
'''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						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Any:


		'''simple docstring'''


		snake_case  :       List[Any]     			=						'''\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 	snake_case__       (      snake_case_					):







		"""simple docstring"""

		def       lowerCAmelCase				(      self				:			Union[str, Any]  ,	UpperCamelCase__				:			Dict      )						->			Tuple:






				"""simple docstring"""







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

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



		def       lowerCAmelCase				(      self				:			int      )						->			str:






				"""simple docstring"""







				self.run_eval_tester(UpperCamelCase__      )



		@parameterized.expand([BART_TINY, MBART_TINY]      )
		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[Any]      )						->			List[str]:






				"""simple docstring"""







				self.run_eval_tester(UpperCamelCase__      )



		@parameterized.expand([T5_TINY, MBART_TINY]      )
		@slow
		def       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			Any      )						->			str:






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						Path(self.get_auto_remove_tmp_dir()      ) / '''utest_input.source'''
				snake_case  :       List[Any]     			=						input_file_name.parent / '''utest_output.txt'''
				assert not output_file_name.exists()

				snake_case  :       Tuple     			=						{
				    '''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!''',
				    ],
				}

				snake_case  :       str     			=						Path(self.get_auto_remove_tmp_dir()      )
				snake_case  :       Any     			=						str(tmp_dir / '''scores.json'''      )
				snake_case  :       Union[str, Any]     			=						str(tmp_dir / '''val.target'''      )
				_dump_articles(UpperCamelCase__  ,	text['''en''']      )
				_dump_articles(UpperCamelCase__  ,	text['''de''']      )
				snake_case  :       Dict     			=						'''translation_en_to_de''' if model == T5_TINY else '''summarization'''
				snake_case  :       List[Any]     			=						f'\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase__      )}\n            {str(UpperCamelCase__      )}\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(UpperCamelCase__  ,	'''argv'''  ,	UpperCamelCase__      ):
						with CaptureStdout() as cs:
								run_search()
						snake_case  :       Tuple     			=						[''' num_beams | length_penalty''', model, '''Best score args''']
						snake_case  :       str     			=						['''Info''']
						if "translation" in task:
								expected_strings.append('''bleu'''      )
						else:
								expected_strings.extend(UpperCamelCase__      )
						for w in expected_strings:
								assert w in cs.out
						for w in un_expected_strings:
								assert w not in cs.out
						assert Path(UpperCamelCase__      ).exists()
						os.remove(Path(UpperCamelCase__      )      )










from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

								# set step values
								self.scheduler.set_timesteps(a   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

								snake_case_           =				(image / 2 + 0.5).clamp(0			,     1   )
								snake_case_           =				image.cpu().permute(0			,     2			,     3			,     1   ).numpy()
								if output_type == "pil":
															snake_case_           =				self.numpy_to_pil(a   )

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





