:







					"""simple docstring"""






					A__        =   self.get_auto_remove_tmp_dir()
					A__        =   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()
							A__        =   get_results(__lowerCAmelCase   )
							self.assertLess(result["""eval_perplexity"""]							,	42   )






			@slow
			def    a_       (			self   :    Tuple   )	->	Dict:







					"""simple docstring"""






					A__        =   self.get_auto_remove_tmp_dir()
					A__        =   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()
							A__        =   get_results(__lowerCAmelCase   )
							self.assertGreaterEqual(result["""eval_accuracy"""]							,	0.4_2   )






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







					"""simple docstring"""






					A__        =   7 if get_gpu_count() > 1 else 2

					A__        =   self.get_auto_remove_tmp_dir()
					A__        =   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()
							A__        =   get_results(__lowerCAmelCase   )
							self.assertGreaterEqual(result["""eval_accuracy"""]							,	0.7_5   )
							self.assertGreaterEqual(result["""eval_f1"""]							,	0.3   )







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







					"""simple docstring"""






					A__        =   self.get_auto_remove_tmp_dir()
					A__        =   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()
							A__        =   get_results(__lowerCAmelCase   )
							self.assertGreaterEqual(result["""eval_f1"""]							,	30   )
							self.assertGreaterEqual(result["""eval_exact"""]							,	30   )










from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A    :					Union[str, Any] =      logging.get_logger(__name__)

A    :					int =      {
    '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
    '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
    '''xlm-roberta-large-finetuned-conll02-dutch''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll02-spanish''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-english''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
    ),
    '''xlm-roberta-large-finetuned-conll03-german''': (
        '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
    ),
}


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			__lowerCamelCase	:       Any							=    '''xlm-roberta'''






			def __init__(			self   :    Optional[Any]							,	__lowerCAmelCase   :    List[Any]=3_05_22							,	__lowerCAmelCase   :    int=7_68							,	__lowerCAmelCase   :    Tuple=12							,	__lowerCAmelCase   :    str=12							,	__lowerCAmelCase   :    Union[str, Any]=30_72							,	__lowerCAmelCase   :    Union[str, Any]="gelu"							,	__lowerCAmelCase   :    Optional[int]=0.1							,	__lowerCAmelCase   :    Dict=0.1							,	__lowerCAmelCase   :    List[str]=5_12							,	__lowerCAmelCase   :    Tuple=2							,	__lowerCAmelCase   :    Dict=0.0_2							,	__lowerCAmelCase   :    List[str]=1e-12							,	__lowerCAmelCase   :    Union[str, Any]=1							,	__lowerCAmelCase   :    str=0							,	__lowerCAmelCase   :    Optional[int]=2							,	__lowerCAmelCase   :    Tuple="absolute"							,	__lowerCAmelCase   :    Any=True							,	__lowerCAmelCase   :    Any=None							,	**__lowerCAmelCase   :    str							,	)	->	Optional[Any]:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,	bos_token_id=__lowerCAmelCase							,	eos_token_id=__lowerCAmelCase							,	**__lowerCAmelCase   )

					A__        =   vocab_size
					A__        =   hidden_size
					A__        =   num_hidden_layers
					A__        =   num_attention_heads
					A__        =   hidden_act
					A__        =   intermediate_size
					A__        =   hidden_dropout_prob
					A__        =   attention_probs_dropout_prob
					A__        =   max_position_embeddings
					A__        =   type_vocab_size
					A__        =   initializer_range
					A__        =   layer_norm_eps
					A__        =   position_embedding_type
					A__        =   use_cache
					A__        =   classifier_dropout


class   A			(SCREAMING_SNAKE_CASE    ):
			'''simple docstring'''




			@property
			def    a_       (			self   :    int   )	->	Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__        =   {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__        =   {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]   )



