


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


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





@require_torch
class 					_a							(				unittest.TestCase):




	def 							UpperCAmelCase__(					self						:		List[str]		,					_SCREAMING_SNAKE_CASE						:		Dict		)->       Tuple:
			return FSMTTokenizer.from_pretrained(_a		)




	def 							UpperCAmelCase__(					self						:		int		,					_SCREAMING_SNAKE_CASE						:		List[str]		)->       Optional[int]:
			lowerCAmelCase__   :			List[str]         =					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 							UpperCAmelCase__(					self						:		int		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]		,					_SCREAMING_SNAKE_CASE						:		Union[str, Any]		)->       Optional[int]:
			lowerCAmelCase__   :			int         =					F'facebook/wmt19-{pair}'
			lowerCAmelCase__   :			Any         =					self.get_tokenizer(_a		)
			lowerCAmelCase__   :			Any         =					self.get_model(_a		)

			lowerCAmelCase__   :			Union[str, Any]         =					bleu_data[pair]['src']
			lowerCAmelCase__   :			Optional[int]         =					bleu_data[pair]['tgt']

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






'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				UNetaDModel
  __UpperCAmelCase				:				KarrasVeScheduler


  def __init__(		self      :     Union[str, Any]							,_a      :     UNetaDModel							,_a      :     KarrasVeScheduler       ):


     '''simple docstring'''






     super().__init__()
     self.register_modules(unet=_a							,scheduler=_a       )


  @torch.no_grad()
  def __call__(		self      :     List[Any]							,_a      :     int = 1							,_a      :     int = 50							,_a      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,_a      :     Optional[str] = "pil"							,_a      :     bool = True							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Any			=   self.unet.config.sample_size
     _a				:	Optional[int]			=   (batch_size, 3, img_size, img_size)

     _a				:	Dict			=   self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     _a				:	Dict			=   randn_tensor(_a							,generator=_a							,device=self.device       ) * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(_a       )

     for t in self.progress_bar(self.scheduler.timesteps       ):
        # here sigma_t == t_i from the paper
        _a				:	Optional[int]			=   self.scheduler.schedule[t]
        _a				:	List[str]			=   self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        _a,  _a				:	List[Any]			=   self.scheduler.add_noise_to_input(_a							,_a							,generator=_a       )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        _a				:	Optional[int]			=   (sigma_hat / 2) * model((sample_hat + 1) / 2							,sigma_hat / 2       ).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        _a				:	Tuple			=   self.scheduler.step(_a							,_a							,_a							,_a       )

        if sigma_prev != 0:
           # 6. Apply 2nd order correction
           # The model inputs and output are adjusted by following eq. (213) in [1].
           _a				:	Optional[int]			=   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,sigma_prev / 2       ).sample
           _a				:	Optional[Any]			=   self.scheduler.step_correct(
               _a							,_a							,_a							,_a							,step_output.prev_sample							,step_output['derivative']							,)
        _a				:	Dict			=   step_output.prev_sample

     _a				:	Tuple			=   (sample / 2 + 0.5).clamp(0							,1       )
     _a				:	Optional[Any]			=   sample.cpu().permute(0							,2							,3							,1       ).numpy()
     if output_type == "pil":
        _a				:	List[str]			=   self.numpy_to_pil(_a       )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_a       )



