



"""simple docstring"""





import flax.linen as nn
import jax
import jax.numpy as jnp




class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					42
							_UpperCAmelCase      				=					jnp.floataa






							def        lowerCamelCase_   (  self						)			->							Tuple:
														_UpperCAmelCase							 =		nn.Conv(
														    self.out_channels	,       kernel_size=(3, 3)	,       strides=(1, 1)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )






							def __call__(  self	,       snake_case						)			->							Optional[Any]:
														_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		hidden_states.shape
														_UpperCAmelCase							 =		jax.image.resize(
														    snake_case	,       shape=(batch, height * 2, width * 2, channels)	,       method='nearest'	,       )
														_UpperCAmelCase							 =		self.conv(snake_case						)
														return hidden_states







class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					42
							_UpperCAmelCase      				=					jnp.floataa






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
														_UpperCAmelCase							 =		nn.Conv(
														    self.out_channels	,       kernel_size=(3, 3)	,       strides=(2, 2)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )






							def __call__(  self	,       snake_case						)			->							Tuple:
														# pad = ((0, 0), (0, 1), (0, 1), (0, 0))  # pad height and width dim
														# hidden_states = jnp.pad(hidden_states, pad_width=pad)
														_UpperCAmelCase							 =		self.conv(snake_case						)
														return hidden_states







class 			lowercase__			(      nn.Module    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					42
							_UpperCAmelCase      				=					None
							_UpperCAmelCase      				=					0.0
							_UpperCAmelCase      				=					None
							_UpperCAmelCase      				=					jnp.floataa






							def        lowerCamelCase_   (  self						)			->							List[Any]:
														_UpperCAmelCase							 =		self.in_channels if self.out_channels is None else self.out_channels

														_UpperCAmelCase							 =		nn.GroupNorm(num_groups=32	,       epsilon=1E-5						)
														_UpperCAmelCase							 =		nn.Conv(
														    snake_case	,       kernel_size=(3, 3)	,       strides=(1, 1)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )

														_UpperCAmelCase							 =		nn.Dense(snake_case	,       dtype=self.dtype						)

														_UpperCAmelCase							 =		nn.GroupNorm(num_groups=32	,       epsilon=1E-5						)
														_UpperCAmelCase							 =		nn.Dropout(self.dropout_prob						)
														_UpperCAmelCase							 =		nn.Conv(
														    snake_case	,       kernel_size=(3, 3)	,       strides=(1, 1)	,       padding=((1, 1), (1, 1))	,       dtype=self.dtype	,       )

														_UpperCAmelCase							 =		self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

														_UpperCAmelCase							 =		None
														if use_nin_shortcut:
																					_UpperCAmelCase							 =		nn.Conv(
																					    snake_case	,       kernel_size=(1, 1)	,       strides=(1, 1)	,       padding='VALID'	,       dtype=self.dtype	,       )






							def __call__(  self	,       snake_case	,       snake_case	,       snake_case=True						)			->							Union[str, Any]:
														_UpperCAmelCase							 =		hidden_states
														_UpperCAmelCase							 =		self.norma(snake_case						)
														_UpperCAmelCase							 =		nn.swish(snake_case						)
														_UpperCAmelCase							 =		self.conva(snake_case						)

														_UpperCAmelCase							 =		self.time_emb_proj(nn.swish(snake_case						)						)
														_UpperCAmelCase							 =		jnp.expand_dims(jnp.expand_dims(snake_case	,       1						)	,       1						)
														_UpperCAmelCase							 =		hidden_states + temb

														_UpperCAmelCase							 =		self.norma(snake_case						)
														_UpperCAmelCase							 =		nn.swish(snake_case						)
														_UpperCAmelCase							 =		self.dropout(snake_case	,       snake_case						)
														_UpperCAmelCase							 =		self.conva(snake_case						)

														if self.conv_shortcut is not None:
																					_UpperCAmelCase							 =		self.conv_shortcut(snake_case						)

														return hidden_states + residual







"""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)
lowercase  							=					logging.getLogger()




def 	UpperCAmelCase     (		A	:						Path     ,				A	:						list       ):

		'''simple docstring'''


		_UpperCAmelCase							 =		'\n'.join(A       )
		Path(A       ).open('w'       ).writelines(A       )


lowercase  							=					'''patrickvonplaten/t5-tiny-random'''
lowercase  							=					'''sshleifer/bart-tiny-random'''
lowercase  							=					'''sshleifer/tiny-mbart'''

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






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







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

									_UpperCAmelCase							 =		str(Path(self.get_auto_remove_tmp_dir()						) / 'scores.json'						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		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(snake_case	,       'argv'	,       snake_case						):
											run_generate()
											assert Path(snake_case						).exists()
											# os.remove(Path(output_file_name))






							def        lowerCamelCase_   (  self						)			->							str:
									self.run_eval_tester(snake_case						)






							@parameterized.expand([BART_TINY, MBART_TINY]						)
							@slow
							def        lowerCamelCase_   (  self	,       snake_case						)			->							List[Any]:
									self.run_eval_tester(snake_case						)






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

									_UpperCAmelCase							 =		{
									    '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!',
									    ],
									}

									_UpperCAmelCase							 =		Path(self.get_auto_remove_tmp_dir()						)
									_UpperCAmelCase							 =		str(tmp_dir / 'scores.json'						)
									_UpperCAmelCase							 =		str(tmp_dir / 'val.target'						)
									_dump_articles(snake_case	,       text['en']						)
									_dump_articles(snake_case	,       text['de']						)
									_UpperCAmelCase							 =		'translation_en_to_de' if model == T5_TINY else 'summarization'
									_UpperCAmelCase							 =		f'\n            run_eval_search.py\n            {model}\n            {str(snake_case						)}\n            {str(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(snake_case	,       'argv'	,       snake_case						):
											with CaptureStdout() as cs:
													run_search()
											_UpperCAmelCase							 =		[' num_beams | length_penalty', model, 'Best score args']
											_UpperCAmelCase							 =		['Info']
											if "translation" in task:
													expected_strings.append('bleu'						)
											else:
													expected_strings.extend(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(snake_case						).exists()
											os.remove(Path(snake_case						)						)


