


"""simple docstring"""


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
A	:  str            =     logging.getLogger()


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     '''\n'''.join(snake_case__ )
			Path(snake_case__ ).open("w" ).writelines(snake_case__ )


A	:  Dict            =     """patrickvonplaten/t5-tiny-random"""
A	:  List[Any]            =     """sshleifer/bart-tiny-random"""
A	:  str            =     """sshleifer/tiny-mbart"""

A	:  Any            =     logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


class     _UpperCamelCase    (  lowerCamelCase__						):
							'''simple docstring'''







							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     Path(self.get_auto_remove_tmp_dir()      ) / '''utest_input.source'''
										__lowerCAmelCase							       =     input_file_name.parent / '''utest_output.txt'''
										assert not output_file_name.exists()
										__lowerCAmelCase							       =     [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
										_dump_articles(SCREAMING_SNAKE_CASE__       ,  SCREAMING_SNAKE_CASE__      )

										__lowerCAmelCase							       =     str(Path(self.get_auto_remove_tmp_dir()      ) / "scores.json"      )
										__lowerCAmelCase							       =     '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
										__lowerCAmelCase							       =     f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

										with patch.object(SCREAMING_SNAKE_CASE__       ,  "argv"       ,  SCREAMING_SNAKE_CASE__      ):
													run_generate()
													assert Path(SCREAMING_SNAKE_CASE__      ).exists()
													# os.remove(Path(output_file_name))

							def      snake_case							(	self      ):
										self.run_eval_tester(SCREAMING_SNAKE_CASE__      )

							@parameterized.expand([BART_TINY, MBART_TINY]      )
							@slow
							def      snake_case							(	self       ,  __a      ):
										self.run_eval_tester(SCREAMING_SNAKE_CASE__      )



							@parameterized.expand([T5_TINY, MBART_TINY]      )
							@slow
							def      snake_case							(	self       ,  __a      ):
										__lowerCAmelCase							       =     Path(self.get_auto_remove_tmp_dir()      ) / '''utest_input.source'''
										__lowerCAmelCase							       =     input_file_name.parent / '''utest_output.txt'''
										assert not output_file_name.exists()

										__lowerCAmelCase							       =     {
										    '''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
										    '''de''': [
										        '''Maschinelles Lernen ist großartig, oder?''',
										        '''Ich esse gerne Bananen''',
										        '''Morgen ist wieder ein toller Tag!''',
										    ],
										}

										__lowerCAmelCase							       =     Path(self.get_auto_remove_tmp_dir()      )
										__lowerCAmelCase							       =     str(tmp_dir / "scores.json"      )
										__lowerCAmelCase							       =     str(tmp_dir / "val.target"      )
										_dump_articles(SCREAMING_SNAKE_CASE__       ,  text["en"]      )
										_dump_articles(SCREAMING_SNAKE_CASE__       ,  text["de"]      )
										__lowerCAmelCase							       =     '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
										__lowerCAmelCase							       =     f"\n            run_eval_search.py\n            {model}\n            {str(SCREAMING_SNAKE_CASE__      )}\n            {str(SCREAMING_SNAKE_CASE__      )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
										testargs.extend(["--search", "num_beams=1:2 length_penalty=0.9:1.0"]      )

										with patch.object(SCREAMING_SNAKE_CASE__       ,  "argv"       ,  SCREAMING_SNAKE_CASE__      ):
													with CaptureStdout() as cs:
																run_search()
													__lowerCAmelCase							       =     [''' num_beams | length_penalty''', model, '''Best score args''']
													__lowerCAmelCase							       =     ['''Info''']
													if "translation" in task:
																expected_strings.append("bleu"      )
													else:
																expected_strings.extend(SCREAMING_SNAKE_CASE__      )
													for w in expected_strings:
																assert w in cs.out
													for w in un_expected_strings:
																assert w not in cs.out
													assert Path(SCREAMING_SNAKE_CASE__      ).exists()
													os.remove(Path(SCREAMING_SNAKE_CASE__      )      )








"""simple docstring"""






import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
					import jax.numpy as jnp

if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch






class      A(  unittest.TestCase		):

		"""simple docstring"""





		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	{
					    '''task_specific_params''': {
					        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_28, '''min_length''': 12, '''num_beams''': 4},
					        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_42, '''min_length''': 56, '''num_beams''': 4},
					        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
					    }
					}
					_UpperCamelCase					:Union[str, Any]					     =	{
					    '''task_specific_params.summarization.length_penalty''': 1.0,
					    '''task_specific_params.summarization.max_length''': 1_28,
					    '''task_specific_params.summarization.min_length''': 12,
					    '''task_specific_params.summarization.num_beams''': 4,
					    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
					    '''task_specific_params.summarization_cnn.max_length''': 1_42,
					    '''task_specific_params.summarization_cnn.min_length''': 56,
					    '''task_specific_params.summarization_cnn.num_beams''': 4,
					    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
					    '''task_specific_params.summarization_xsum.max_length''': 62,
					    '''task_specific_params.summarization_xsum.min_length''': 11,
					    '''task_specific_params.summarization_xsum.num_beams''': 6,
					}

					self.assertEqual(flatten_dict(SCREAMING_SNAKE_CASE__   )					,				SCREAMING_SNAKE_CASE__   )
		def   _UpperCamelCase( self   )      ->	Any:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	np.random.randn(3					,				4   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				x.transpose()   )   )

					_UpperCamelCase					:Tuple					     =	np.random.randn(3					,				4					,				5   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				x.transpose((1, 2, 0)   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Any					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				transpose(SCREAMING_SNAKE_CASE__   ).numpy()   )   )

					_UpperCamelCase					:int					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Any					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	Dict:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:List[str]					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				transpose(SCREAMING_SNAKE_CASE__   ).numpy()   )   )

					_UpperCamelCase					:int					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Dict					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   ).numpy()   )   )
		@require_flax
		def   _UpperCamelCase( self   )      ->	str:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Optional[Any]					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__   )					,				np.asarray(transpose(SCREAMING_SNAKE_CASE__   )   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Dict					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )					,				np.asarray(transpose(SCREAMING_SNAKE_CASE__					,				axes=(1, 2, 0)   )   )   )   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:int					     =	np.random.randn(3					,				4   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				np.reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4					,				5   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				np.reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Optional[Any]					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   ).numpy()   )   )

					_UpperCamelCase					:str					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:str					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Optional[int]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Dict					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   ).numpy()   )   )

					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Optional[int]					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   ).numpy()   )   )
		@require_flax
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:str					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )					,				np.asarray(reshape(SCREAMING_SNAKE_CASE__					,				(4, 3)   )   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4					,				5   )
					_UpperCamelCase					:Union[str, Any]					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )					,				np.asarray(reshape(SCREAMING_SNAKE_CASE__					,				(12, 5)   )   )   )   )
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	np.random.randn(1					,				3					,				4   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__   )					,				np.squeeze(SCREAMING_SNAKE_CASE__   )   )   )

					_UpperCamelCase					:List[Any]					     =	np.random.randn(1					,				4					,				1					,				5   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				np.squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	np.random.randn(1					,				3					,				4   )
					_UpperCamelCase					:Any					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__   )					,				squeeze(SCREAMING_SNAKE_CASE__   ).numpy()   )   )

					_UpperCamelCase					:List[str]					     =	np.random.randn(1					,				4					,				1					,				5   )
					_UpperCamelCase					:List[Any]					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	int:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	np.random.randn(1					,				3					,				4   )
					_UpperCamelCase					:str					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__   )					,				squeeze(SCREAMING_SNAKE_CASE__   ).numpy()   )   )

					_UpperCamelCase					:str					     =	np.random.randn(1					,				4					,				1					,				5   )
					_UpperCamelCase					:str					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   ).numpy()   )   )
		@require_flax
		def   _UpperCamelCase( self   )      ->	Optional[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Union[str, Any]					     =	np.random.randn(1					,				3					,				4   )
					_UpperCamelCase					:Optional[Any]					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__   )					,				np.asarray(squeeze(SCREAMING_SNAKE_CASE__   )   )   )   )

					_UpperCamelCase					:Dict					     =	np.random.randn(1					,				4					,				1					,				5   )
					_UpperCamelCase					:int					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )					,				np.asarray(squeeze(SCREAMING_SNAKE_CASE__					,				axis=2   )   )   )   )
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:Dict					     =	np.random.randn(3					,				4   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				np.expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )   )   )
		@require_torch
		def   _UpperCamelCase( self   )      ->	Tuple:

					"""simple docstring"""



					_UpperCamelCase					:List[str]					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:Union[str, Any]					     =	torch.tensor(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   ).numpy()   )   )
		@require_tf
		def   _UpperCamelCase( self   )      ->	Optional[int]:

					"""simple docstring"""



					_UpperCamelCase					:str					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:int					     =	tf.constant(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   ).numpy()   )   )





		@require_flax
		def   _UpperCamelCase( self   )      ->	List[Any]:

					"""simple docstring"""



					_UpperCamelCase					:Tuple					     =	np.random.randn(3					,				4   )
					_UpperCamelCase					:str					     =	jnp.array(SCREAMING_SNAKE_CASE__   )
					self.assertTrue(np.allclose(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )					,				np.asarray(expand_dims(SCREAMING_SNAKE_CASE__					,				axis=1   )   )   )   )

