


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__  :Optional[int]             =							[
    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__  :str             =							logging.getLogger()

def 	lowerCAmelCase__			(      )  ->		int:


							'''simple docstring'''





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

def 	lowerCAmelCase__			(      a__:		Any      ,  a__:		Optional[Any]="eval"	)  ->		List[str]:


							'''simple docstring'''





							_UpperCAmelCase					=      os.path.join(_lowerCAmelCase      ,  F'''{split}_results.json'''	)
							if os.path.exists(_lowerCAmelCase	):
														with open(_lowerCAmelCase      ,  'r'	) as f:
																					return json.load(_lowerCAmelCase	)
							raise ValueError(F'''can\'t find {path}'''	)


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







class 		__a				(    _a      ):
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_auto_remove_tmp_dir()
									_UpperCAmelCase					=      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(snake_case_      ,   'argv'      ,   snake_case_						):
																run_flax_glue.main()
																_UpperCAmelCase					=      get_results(snake_case_						)
																self.assertGreaterEqual(result['eval_accuracy']      ,   0.75						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_auto_remove_tmp_dir()
									_UpperCAmelCase					=      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(snake_case_      ,   'argv'      ,   snake_case_						):
																run_clm_flax.main()
																_UpperCAmelCase					=      get_results(snake_case_						)
																self.assertLess(result['eval_perplexity']      ,   100						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	List[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_auto_remove_tmp_dir()
									_UpperCAmelCase					=      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(snake_case_      ,   'argv'      ,   snake_case_						):
																run_summarization_flax.main()
																_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_auto_remove_tmp_dir()
									_UpperCAmelCase					=      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(snake_case_      ,   'argv'      ,   snake_case_						):
																run_mlm_flax.main()
																_UpperCAmelCase					=      get_results(snake_case_						)
																self.assertLess(result['eval_perplexity']      ,   42						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase					=      self.get_auto_remove_tmp_dir()
									_UpperCAmelCase					=      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(snake_case_      ,   'argv'      ,   snake_case_						):
																run_ta_mlm_flax.main()
																_UpperCAmelCase					=      get_results(snake_case_						)
																self.assertGreaterEqual(result['eval_accuracy']      ,   0.42						)
		@slow
		def      UpperCAmelCase__    (      self						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      7 if get_gpu_count() > 1 else 2

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






		@slow
		def      UpperCAmelCase__    (      self						)			->	Any:




									"""simple docstring"""





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




from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]      ,  a__:		NDArray[floataa]      ,  a__:		list[int]      ,  a__:		int      ,  )  ->		list[float]:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      coefficient_matrix.shape
							_UpperCAmelCase		,	_UpperCAmelCase					=      constant_matrix.shape

							if rowsa != colsa:
														_UpperCAmelCase					=      F'''Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if colsa != 1:
														_UpperCAmelCase					=      F'''Constant matrix must be nx1 but received {rowsa}x{colsa}'''
														raise ValueError(a__	)

							if rowsa != rowsa:
														_UpperCAmelCase					=      (
														    'Coefficient and constant matrices dimensions must be nxn and nx1 but '
														    F'''received {rowsa}x{colsa} and {rowsa}x{colsa}'''
														)
														raise ValueError(a__	)

							if len(a__	) != rowsa:
														_UpperCAmelCase					=      (
														    'Number of initial values must be equal to number of rows in coefficient '
														    F'''matrix but received {len(a__	)} and {rowsa}'''
														)
														raise ValueError(a__	)

							if iterations <= 0:
														raise ValueError('Iterations must be at least 1'	)

							_UpperCAmelCase					=      np.concatenate(
							    (coefficient_matrix, constant_matrix)      ,  axis=1	)

							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							strictly_diagonally_dominant(a__	)

							# Iterates the whole matrix for given number of times
							for _ in range(a__	):
														_UpperCAmelCase					=      []
														for row in range(a__	):
																					_UpperCAmelCase					=      0
																					for col in range(a__	):
																												if col == row:
																																			_UpperCAmelCase					=      table[row][col]
																												elif col == cols - 1:
																																			_UpperCAmelCase					=      table[row][col]
																												else:
																																			temp += (-1) * table[row][col] * init_val[col]
																					_UpperCAmelCase					=      (temp + val) / denom
																					new_val.append(a__	)
														_UpperCAmelCase					=      new_val

							return [float(a__	) for i in new_val]

def 	lowerCAmelCase__			(      a__:		NDArray[floataa]	)  ->		bool:


							'''simple docstring'''





							_UpperCAmelCase		,	_UpperCAmelCase					=      table.shape

							_UpperCAmelCase					=      True

							for i in range(0      ,  a__	):
														_UpperCAmelCase					=      0
														for j in range(0      ,  cols - 1	):
																					if i == j:
																												continue
																					else:
																												total += table[i][j]

														if table[i][i] <= total:
																					raise ValueError('Coefficient matrix is not strictly diagonally dominant'	)

							return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
		import doctest

		doctest.testmod()
