



"""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


__snake_case							:   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)

__snake_case							:   Tuple      =    logging.getLogger()


def 			_lowercase				(  )						->   Optional[Any]:
			__lowerCAmelCase      :  int												=       argparse.ArgumentParser()
			parser.add_argument("-f"	)
			__lowerCAmelCase      :  Optional[Any]												=       parser.parse_args()
			return args.f


def 			_lowercase				(  __snake_case					,__snake_case="eval"	)						->   Optional[Any]:
			__lowerCAmelCase      :  Optional[Any]												=       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}"""	)


__snake_case							:   int      =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)





class 			A__   (  lowerCamelCase_							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  Tuple												=       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(_lowercase       ,			"argv"       ,			_lowercase):
							run_flax_glue.main()
							__lowerCAmelCase      :  Tuple												=       get_results(_lowercase)
							self.assertGreaterEqual(result["eval_accuracy"]       ,			0.75)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  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(_lowercase       ,			"argv"       ,			_lowercase):
							run_clm_flax.main()
							__lowerCAmelCase      :  Optional[Any]												=       get_results(_lowercase)
							self.assertLess(result["eval_perplexity"]       ,			100)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[int])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  Optional[int]												=       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(_lowercase       ,			"argv"       ,			_lowercase):
							run_summarization_flax.main()
							__lowerCAmelCase      :  Union[str, Any]												=       get_results(_lowercase       ,			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:		Optional[Any])				->       Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase      :  Union[str, Any]												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  Union[str, Any]												=       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(_lowercase       ,			"argv"       ,			_lowercase):
							run_mlm_flax.main()
							__lowerCAmelCase      :  Optional[int]												=       get_results(_lowercase)
							self.assertLess(result["eval_perplexity"]       ,			42)

	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       Dict:

				"""simple docstring"""

				__lowerCAmelCase      :  str												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  int												=       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(_lowercase       ,			"argv"       ,			_lowercase):
							run_ta_mlm_flax.main()
							__lowerCAmelCase      :  List[str]												=       get_results(_lowercase)
							self.assertGreaterEqual(result["eval_accuracy"]       ,			0.42)

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

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       7 if get_gpu_count() > 1 else 2

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







	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Dict)				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Tuple												=       self.get_auto_remove_tmp_dir()
				__lowerCAmelCase      :  Optional[Any]												=       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(_lowercase       ,			"argv"       ,			_lowercase):
							run_qa.main()
							__lowerCAmelCase      :  Optional[int]												=       get_results(_lowercase)
							self.assertGreaterEqual(result["eval_f1"]       ,			30)
							self.assertGreaterEqual(result["eval_exact"]       ,			30)
import doctest
from collections import deque

import numpy as np







class __snake_case				:




   def __init__(    self			:				Dict      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      [2, 1, 2, -1]
     SCREAMING_SNAKE_CASE__										=      [1, 2, 3, 4]




   def 	__a						(    self			:				int      ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__										=      len(self.first_signal      )
     SCREAMING_SNAKE_CASE__										=      len(self.second_signal      )

     SCREAMING_SNAKE_CASE__										=      max(_lowercase       ,	_lowercase      )

     # create a zero matrix of max_length x max_length
     SCREAMING_SNAKE_CASE__										=      [[0] * max_length for i in range(_lowercase      )]

     # fills the smaller signal with zeros to make both signals of same length
     if length_first_signal < length_second_signal:
       self.first_signal += [0] * (max_length - length_first_signal)
     elif length_first_signal > length_second_signal:
       self.second_signal += [0] * (max_length - length_second_signal)
     for i in range(_lowercase      ):
       SCREAMING_SNAKE_CASE__										=      deque(self.second_signal      )
       rotated_signal.rotate(_lowercase      )
       for j, item in enumerate(_lowercase      ):
         matrix[i][j] += item

        # multiply the matrix with the first signal
     SCREAMING_SNAKE_CASE__										=      np.matmul(np.transpose(_lowercase      )       ,	np.transpose(self.first_signal      )      )

     # rounding-off to two decimal places
     return [round(_lowercase       ,	2      ) for i in final_signal]


if __name__ == "__main__":
   doctest.testmod()
