


'''simple docstring'''





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__  			=				[
    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__  			=				logging.getLogger()






def 						UpperCAmelCase__(				):






			"""simple docstring"""




			__A=	argparse.ArgumentParser()
			parser.add_argument('-f' )
			__A=	parser.parse_args()
			return args.f






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Optional[Any],_SCREAMING_SNAKE_CASE					: Optional[int]="eval" ):






			"""simple docstring"""




			__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}""" )


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






class 						a__					(					a_   ):





			'''simple docstring'''

			def 					lowerCAmelCase     (  self							:			Any	)      ->		Dict:
						__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(a_			,    'argv'			,    a_	):
									run_flax_glue.main()
									__A=	get_results(a_	)
									self.assertGreaterEqual(result['eval_accuracy']			,    0.75	)
			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[str]:
						__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(a_			,    'argv'			,    a_	):
									run_clm_flax.main()
									__A=	get_results(a_	)
									self.assertLess(result['eval_perplexity']			,    100	)
			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Optional[Any]:
						__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(a_			,    'argv'			,    a_	):
									run_summarization_flax.main()
									__A=	get_results(a_			,    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 					lowerCAmelCase     (  self							:			List[str]	)      ->		List[Any]:
						__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(a_			,    'argv'			,    a_	):
									run_mlm_flax.main()
									__A=	get_results(a_	)
									self.assertLess(result['eval_perplexity']			,    42	)
			@slow
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		List[str]:
						__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(a_			,    'argv'			,    a_	):
									run_ta_mlm_flax.main()
									__A=	get_results(a_	)
									self.assertGreaterEqual(result['eval_accuracy']			,    0.42	)
			@slow
			def 					lowerCAmelCase     (  self							:			List[str]	)      ->		Union[str, Any]:
						# with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
						__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(a_			,    'argv'			,    a_	):
									run_flax_ner.main()
									__A=	get_results(a_	)
									self.assertGreaterEqual(result['eval_accuracy']			,    0.75	)
									self.assertGreaterEqual(result['eval_f1']			,    0.3	)

			@slow
			def 					lowerCAmelCase     (  self							:			Optional[int]	)      ->		Optional[Any]:
						__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(a_			,    'argv'			,    a_	):
									run_qa.main()
									__A=	get_results(a_	)
									self.assertGreaterEqual(result['eval_f1']			,    30	)
									self.assertGreaterEqual(result['eval_exact']			,    30	)








'''simple docstring'''

import math

import tensorflow as tf
from packaging import version







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    str      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    int      							=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0					)				,							x.dtype					)					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(math.pi				,							x.dtype					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi					) * (x + coeff * tf.pow(__UpperCAmelCase				,							3					))					))

			return x * cdf







def a_     (    __UpperCAmelCase					)				->					Tuple:





			"""simple docstring"""







			snake_case:    Optional[int]      							=tf.convert_to_tensor(__UpperCAmelCase					)

			return x * tf.tanh(tf.math.softplus(__UpperCAmelCase					)					)







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			snake_case:    List[str]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Dict      							=tf.cast(0.04_4715				,							x.dtype					)
			snake_case:    List[str]      							=tf.cast(0.79_7884_5608				,							x.dtype					)

			return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)					))







def a_     (    __UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=tf.convert_to_tensor(__UpperCAmelCase					)
			snake_case:    Tuple      							=tf.cast(1.702				,							x.dtype					)
			return x * tf.math.sigmoid(coeff * x					)







def a_     (    __UpperCAmelCase					)				->					int:





			"""simple docstring"""







			return tf.clip_by_value(_gelu(__UpperCAmelCase					)				,							-10				,							10					)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase=-1					)				->					List[str]:





			"""simple docstring"""







			snake_case							,  snake_case:    int      							=tf.split(__UpperCAmelCase				,							2				,							axis=__UpperCAmelCase					)
			return a * tf.math.sigmoid(__UpperCAmelCase					)


if version.parse(tf.version.VERSION) >= version.parse('2.4'):







	def a_     (    __UpperCAmelCase					)				->					int:





				"""simple docstring"""







				return tf.keras.activations.gelu(__UpperCAmelCase				,							approximate=__UpperCAmelCase					)

	a							   =				tf.keras.activations.gelu







	a							   =				approximate_gelu_wrap
else:
	a							   =				_gelu
	a							   =				_gelu_new


a							   =				{
    'gelu': gelu,
    'gelu_10': gelu_aa,
    'gelu_fast': gelu_fast,
    'gelu_new': gelu_new,
    'glu': glu,
    'mish': mish,
    'quick_gelu': quick_gelu,
    'relu': tf.keras.activations.relu,
    'sigmoid': tf.keras.activations.sigmoid,
    'silu': tf.keras.activations.swish,
    'swish': tf.keras.activations.swish,
    'tanh': tf.keras.activations.tanh,
}







def a_     (    __UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if activation_string in ACTaFN:
						return ACTaFN[activation_string]
			else:
						raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()					)}'''					)




