
"""simple docstring"""
import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class   _UpperCAmelCase					(       _snake_case		,       unittest.TestCase):
    __lowercase				:			Union[str, Any]	=       KandinskyVaaPriorEmbaEmbPipeline
    __lowercase				:			Optional[Any]	=       ["""prompt""", """image"""]
    __lowercase				:			List[str]	=       ["""prompt""", """image"""]
    __lowercase				:			int	=       [
        """num_images_per_prompt""",
        """strength""",
        """generator""",
        """num_inference_steps""",
        """latents""",
        """negative_prompt""",
        """guidance_scale""",
        """output_type""",
        """return_dict""",
    ]
    __lowercase				:			Optional[Any]	=       False
    @property
    def        lowerCamelCase__						(     self  ):
      return 32
    @property
    def        lowerCamelCase__						(     self  ):
      return 32
    @property
    def        lowerCamelCase__						(     self  ):
      return self.time_input_dim
    @property
    def        lowerCamelCase__						(     self  ):
      return self.time_input_dim * 4
    @property
    def        lowerCamelCase__						(     self  ):
      return 1_00
    @property
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Dict            = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"  )
      return tokenizer
    @property
    def        lowerCamelCase__						(     self  ):
      torch.manual_seed(0  )
      _snake_case  :    Tuple            = CLIPTextConfig(
          bos_token_id=0    ,     eos_token_id=2    ,     hidden_size=self.text_embedder_hidden_size    ,     projection_dim=self.text_embedder_hidden_size    ,     intermediate_size=37    ,     layer_norm_eps=1E-05    ,     num_attention_heads=4    ,     num_hidden_layers=5    ,     pad_token_id=1    ,     vocab_size=10_00    ,     )
      return CLIPTextModelWithProjection(snake_case_  )
    @property
    def        lowerCamelCase__						(     self  ):
      torch.manual_seed(0  )

      _snake_case  :    Tuple            = {
          "num_attention_heads": 2,
          "attention_head_dim": 12,
          "embedding_dim": self.text_embedder_hidden_size,
          "num_layers": 1,
      }

      _snake_case  :    Optional[int]            = PriorTransformer(**snake_case_  )
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      _snake_case  :    Optional[int]            = nn.Parameter(torch.ones(model.clip_std.shape  )  )
      return model
    @property
    def        lowerCamelCase__						(     self  ):
      torch.manual_seed(0  )
      _snake_case  :    int            = CLIPVisionConfig(
          hidden_size=self.text_embedder_hidden_size    ,     image_size=2_24    ,     projection_dim=self.text_embedder_hidden_size    ,     intermediate_size=37    ,     num_attention_heads=4    ,     num_channels=3    ,     num_hidden_layers=5    ,     patch_size=14    ,     )

      _snake_case  :    Optional[Any]            = CLIPVisionModelWithProjection(snake_case_  )
      return model
    @property
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[Any]            = CLIPImageProcessor(
          crop_size=2_24    ,     do_center_crop=snake_case_    ,     do_normalize=snake_case_    ,     do_resize=snake_case_    ,     image_mean=[0.48145466, 0.4578275, 0.40821073]    ,     image_std=[0.26862954, 0.26130258, 0.27577711]    ,     resample=3    ,     size=2_24    ,     )

      return image_processor
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Optional[int]            = self.dummy_prior
      _snake_case  :    str            = self.dummy_image_encoder
      _snake_case  :    Any            = self.dummy_text_encoder
      _snake_case  :    List[str]            = self.dummy_tokenizer
      _snake_case  :    List[str]            = self.dummy_image_processor

      _snake_case  :    Tuple            = UnCLIPScheduler(
          variance_type="fixed_small_log"    ,     prediction_type="sample"    ,     num_train_timesteps=10_00    ,     clip_sample=snake_case_    ,     clip_sample_range=10.0    ,     )

      _snake_case  :    List[Any]            = {
          "prior": prior,
          "image_encoder": image_encoder,
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "scheduler": scheduler,
          "image_processor": image_processor,
      }

      return components
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=0  ):
      if str(snake_case_  ).startswith("mps"  ):
        _snake_case  :    str            = torch.manual_seed(snake_case_  )
      else:
        _snake_case  :    List[str]            = torch.Generator(device=snake_case_  ).manual_seed(snake_case_  )

      _snake_case  :    Union[str, Any]            = floats_tensor((1, 3, 64, 64)    ,     rng=random.Random(snake_case_  )  ).to(snake_case_  )
      _snake_case  :    str            = image.cpu().permute(0    ,     2    ,     3    ,     1  )[0]
      _snake_case  :    Union[str, Any]            = Image.fromarray(np.uinta(snake_case_  )  ).convert("RGB"  ).resize((2_56, 2_56)  )

      _snake_case  :    Optional[Any]            = {
          "prompt": "horse",
          "image": init_image,
          "strength": 0.5,
          "generator": generator,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Tuple            = "cpu"

      _snake_case  :    str            = self.get_dummy_components()

      _snake_case  :    List[str]            = self.pipeline_class(**snake_case_  )
      _snake_case  :    List[Any]            = pipe.to(snake_case_  )

      pipe.set_progress_bar_config(disable=snake_case_  )

      _snake_case  :    Dict            = pipe(**self.get_dummy_inputs(snake_case_  )  )
      _snake_case  :    Union[str, Any]            = output.image_embeds

      _snake_case  :    Optional[int]            = pipe(
          **self.get_dummy_inputs(snake_case_  )    ,     return_dict=snake_case_    ,     )[0]

      _snake_case  :    str            = image[0, -10:]
      _snake_case  :    Optional[Any]            = image_from_tuple[0, -10:]

      assert image.shape == (1, 32)

      _snake_case  :    List[Any]            = np.array(
          [
              0.1071284,
              1.3330271,
              0.61260223,
              -0.6691065,
              -0.3846852,
              -1.0303661,
              0.22716111,
              0.03348901,
              0.30040675,
              -0.24805029,
          ]  )

      assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-2
      assert np.abs(image_from_tuple_slice.flatten() - expected_slice  ).max() < 1E-2
    @skip_mps
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    List[Any]            = torch_device == "cpu"
      _snake_case  :    Union[str, Any]            = True
      _snake_case  :    Any            = False

      self._test_inference_batch_single_identical(
          test_max_difference=snake_case_    ,     relax_max_difference=snake_case_    ,     test_mean_pixel_difference=snake_case_    ,     )


    @skip_mps
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    Union[str, Any]            = torch_device == "cpu"
      _snake_case  :    Optional[int]            = False

      self._test_attention_slicing_forward_pass(
          test_max_difference=snake_case_    ,     test_mean_pixel_difference=snake_case_    ,     )






'''simple docstring'''







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_						):
    UpperCamelCase__	     =      copy.deepcopy(lowerCAmelCase_						)
    UpperCamelCase__	     =      sorted(lowerCAmelCase_						)
    UpperCamelCase__	     =      [bisect.bisect_right(lowerCAmelCase_       ,	lowerCAmelCase_						) for y in x]
    return quantized





def     snake_case__			(lowerCAmelCase_       ,	lowerCAmelCase_=0						):
    UpperCamelCase__	     =      np.arange(start=3       ,	stop=lowerCAmelCase_       ,	step=4						).tolist() if k > 0 else [10]
    UpperCamelCase__	     =      _quantize(lowerCAmelCase_       ,	lowerCAmelCase_						)
    # count number of elements per group
    UpperCamelCase__	     =      np.unique(lowerCAmelCase_       ,	return_counts=lowerCAmelCase_						)[1]
    UpperCamelCase__	     =      [0] + bins + [np.inf]
    logger.info("Using {} as bins for aspect lengths quantization".format(lowerCAmelCase_						)						)
    logger.info("Count of instances per bin: {}".format(lowerCAmelCase_						)						)
    return groups





class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       if not isinstance(__UpperCamelCase  ,    __UpperCamelCase			):
           raise ValueError(
               "sampler should be an instance of torch.utils.data.Sampler, but got sampler={}".format(__UpperCamelCase			)			)
       UpperCamelCase__	     =      sampler
       UpperCamelCase__	     =      group_ids
       UpperCamelCase__	     =      batch_size


   def __iter__(	self			):
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)
       UpperCamelCase__	     =      defaultdict(__UpperCamelCase			)

       UpperCamelCase__	     =      0
       for idx in self.sampler:
           UpperCamelCase__	     =      self.group_ids[idx]
           buffer_per_group[group_id].append(__UpperCamelCase			)
           samples_per_group[group_id].append(__UpperCamelCase			)
           if len(buffer_per_group[group_id]			) == self.batch_size:
               yield buffer_per_group[group_id]  # TODO
               num_batches += 1
               del buffer_per_group[group_id]
           assert len(buffer_per_group[group_id]			) < self.batch_size

       # now we have run out of elements that satisfy
       # the group criteria, let's return the remaining
       # elements so that the size of the sampler is
       # deterministic
       UpperCamelCase__	     =      len(self			)
       UpperCamelCase__	     =      expected_num_batches - num_batches
       if num_remaining > 0:
           # for the remaining batches, group the batches by similar lengths
           UpperCamelCase__	     =      []
           for group_id, idxs in sorted(buffer_per_group.items()  ,    key=lambda __UpperCamelCase			: x[0]			):
               batch_idx.extend(__UpperCamelCase			)
               if len(__UpperCamelCase			) >= self.batch_size:
                   yield batch_idx[: self.batch_size]
                   UpperCamelCase__	     =      batch_idx[self.batch_size :]
                   num_remaining -= 1
           if len(__UpperCamelCase			) > 0:
               yield batch_idx
               num_remaining -= 1
       assert num_remaining == 0







   def __len__(	self			):
       return (len(self.sampler			) + self.batch_size - 1) // self.batch_size




