





import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE_						(					snake_case_					):






						def __init__(      self      :      List[str]		,   _A      :      Union[List[ControlNetModel], Tuple[ControlNetModel]]	)							->    List[str]:







							"""simple docstring"""


							super().__init__()
							snake_case_		:			Tuple											=	nn.ModuleList(_A	)






						def UpperCAmelCase_				(      self      :      List[str]		,   _A      :      torch.FloatTensor		,   _A      :      Union[torch.Tensor, float, int]		,   _A      :      torch.Tensor		,   _A      :      List[torch.tensor]		,   _A      :      List[float]		,   _A      :      Optional[torch.Tensor] = None		,   _A      :      Optional[torch.Tensor] = None		,   _A      :      Optional[torch.Tensor] = None		,   _A      :      Optional[Dict[str, Any]] = None		,   _A      :      bool = False		,   _A      :      bool = True		,   )							->    Union[ControlNetOutput, Tuple]:







							"""simple docstring"""


							for i, (image, scale, controlnet) in enumerate(zip(_A		,   _A		,   self.nets	)	):
								snake_case_						,snake_case_		:			Optional[int]											=	controlnet(
								    _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   _A		,   )

								# merge samples
								if i == 0:
									snake_case_						,snake_case_		:			Dict											=	down_samples, mid_sample
								else:
									snake_case_		:			Tuple											=	[
									    samples_prev + samples_curr
									    for samples_prev, samples_curr in zip(_A		,   _A	)
									]
									mid_block_res_sample += mid_sample

							return down_block_res_samples, mid_block_res_sample






						def UpperCAmelCase_				(      self      :      str		,   _A      :      Union[str, os.PathLike]		,   _A      :      bool = True		,   _A      :      Callable = None		,   _A      :      bool = False		,   _A      :      Optional[str] = None		,   )							->    Optional[int]:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	0
							snake_case_		:			Optional[Any]											=	save_directory
							for controlnet in self.nets:
								controlnet.save_pretrained(
								    _A		,   is_main_process=_A		,   save_function=_A		,   safe_serialization=_A		,   variant=_A		,   )

								idx += 1
								snake_case_		:			Tuple											=	model_path_to_save + F"""_{idx}"""






						@classmethod
						def UpperCAmelCase_				(      cls      :      Tuple		,   _A      :      Optional[Union[str, os.PathLike]]		,   **_A      :      int	)							->    str:







							"""simple docstring"""


							snake_case_		:			Union[str, Any]											=	0
							snake_case_		:			Tuple											=	[]

							# load controlnet and append to list until no controlnet directory exists anymore
							# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
							# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
							snake_case_		:			int											=	pretrained_model_path
							while os.path.isdir(_A	):
								snake_case_		:			Optional[Any]											=	ControlNetModel.from_pretrained(_A		,   **_A	)
								controlnets.append(_A	)

								idx += 1
								snake_case_		:			Optional[int]											=	pretrained_model_path + F"""_{idx}"""

							logger.info(F"""{len(_A	)} controlnets loaded from {pretrained_model_path}."""	)

							if len(_A	) == 0:
								raise ValueError(
								    F"""No ControlNets found under {os.path.dirname(_A	)}. Expected at least {pretrained_model_path + '_0'}."""	)

							return cls(_A	)








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


_SCREAMING_SNAKE_CASE      	=		get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
						_SCREAMING_SNAKE_CASE      	=		json.load(f)
@require_torch
class SCREAMING_SNAKE_CASE_						(					unittest.TestCase					):






						def UpperCAmelCase_				(      self      :      Union[str, Any]		,   _A      :      Any	)							->    Optional[Any]:







							"""simple docstring"""


							return FSMTTokenizer.from_pretrained(_A	)






						def UpperCAmelCase_				(      self      :      Dict		,   _A      :      Union[str, Any]	)							->    Optional[Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	FSMTForConditionalGeneration.from_pretrained(_A	).to(_A	)
							if torch_device == "cuda":
								model.half()
							return model






						@parameterized.expand(
						    [
						        ['en-ru', 2_6.0],
						        ['ru-en', 2_2.0],
						        ['en-de', 2_2.0],
						        ['de-en', 2_9.0],
						    ]	)
						@slow
						def UpperCAmelCase_				(      self      :      int		,   _A      :      Optional[Any]		,   _A      :      Optional[Any]	)							->    Union[str, Any]:







							"""simple docstring"""


							snake_case_		:			Tuple											=	F"""facebook/wmt19-{pair}"""
							snake_case_		:			List[Any]											=	self.get_tokenizer(_A	)
							snake_case_		:			List[str]											=	self.get_model(_A	)

							snake_case_		:			Union[str, Any]											=	bleu_data[pair]['src']
							snake_case_		:			List[str]											=	bleu_data[pair]['tgt']

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

