






"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Optional[Any]    =      KandinskyPipeline
 UpperCamelCase      : List[str]    =      [
     '''prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
 ]
 UpperCamelCase      : Tuple    =      ['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
 UpperCamelCase      : int    =      [
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 UpperCamelCase      : str    =      False


 @property
 def _lowercase			(  self	:		int							)      -> Optional[int]:
   return 32


 @property
 def _lowercase			(  self	:		str							)      -> Any:
   return 32


 @property
 def _lowercase			(  self	:		List[str]							)      -> str:
   return self.time_input_dim


 @property
 def _lowercase			(  self	:		List[Any]							)      -> Optional[int]:
   return self.time_input_dim * 4


 @property
 def _lowercase			(  self	:		Optional[int]							)      -> Optional[int]:
   return 100


 @property
 def _lowercase			(  self	:		Optional[int]							)      -> Optional[int]:
   _a						: str    =			XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
   return tokenizer


 @property
 def _lowercase			(  self	:		List[Any]							)      -> Any:
   torch.manual_seed(0							)
   _a						: 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							,		)

   _a						: Optional[Any]    =			MultilingualCLIP(UpperCAmelCase__							)
   _a						: int    =			text_encoder.eval()

   return text_encoder


 @property
 def _lowercase			(  self	:		Dict							)      -> Optional[int]:
   torch.manual_seed(0							)

   _a						: List[Any]    =			{
       """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,
   }

   _a						: Dict    =			UNetaDConditionModel(**UpperCAmelCase__							)
   return model


 @property
 def _lowercase			(  self	:		str							)      -> Union[str, Any]:
   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 _lowercase			(  self	:		int							)      -> Tuple:
   torch.manual_seed(0							)
   _a						: List[Any]    =			VQModel(**self.dummy_movq_kwargs							)
   return model


 def _lowercase			(  self	:		Optional[Any]							)      -> Union[str, Any]:
   _a						: Optional[int]    =			self.dummy_text_encoder
   _a						: Optional[int]    =			self.dummy_tokenizer
   _a						: Optional[Any]    =			self.dummy_unet
   _a						: Dict    =			self.dummy_movq

   _a						: Any    =			DDIMScheduler(
       num_train_timesteps=1000							,		beta_schedule="""linear"""							,		beta_start=0.0_0_0_8_5							,		beta_end=0.0_1_2							,		clip_sample=UpperCAmelCase__							,		set_alpha_to_one=UpperCAmelCase__							,		steps_offset=1							,		prediction_type="""epsilon"""							,		thresholding=UpperCAmelCase__							,		)

   _a						: Optional[Any]    =			{
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }
   return components


 def _lowercase			(  self	:		Union[str, Any]							,		UpperCAmelCase__	:		Tuple							,		UpperCAmelCase__	:		Dict=0							)      -> Optional[int]:
   _a						: List[Any]    =			floats_tensor((1, self.cross_attention_dim)							,		rng=random.Random(UpperCAmelCase__							)							).to(UpperCAmelCase__							)
   _a						: int    =			floats_tensor((1, self.cross_attention_dim)							,		rng=random.Random(seed + 1							)							).to(UpperCAmelCase__							)
   if str(UpperCAmelCase__							).startswith("""mps"""							):
     _a						: Union[str, Any]    =			torch.manual_seed(UpperCAmelCase__							)
   else:
     _a						: List[Any]    =			torch.Generator(device=UpperCAmelCase__							).manual_seed(UpperCAmelCase__							)
   _a						: Tuple    =			{
       """prompt""": """horse""",
       """image_embeds""": image_embeds,
       """negative_image_embeds""": negative_image_embeds,
       """generator""": generator,
       """height""": 64,
       """width""": 64,
       """guidance_scale""": 4.0,
       """num_inference_steps""": 2,
       """output_type""": """np""",
   }
   return inputs







 def _lowercase			(  self	:		Union[str, Any]							)      -> Any:
   _a						: List[str]    =			"""cpu"""

   _a						: List[str]    =			self.get_dummy_components()

   _a						: str    =			self.pipeline_class(**UpperCAmelCase__							)
   _a						: Tuple    =			pipe.to(UpperCAmelCase__							)

   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a						: int    =			pipe(**self.get_dummy_inputs(UpperCAmelCase__							)							)
   _a						: Dict    =			output.images

   _a						: str    =			pipe(
       **self.get_dummy_inputs(UpperCAmelCase__							)							,		return_dict=UpperCAmelCase__							,		)[0]

   _a						: str    =			image[0, -3:, -3:, -1]
   _a						: str    =			image_from_tuple[0, -3:, -3:, -1]

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

   _a						: str    =			np.array(
       [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]							)

   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  UpperCamelCase    (			unittest.TestCase   ):


 def _lowercase			(  self	:		int							)      -> List[str]:
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def _lowercase			(  self	:		str							)      -> str:
   _a						: Tuple    =			load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinsky/kandinsky_text2img_cat_fp16.npy"""							)

   _a						: Any    =			KandinskyPriorPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1-prior"""							,		torch_dtype=torch.floataa							)
   pipe_prior.to(UpperCAmelCase__							)

   _a						: List[Any]    =			KandinskyPipeline.from_pretrained("""kandinsky-community/kandinsky-2-1"""							,		torch_dtype=torch.floataa							)
   _a						: List[Any]    =			pipeline.to(UpperCAmelCase__							)
   pipeline.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a						: str    =			"""red cat, 4k photo"""

   _a						: List[Any]    =			torch.Generator(device="""cuda"""							).manual_seed(0							)
   _a   ,						_a						: Optional[Any]    =			pipe_prior(
       UpperCAmelCase__							,		generator=UpperCAmelCase__							,		num_inference_steps=5							,		negative_prompt=""""""							,		).to_tuple()

   _a						: Tuple    =			torch.Generator(device="""cuda"""							).manual_seed(0							)
   _a						: Optional[int]    =			pipeline(
       UpperCAmelCase__							,		image_embeds=UpperCAmelCase__							,		negative_image_embeds=UpperCAmelCase__							,		generator=UpperCAmelCase__							,		num_inference_steps=100							,		output_type="""np"""							,		)

   _a						: Union[str, Any]    =			output.images[0]

   assert image.shape == (512, 512, 3)

   assert_mean_pixel_difference(UpperCAmelCase__							,		UpperCAmelCase__							)









"""simple docstring"""




import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase    (			snake_case_	,  unittest.TestCase   ):
 UpperCamelCase      : Union[str, Any]    =      BertweetTokenizer
 UpperCamelCase      : Optional[Any]    =      False


 def _lowercase			(  self	:		Dict							)      -> List[Any]:
   super().setUp()

   # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
   _a						: Tuple    =			["""I""", """m""", """V@@""", """R@@""", """r""", """e@@"""]
   _a						: str    =			dict(zip(UpperCAmelCase__							,		range(len(UpperCAmelCase__							)							)							)							)
   _a						: Dict    =			["""#version: 0.2""", """a m</w>"""]
   _a						: int    =			{"""unk_token""": """<unk>"""}

   _a						: Union[str, Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""vocab_file"""]							)
   _a						: List[Any]    =			os.path.join(self.tmpdirname							,		VOCAB_FILES_NAMES["""merges_file"""]							)
   with open(self.vocab_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     for token in vocab_tokens:
       fp.write(f"""{token} {vocab_tokens[token]}\n"""							)
   with open(self.merges_file							,		"""w"""							,		encoding="""utf-8"""							) as fp:
     fp.write("""\n""".join(UpperCAmelCase__							)							)


 def _lowercase			(  self	:		Any							,		**UpperCAmelCase__	:		Optional[Any]							)      -> Dict:
   kwargs.update(self.special_tokens_map							)
   return BertweetTokenizer.from_pretrained(self.tmpdirname							,		**UpperCAmelCase__							)


 def _lowercase			(  self	:		Dict							,		UpperCAmelCase__	:		Optional[int]							)      -> Dict:
   _a						: List[str]    =			"""I am VinAI Research"""
   _a						: Optional[int]    =			"""I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>"""
   return input_text, output_text







 def _lowercase			(  self	:		Tuple							)      -> Optional[int]:
   _a						: Optional[int]    =			BertweetTokenizer(self.vocab_file							,		self.merges_file							,		**self.special_tokens_map							)
   _a						: Tuple    =			"""I am VinAI Research"""
   _a						: Dict    =			"""I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h""".split()
   _a						: str    =			tokenizer.tokenize(UpperCAmelCase__							)
   self.assertListEqual(UpperCAmelCase__							,		UpperCAmelCase__							)

   _a						: Tuple    =			tokens + [tokenizer.unk_token]

   _a						: Tuple    =			[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
   self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__							)							,		UpperCAmelCase__							)

