




'''simple docstring'''







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 					_a (a__,		unittest.TestCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[Any]          =					KandinskyImgaImgPipeline
     lowerCAmelCase_  :     Optional[Any]          =					["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
     lowerCAmelCase_  :     Optional[Any]          =					[
         """prompt""",
         """negative_prompt""",
         """image_embeds""",
         """negative_image_embeds""",
         """image""",
     ]
     lowerCAmelCase_  :     Any          =					[
         """generator""",
         """height""",
         """width""",
         """strength""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     lowerCAmelCase_  :     Any          =					False







     @property
     def 			snake_case_      (				self							)      ->				Tuple:
          return 32







     @property
     def 			snake_case_      (				self							)      ->				Optional[Any]:
          return 32







     @property
     def 			snake_case_      (				self							)      ->				Tuple:
          return self.time_input_dim







     @property
     def 			snake_case_      (				self							)      ->				Any:
          return self.time_input_dim * 4







     @property
     def 			snake_case_      (				self							)      ->				str:
          return 100







     @property
     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      Union[str, Any]        =			XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""							)
          return tokenizer







     @property
     def 			snake_case_      (				self							)      ->				Union[str, Any]:
          torch.manual_seed(0							)
          snake_case			:      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=1_005							,)

          snake_case			:      int        =			MultilingualCLIP(__a							)
          snake_case			:      List[Any]        =			text_encoder.eval()

          return text_encoder







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

          snake_case			:      Optional[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,
          }

          snake_case			:      str        =			UNetaDConditionModel(**__a							)
          return model







     @property
     def 			snake_case_      (				self							)      ->				Tuple:
          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							)      ->				Tuple:
          torch.manual_seed(0							)
          snake_case			:      Tuple        =			VQModel(**self.dummy_movq_kwargs							)
          return model







     def 			snake_case_      (				self							)      ->				Any:
          snake_case			:      int        =			self.dummy_text_encoder
          snake_case			:      Dict        =			self.dummy_tokenizer
          snake_case			:      Optional[Any]        =			self.dummy_unet
          snake_case			:      List[Any]        =			self.dummy_movq

          snake_case			:      int        =			{
              """num_train_timesteps""": 1_000,
              """beta_schedule""": """linear""",
              """beta_start""": 0.00_085,
              """beta_end""": 0.012,
              """clip_sample""": False,
              """set_alpha_to_one""": False,
              """steps_offset""": 0,
              """prediction_type""": """epsilon""",
              """thresholding""": False,
          }

          snake_case			:      List[Any]        =			DDIMScheduler(**__a							)

          snake_case			:      List[Any]        =			{
              """text_encoder""": text_encoder,
              """tokenizer""": tokenizer,
              """unet""": unet,
              """scheduler""": scheduler,
              """movq""": movq,
          }

          return components







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

          if str(__a							).startswith("""mps"""							):
               snake_case			:      Optional[int]        =			torch.manual_seed(__a							)
          else:
               snake_case			:      Any        =			torch.Generator(device=__a							).manual_seed(__a							)
          snake_case			:      Optional[Any]        =			{
              """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							)      ->				Dict:
          snake_case			:      int        =			"""cpu"""

          snake_case			:      str        =			self.get_dummy_components()

          snake_case			:      Optional[int]        =			self.pipeline_class(**__a							)
          snake_case			:      Any        =			pipe.to(__a							)

          pipe.set_progress_bar_config(disable=__a							)

          snake_case			:      List[Any]        =			pipe(**self.get_dummy_inputs(__a							)							)
          snake_case			:      Tuple        =			output.images

          snake_case			:      Any        =			pipe(
              **self.get_dummy_inputs(__a							)							,return_dict=__a							,)[0]

          snake_case			:      Any        =			image[0, -3:, -3:, -1]
          snake_case			:      Union[str, Any]        =			image_from_tuple[0, -3:, -3:, -1]

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

          snake_case			:      Union[str, Any]        =			np.array(
              [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]							)
          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 					_a (unittest.TestCase	):





     '''simple docstring'''



     def 			snake_case_      (				self							)      ->				Optional[Any]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







     def 			snake_case_      (				self							)      ->				List[Any]:
          snake_case			:      List[Any]        =			load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
              """/kandinsky/kandinsky_img2img_frog.npy"""							)

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

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

          snake_case			:      List[Any]        =			KandinskyImgaImgPipeline.from_pretrained(
              """kandinsky-community/kandinsky-2-1"""							,torch_dtype=torch.floataa							)
          snake_case			:      Any        =			pipeline.to(__a							)

          pipeline.set_progress_bar_config(disable=__a							)

          snake_case			:      int        =			torch.Generator(device="""cpu"""							).manual_seed(0							)
          snake_case ,		snake_case			:      Dict        =			pipe_prior(
              __a							,generator=__a							,num_inference_steps=5							,negative_prompt=""""""							,).to_tuple()

          snake_case			:      Optional[int]        =			pipeline(
              __a							,image=__a							,image_embeds=__a							,negative_image_embeds=__a							,generator=__a							,num_inference_steps=100							,height=768							,width=768							,strength=0.2							,output_type="""np"""							,)

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

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

          assert_mean_pixel_difference(__a							,__a							)









'''simple docstring'''







import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
lowercase      :   Any      =       logging.getLogger(__name__)

if __name__ == "__main__":
      lowercase      :   List[Any]      =       argparse.ArgumentParser(
          description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
      )
      parser.add_argument(
          """--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
      )
      parser.add_argument(
          """--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
      )
      parser.add_argument("""--vocab_size""", default=3_0522, type=int)
      lowercase      :   str      =       parser.parse_args()

      logger.info(F"""Loading data from {args.data_file}""")
      with open(args.data_file, """rb""") as fp:
            lowercase      :   int      =       pickle.load(fp)

      logger.info("""Counting occurrences for MLM.""")
      lowercase      :   List[Any]      =       Counter()
      for tk_ids in data:
            counter.update(tk_ids)
      lowercase      :   int      =       [0] * args.vocab_size
      for k, v in counter.items():
            lowercase      :   List[Any]      =       v

      logger.info(F"""Dump to {args.token_counts_dump}""")
      with open(args.token_counts_dump, """wb""") as handle:
            pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)



