




"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a_	   =    get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, 'r', encoding='utf-8') as f:
			a_	   =    json.load(f)

@require_torch
class    UpperCAmelCase_  (       unittest.TestCase						):
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    List[str]:
					return FSMTTokenizer.from_pretrained(_a    )
		def      _lowerCamelCase				(		self							,		UpperCamelCase_    )		->    Union[str, Any]:
					__lowercase       :   List[Any]        =    FSMTForConditionalGeneration.from_pretrained(_a    ).to(_a    )
					if torch_device == "cuda":
								model.half()
					return model
		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]    )
		@slow
		def      _lowerCamelCase				(		self							,		UpperCamelCase_							,		UpperCamelCase_    )		->    Dict:
					# note: this test is not testing the best performance since it only evals a small batch
					# but it should be enough to detect a regression in the output quality
					__lowercase       :   Optional[Any]        =    F"""facebook/wmt19-{pair}"""
					__lowercase       :   str        =    self.get_tokenizer(_a    )
					__lowercase       :   str        =    self.get_model(_a    )

					__lowercase       :   Optional[Any]        =    bleu_data[pair]["src"]
					__lowercase       :   List[Any]        =    bleu_data[pair]["tgt"]

					__lowercase       :   List[str]        =    tokenizer(_a							,		return_tensors='''pt'''							,		truncation=_a							,		padding='''longest'''    ).to(_a    )
					__lowercase       :   List[Any]        =    model.generate(
					    input_ids=batch.input_ids							,		num_beams=8							,		)
					__lowercase       :   Optional[int]        =    tokenizer.batch_decode(
					    _a							,		skip_special_tokens=_a							,		clean_up_tokenization_spaces=_a    )
					__lowercase       :   Union[str, Any]        =    calculate_bleu(_a							,		_a    )
					print(_a    )
					self.assertGreaterEqual(scores['''bleu''']							,		_a    )





"""simple docstring"""




import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       if conf_path is None:
              lowercase    :				List[Any]       		=							"./model_checkpoints/vqgan_only.yaml"
       lowercase    :				Tuple       		=							load_config(__snake_case     , display=__snake_case  )
       lowercase    :				List[Any]       		=							VQModel(**config.model.params  )
       if ckpt_path is None:
              lowercase    :				List[str]       		=							"./model_checkpoints/vqgan_only.pt"
       lowercase    :				Optional[int]       		=							torch.load(__snake_case     , map_location=__snake_case  )
       if ".ckpt" in ckpt_path:
              lowercase    :				str       		=							sd["state_dict"]
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       model.to(__snake_case  )
       del sd
       return model


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       if "target" not in config:
              raise KeyError("Expected key `target` to instantiate."  )
       return get_obj_from_str(config["target"]  )(**config.get("params"     , {}  )  )


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							instantiate_from_config(__snake_case  )
       if sd is not None:
              model.load_state_dict(__snake_case  )
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


