



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 				lowerCAmelCase_		(  a__       ,    unittest.TestCase							):
       UpperCAmelCase__				:					int        =  KandinskyImgaImgPipeline
       UpperCAmelCase__				:					Union[str, Any]        =  ["prompt", "image_embeds", "negative_image_embeds", "image"]
       UpperCAmelCase__				:					str        =  [
           "prompt",
           "negative_prompt",
           "image_embeds",
           "negative_image_embeds",
           "image",
       ]
       UpperCAmelCase__				:					str        =  [
           "generator",
           "height",
           "width",
           "strength",
           "guidance_scale",
           "negative_prompt",
           "num_inference_steps",
           "return_dict",
           "guidance_scale",
           "num_images_per_prompt",
           "output_type",
           "return_dict",
       ]
       UpperCAmelCase__				:					List[Any]        =  False
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return 32
       @property
       def 					snake_case_       (			self       )     ->		Any:
          return 32
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.time_input_dim
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          return self.time_input_dim * 4
       @property
       def 					snake_case_       (			self       )     ->		Dict:
          return 100
       @property
       def 					snake_case_       (			self       )     ->		Optional[Any]:
          UpperCamelCase    :  Dict							=	XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'       )
          return tokenizer
       @property
       def 					snake_case_       (			self       )     ->		List[str]:
          torch.manual_seed(0       )
          UpperCamelCase    :  str							=	MCLIPConfig(
              numDims=self.cross_attention_dim,			transformerDimensions=self.text_embedder_hidden_size,			hidden_size=self.text_embedder_hidden_size,			intermediate_size=37,			num_attention_heads=4,			num_hidden_layers=5,			vocab_size=1005,			)

          UpperCamelCase    :  int							=	MultilingualCLIP(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	text_encoder.eval()

          return text_encoder
       @property
       def 					snake_case_       (			self       )     ->		List[Any]:
          torch.manual_seed(0       )

          UpperCamelCase    :  Optional[int]							=	{
              'in_channels': 4,
              # Out channels is double in channels because predicts mean and variance
              'out_channels': 8,
              'addition_embed_type': 'text_image',
              'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
              'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
              'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
              'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
              'layers_per_block': 1,
              'encoder_hid_dim': self.text_embedder_hidden_size,
              'encoder_hid_dim_type': 'text_image_proj',
              'cross_attention_dim': self.cross_attention_dim,
              'attention_head_dim': 4,
              'resnet_time_scale_shift': 'scale_shift',
              'class_embed_type': None,
          }

          UpperCamelCase    :  Optional[Any]							=	UNetaDConditionModel(**SCREAMING_SNAKE_CASE_       )
          return model
       @property
       def 					snake_case_       (			self       )     ->		List[str]:
          return {
              "block_out_channels": [32, 64],
              "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
              "in_channels": 3,
              "latent_channels": 4,
              "layers_per_block": 1,
              "norm_num_groups": 8,
              "norm_type": "spatial",
              "num_vq_embeddings": 12,
              "out_channels": 3,
              "up_block_types": [
                  "AttnUpDecoderBlock2D",
                  "UpDecoderBlock2D",
              ],
              "vq_embed_dim": 4,
          }
       @property
       def 					snake_case_       (			self       )     ->		Optional[int]:
          torch.manual_seed(0       )
          UpperCamelCase    :  str							=	VQModel(**self.dummy_movq_kwargs       )
          return model
       def 					snake_case_       (			self       )     ->		List[Any]:
          UpperCamelCase    :  Union[str, Any]							=	self.dummy_text_encoder
          UpperCamelCase    :  Tuple							=	self.dummy_tokenizer
          UpperCamelCase    :  Optional[int]							=	self.dummy_unet
          UpperCamelCase    :  Union[str, Any]							=	self.dummy_movq

          UpperCamelCase    :  Any							=	{
              'num_train_timesteps': 1000,
              'beta_schedule': 'linear',
              'beta_start': 0.0_00_85,
              'beta_end': 0.0_12,
              'clip_sample': False,
              'set_alpha_to_one': False,
              'steps_offset': 0,
              'prediction_type': 'epsilon',
              'thresholding': False,
          }

          UpperCamelCase    :  Union[str, Any]							=	DDIMScheduler(**SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  str							=	{
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'unet': unet,
              'scheduler': scheduler,
              'movq': movq,
          }

          return components
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=0       )     ->		Any:
          UpperCamelCase    :  Union[str, Any]							=	floats_tensor((1, self.cross_attention_dim),			rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  Optional[int]							=	floats_tensor((1, self.cross_attention_dim),			rng=random.Random(seed + 1       )       ).to(SCREAMING_SNAKE_CASE_       )
          # create init_image
          UpperCamelCase    :  List[Any]							=	floats_tensor((1, 3, 64, 64),			rng=random.Random(SCREAMING_SNAKE_CASE_       )       ).to(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[Any]							=	image.cpu().permute(0,			2,			3,			1       )[0]
          UpperCamelCase    :  List[Any]							=	Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_       )       ).convert('RGB'       ).resize((256, 256)       )

          if str(SCREAMING_SNAKE_CASE_       ).startswith('mps'       ):
             UpperCamelCase    :  Tuple							=	torch.manual_seed(SCREAMING_SNAKE_CASE_       )
          else:
             UpperCamelCase    :  Dict							=	torch.Generator(device=SCREAMING_SNAKE_CASE_       ).manual_seed(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	{
              'prompt': 'horse',
              'image': init_image,
              'image_embeds': image_embeds,
              'negative_image_embeds': negative_image_embeds,
              'generator': generator,
              'height': 64,
              'width': 64,
              'num_inference_steps': 10,
              'guidance_scale': 7.0,
              'strength': 0.2,
              'output_type': 'np',
          }
          return inputs






       def 					snake_case_       (			self       )     ->		Any:
          UpperCamelCase    :  Optional[Any]							=	'cpu'

          UpperCamelCase    :  Optional[Any]							=	self.get_dummy_components()

          UpperCamelCase    :  int							=	self.pipeline_class(**SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  List[str]							=	pipe.to(SCREAMING_SNAKE_CASE_       )

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Union[str, Any]							=	pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       )       )
          UpperCamelCase    :  Dict							=	output.images

          UpperCamelCase    :  Any							=	pipe(
              **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_       ),			return_dict=SCREAMING_SNAKE_CASE_,			)[0]

          UpperCamelCase    :  Any							=	image[0, -3:, -3:, -1]
          UpperCamelCase    :  int							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)

          UpperCamelCase    :  List[str]							=	np.array(
              [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]       )
          assert (
              np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
          ), F""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
          assert (
              np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2
          ), F""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""






@slow
@require_torch_gpu
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self       )     ->		Any:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def 					snake_case_       (			self       )     ->		int:
          UpperCamelCase    :  Optional[int]							=	load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/kandinsky/kandinsky_img2img_frog.npy'       )

          UpperCamelCase    :  Tuple							=	load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'       )
          UpperCamelCase    :  Union[str, Any]							=	'A red cartoon frog, 4k'

          UpperCamelCase    :  Union[str, Any]							=	KandinskyPriorPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1-prior',			torch_dtype=torch.floataa       )
          pipe_prior.to(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	KandinskyImgaImgPipeline.from_pretrained(
              'kandinsky-community/kandinsky-2-1',			torch_dtype=torch.floataa       )
          UpperCamelCase    :  Optional[int]							=	pipeline.to(SCREAMING_SNAKE_CASE_       )

          pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  List[str]							=	torch.Generator(device='cpu'       ).manual_seed(0       )
          UpperCamelCase	,     UpperCamelCase    :  Any							=	pipe_prior(
              SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=5,			negative_prompt='',			).to_tuple()

          UpperCamelCase    :  str							=	pipeline(
              SCREAMING_SNAKE_CASE_,			image=SCREAMING_SNAKE_CASE_,			image_embeds=SCREAMING_SNAKE_CASE_,			negative_image_embeds=SCREAMING_SNAKE_CASE_,			generator=SCREAMING_SNAKE_CASE_,			num_inference_steps=100,			height=768,			width=768,			strength=0.2,			output_type='np',			)

          UpperCamelCase    :  Optional[Any]							=	output.images[0]

          assert image.shape == (768, 768, 3)

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )








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


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



@require_torch
class 				lowerCAmelCase_		(  unittest.TestCase							):
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          return FSMTTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		List[str]:
          UpperCamelCase    :  List[Any]							=	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 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		Any:
          # 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
          UpperCamelCase    :  int							=	F"""facebook/wmt19-{pair}"""
          UpperCamelCase    :  Optional[int]							=	self.get_tokenizer(SCREAMING_SNAKE_CASE_       )
          UpperCamelCase    :  str							=	self.get_model(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Dict							=	bleu_data[pair]['src']
          UpperCamelCase    :  Tuple							=	bleu_data[pair]['tgt']

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



