


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)
snake_case_    :      List[str]				    =				logging.getLogger()







def     lowerCamelCase( a__						,a__):
				_SCREAMING_SNAKE_CASE      	="\n".join(lowercase__)
				Path(lowercase__).open('''w''').writelines(lowercase__)


snake_case_    :      List[str]				    =				'''patrickvonplaten/t5-tiny-random'''
snake_case_    :      int				    =				'''sshleifer/bart-tiny-random'''
snake_case_    :      Any				    =				'''sshleifer/tiny-mbart'''

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


class     A__				(		UpperCamelCase__	):




						def 				__UpperCamelCase					(							self       :   Optional[int]					,     _a       :   Optional[Any]	)				->  Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE      	=Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
										_SCREAMING_SNAKE_CASE      	=input_file_name.parent / "utest_output.txt"
										assert not output_file_name.exists()
										_SCREAMING_SNAKE_CASE      	=[" New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County."]
										_dump_articles(snake_case__					,     snake_case__	)

										_SCREAMING_SNAKE_CASE      	=str(Path(self.get_auto_remove_tmp_dir()	) / '''scores.json'''	)
										_SCREAMING_SNAKE_CASE      	="translation_en_to_de" if model == T5_TINY else "summarization"
										_SCREAMING_SNAKE_CASE      	=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 				__UpperCamelCase					(							self       :   int	)				->  Tuple:


										"""simple docstring"""





										self.run_eval_tester(snake_case__	)




						@parameterized.expand([BART_TINY, MBART_TINY]	)
						@slow
						def 				__UpperCamelCase					(							self       :   Dict					,     _a       :   Optional[int]	)				->  int:


										"""simple docstring"""





										self.run_eval_tester(snake_case__	)




						@parameterized.expand([T5_TINY, MBART_TINY]	)
						@slow
						def 				__UpperCamelCase					(							self       :   Union[str, Any]					,     _a       :   Optional[Any]	)				->  Optional[int]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE      	=Path(self.get_auto_remove_tmp_dir()	) / "utest_input.source"
										_SCREAMING_SNAKE_CASE      	=input_file_name.parent / "utest_output.txt"
										assert not output_file_name.exists()

										_SCREAMING_SNAKE_CASE      	={
										    "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!",
										    ],
										}

										_SCREAMING_SNAKE_CASE      	=Path(self.get_auto_remove_tmp_dir()	)
										_SCREAMING_SNAKE_CASE      	=str(tmp_dir / '''scores.json'''	)
										_SCREAMING_SNAKE_CASE      	=str(tmp_dir / '''val.target'''	)
										_dump_articles(snake_case__					,     text['''en''']	)
										_dump_articles(snake_case__					,     text['''de''']	)
										_SCREAMING_SNAKE_CASE      	="translation_en_to_de" if model == T5_TINY else "summarization"
										_SCREAMING_SNAKE_CASE      	=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()
														_SCREAMING_SNAKE_CASE      	=[" num_beams | length_penalty", model, "Best score args"]
														_SCREAMING_SNAKE_CASE      	=["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__	)	)

'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer






@dataclass
class 							UpperCAmelCase_							(    A				):

 '''simple docstring'''
 lowercase_					:    torch.FloatTensor






class 							UpperCAmelCase_							(    A					,				A				):

 '''simple docstring'''







 @register_to_config
 def __init__(					self		:  List[str]			,				snake_case__		:  int = 3			,				snake_case__		:  int = 3			,				snake_case__		:  Tuple[str] = ("DownEncoderBlock2D",)			,				snake_case__		:  Tuple[str] = ("UpDecoderBlock2D",)			,				snake_case__		:  Tuple[int] = (64,)			,				snake_case__		:  int = 1			,				snake_case__		:  str = "silu"			,				snake_case__		:  int = 3			,				snake_case__		:  int = 32			,				snake_case__		:  int = 2_56			,				snake_case__		:  int = 32			,				snake_case__		:  Optional[int] = None			,				snake_case__		:  float = 0.18215			,				snake_case__		:  str = "group"			,				):



   '''simple docstring'''




   super().__init__()

   # pass init params to Encoder
   UpperCAmelCase__      :					Tuple				       =							Encoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				down_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				double_z=snake_case__			,				)

   UpperCAmelCase__      :					str				       =							vq_embed_dim if vq_embed_dim is not None else latent_channels

   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )
   UpperCAmelCase__      :					int				       =							VectorQuantizer(snake_case__			,				snake_case__			,				beta=0.25			,				remap=snake_case__			,				sane_index_shape=snake_case__ )
   UpperCAmelCase__      :					Tuple				       =							nn.Convad(snake_case__			,				snake_case__			,				1 )

   # pass init params to Decoder
   UpperCAmelCase__      :					Union[str, Any]				       =							Decoder(
       in_channels=snake_case__			,				out_channels=snake_case__			,				up_block_types=snake_case__			,				block_out_channels=snake_case__			,				layers_per_block=snake_case__			,				act_fn=snake_case__			,				norm_num_groups=snake_case__			,				norm_type=snake_case__			,				)







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  Union[str, Any]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					List[Any]				       =							self.encoder(snake_case__ )
   UpperCAmelCase__      :					List[str]				       =							self.quant_conv(snake_case__ )

   if not return_dict:
     return (h,)

   return VQEncoderOutput(latents=snake_case__ )







 @apply_forward_hook
 def 							UpperCamelCase  (					self		:  List[str]			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = False			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   if not force_not_quantize:
     UpperCAmelCase__       ,			UpperCAmelCase__       ,			UpperCAmelCase__      :					Any				       =							self.quantize(snake_case__ )
   else:
     UpperCAmelCase__      :					Tuple				       =							h
   UpperCAmelCase__      :					int				       =							self.post_quant_conv(snake_case__ )
   UpperCAmelCase__      :					Optional[int]				       =							self.decoder(snake_case__			,				quant if self.config.norm_type == "spatial" else None )

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )







 def 							UpperCamelCase  (					self		:  Any			,				snake_case__		:  torch.FloatTensor			,				snake_case__		:  bool = True ):



   '''simple docstring'''




   UpperCAmelCase__      :					Dict				       =							sample
   UpperCAmelCase__      :					int				       =							self.encode(snake_case__ ).latents
   UpperCAmelCase__      :					Any				       =							self.decode(snake_case__ ).sample

   if not return_dict:
     return (dec,)

   return DecoderOutput(sample=snake_case__ )



