
"""simple docstring"""




import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np







class     lowercase:



     def 			lowercase__  (							self   )		->     List[str]:







         """simple docstring"""



         torch.manual_seed(0   )
         a__		=			TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         a__		=			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         a__		=			UNetaDConditionModel(
             sample_size=3_2     ,				layers_per_block=1     ,				block_out_channels=[3_2, 6_4]     ,				down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]     ,				mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,				up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,				in_channels=3     ,				out_channels=6     ,				cross_attention_dim=3_2     ,				encoder_hid_dim=3_2     ,				attention_head_dim=8     ,				addition_embed_type='text'     ,				addition_embed_type_num_heads=2     ,				cross_attention_norm='group_norm'     ,				resnet_time_scale_shift='scale_shift'     ,				act_fn='gelu'     ,				)
         unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         torch.manual_seed(0   )
         a__		=			DDPMScheduler(
             num_train_timesteps=1_0_0_0     ,				beta_schedule='squaredcos_cap_v2'     ,				beta_start=0.00_01     ,				beta_end=0.02     ,				thresholding=__SCREAMING_SNAKE_CASE     ,				dynamic_thresholding_ratio=0.95     ,				sample_max_value=1.0     ,				prediction_type='epsilon'     ,				variance_type='learned_range'     ,				)

         torch.manual_seed(0   )
         a__		=			IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         torch.manual_seed(0   )
         a__		=			TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         a__		=			AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5'   )

         torch.manual_seed(0   )
         a__		=			UNetaDConditionModel(
             sample_size=3_2     ,				layers_per_block=[1, 2]     ,				block_out_channels=[3_2, 6_4]     ,				down_block_types=[
                 'ResnetDownsampleBlock2D',
                 'SimpleCrossAttnDownBlock2D',
             ]     ,				mid_block_type='UNetMidBlock2DSimpleCrossAttn'     ,				up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D']     ,				in_channels=6     ,				out_channels=6     ,				cross_attention_dim=3_2     ,				encoder_hid_dim=3_2     ,				attention_head_dim=8     ,				addition_embed_type='text'     ,				addition_embed_type_num_heads=2     ,				cross_attention_norm='group_norm'     ,				resnet_time_scale_shift='scale_shift'     ,				act_fn='gelu'     ,				class_embed_type='timestep'     ,				mid_block_scale_factor=1.4_14     ,				time_embedding_act_fn='gelu'     ,				time_embedding_dim=3_2     ,				)
         unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         torch.manual_seed(0   )
         a__		=			DDPMScheduler(
             num_train_timesteps=1_0_0_0     ,				beta_schedule='squaredcos_cap_v2'     ,				beta_start=0.00_01     ,				beta_end=0.02     ,				thresholding=__SCREAMING_SNAKE_CASE     ,				dynamic_thresholding_ratio=0.95     ,				sample_max_value=1.0     ,				prediction_type='epsilon'     ,				variance_type='learned_range'     ,				)

         torch.manual_seed(0   )
         a__		=			DDPMScheduler(
             num_train_timesteps=1_0_0_0     ,				beta_schedule='squaredcos_cap_v2'     ,				beta_start=0.00_01     ,				beta_end=0.02     ,				)

         torch.manual_seed(0   )
         a__		=			IFWatermarker()

         return {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "image_noising_scheduler": image_noising_scheduler,
             "watermarker": watermarker,
             "safety_checker": None,
             "feature_extractor": None,
         }



     def 			lowercase__  (							self   )		->     Optional[Any]:







         """simple docstring"""



         a__		=			self.get_dummy_components()
         a__		=			self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
         pipe.to(__SCREAMING_SNAKE_CASE   )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )

         a__		=			inputs['prompt']
         a__		=			inputs['generator']
         a__		=			inputs['num_inference_steps']
         a__		=			inputs['output_type']

         if "image" in inputs:
             a__		=			inputs['image']
         else:
             a__		=			None

         if "mask_image" in inputs:
             a__		=			inputs['mask_image']
         else:
             a__		=			None

         if "original_image" in inputs:
             a__		=			inputs['original_image']
         else:
             a__		=			None

         a__	,     a__		=			pipe.encode_prompt(__SCREAMING_SNAKE_CASE   )

         # inputs with prompt converted to embeddings
         a__		=			{
             'prompt_embeds': prompt_embeds,
             'negative_prompt_embeds': negative_prompt_embeds,
             'generator': generator,
             'num_inference_steps': num_inference_steps,
             'output_type': output_type,
         }

         if image is not None:
             a__		=			image

         if mask_image is not None:
             a__		=			mask_image

         if original_image is not None:
             a__		=			original_image

         # set all optional components to None
         for optional_component in pipe._optional_components:
             setattr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )

         a__		=			pipe(**__SCREAMING_SNAKE_CASE   )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(__SCREAMING_SNAKE_CASE   )
             a__		=			self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE   )
             pipe_loaded.to(__SCREAMING_SNAKE_CASE   )
             pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         for optional_component in pipe._optional_components:
             self.assertTrue(
                 getattr(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) is None     ,				f'`{optional_component}` did not stay set to None after loading.'     ,				)

         a__		=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )

         a__		=			inputs['generator']
         a__		=			inputs['num_inference_steps']
         a__		=			inputs['output_type']

         # inputs with prompt converted to embeddings
         a__		=			{
             'prompt_embeds': prompt_embeds,
             'negative_prompt_embeds': negative_prompt_embeds,
             'generator': generator,
             'num_inference_steps': num_inference_steps,
             'output_type': output_type,
         }

         if image is not None:
             a__		=			image

         if mask_image is not None:
             a__		=			mask_image

         if original_image is not None:
             a__		=			original_image

         a__		=			pipe_loaded(**__SCREAMING_SNAKE_CASE   )[0]

         a__		=			np.abs(to_np(__SCREAMING_SNAKE_CASE   ) - to_np(__SCREAMING_SNAKE_CASE   )   ).max()
         self.assertLess(__SCREAMING_SNAKE_CASE     ,				1e-4   )




     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         a__		=			self.get_dummy_components()
         a__		=			self.pipeline_class(**__SCREAMING_SNAKE_CASE   )
         pipe.to(__SCREAMING_SNAKE_CASE   )
         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         a__		=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )
         a__		=			pipe(**__SCREAMING_SNAKE_CASE   )[0]

         with tempfile.TemporaryDirectory() as tmpdir:
             pipe.save_pretrained(__SCREAMING_SNAKE_CASE   )
             a__		=			self.pipeline_class.from_pretrained(__SCREAMING_SNAKE_CASE   )
             pipe_loaded.to(__SCREAMING_SNAKE_CASE   )
             pipe_loaded.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE   )

         pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor()   )  # For reproducibility tests

         a__		=			self.get_dummy_inputs(__SCREAMING_SNAKE_CASE   )
         a__		=			pipe_loaded(**__SCREAMING_SNAKE_CASE   )[0]

         a__		=			np.abs(to_np(__SCREAMING_SNAKE_CASE   ) - to_np(__SCREAMING_SNAKE_CASE   )   ).max()
         self.assertLess(__SCREAMING_SNAKE_CASE     ,				1e-4   )




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







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         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 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


