



import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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    __UpperCamelCase			(       _lowercase	,	unittest.TestCase				):

 """simple docstring"""



 _lowercase   :    Union[str, Any]      						=					KandinskyInpaintPipeline
 _lowercase   :    List[Any]      						=					['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
 _lowercase   :    List[Any]      						=					[
     '''prompt''',
     '''negative_prompt''',
     '''image_embeds''',
     '''negative_image_embeds''',
     '''image''',
     '''mask_image''',
 ]
 _lowercase   :    Dict      						=					[
     '''generator''',
     '''height''',
     '''width''',
     '''latents''',
     '''guidance_scale''',
     '''negative_prompt''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowercase   :    List[str]      						=					False

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  return 3_2

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  return 3_2

 @property
 def 						_UpperCAmelCase       (	self      ) ->			str:
  return self.time_input_dim

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  return self.time_input_dim * 4

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  return 1_0_0

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
  return tokenizer

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  torch.manual_seed(0      )
  a__												= MCLIPConfig(
      numDims=self.cross_attention_dim		,				transformerDimensions=self.text_embedder_hidden_size		,				hidden_size=self.text_embedder_hidden_size		,				intermediate_size=3_7		,				num_attention_heads=4		,				num_hidden_layers=5		,				vocab_size=1_0_0_5		,				)

  a__												= MultilingualCLIP(SCREAMING_SNAKE_CASE      )
  a__												= text_encoder.eval()

  return text_encoder

 @property
 def 						_UpperCAmelCase       (	self      ) ->			int:
  torch.manual_seed(0      )

  a__												= {
      '''in_channels''': 9,
      # 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__												= UNetaDConditionModel(**SCREAMING_SNAKE_CASE      )
  return model

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Union[str, Any]:
  return {
      "block_out_channels": [3_2, 6_4],
      "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": 1_2,
      "out_channels": 3,
      "up_block_types": [
          "AttnUpDecoderBlock2D",
          "UpDecoderBlock2D",
      ],
      "vq_embed_dim": 4,
  }

 @property
 def 						_UpperCAmelCase       (	self      ) ->			Optional[Any]:
  torch.manual_seed(0      )
  a__												= VQModel(**self.dummy_movq_kwargs      )
  return model

 def 						_UpperCAmelCase       (	self      ) ->			Any:
  a__												= self.dummy_text_encoder
  a__												= self.dummy_tokenizer
  a__												= self.dummy_unet
  a__												= self.dummy_movq

  a__												= DDIMScheduler(
      num_train_timesteps=1_0_0_0		,				beta_schedule='''linear'''		,				beta_start=0.0_00_85		,				beta_end=0.0_12		,				clip_sample=SCREAMING_SNAKE_CASE		,				set_alpha_to_one=SCREAMING_SNAKE_CASE		,				steps_offset=1		,				prediction_type='''epsilon'''		,				thresholding=SCREAMING_SNAKE_CASE		,				)

  a__												= {
      '''text_encoder''': text_encoder,
      '''tokenizer''': tokenizer,
      '''unet''': unet,
      '''scheduler''': scheduler,
      '''movq''': movq,
  }

  return components

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=0      ) ->			Any:
  a__												= floats_tensor((1, self.cross_attention_dim)		,				rng=random.Random(SCREAMING_SNAKE_CASE      )      ).to(SCREAMING_SNAKE_CASE      )
  a__												= floats_tensor((1, self.cross_attention_dim)		,				rng=random.Random(seed + 1      )      ).to(SCREAMING_SNAKE_CASE      )
  # create init_image
  a__												= floats_tensor((1, 3, 6_4, 6_4)		,				rng=random.Random(SCREAMING_SNAKE_CASE      )      ).to(SCREAMING_SNAKE_CASE      )
  a__												= image.cpu().permute(0		,				2		,				3		,				1      )[0]
  a__												= Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE      )      ).convert('''RGB'''      ).resize((2_5_6, 2_5_6)      )
  # create mask
  a__												= np.ones((6_4, 6_4)		,				dtype=np.floataa      )
  a__												= 0

  if str(SCREAMING_SNAKE_CASE      ).startswith('''mps'''      ):
   a__												= torch.manual_seed(SCREAMING_SNAKE_CASE      )
  else:
   a__												= torch.Generator(device=SCREAMING_SNAKE_CASE      ).manual_seed(SCREAMING_SNAKE_CASE      )
  a__												= {
      '''prompt''': '''horse''',
      '''image''': init_image,
      '''mask_image''': mask,
      '''image_embeds''': image_embeds,
      '''negative_image_embeds''': negative_image_embeds,
      '''generator''': generator,
      '''height''': 6_4,
      '''width''': 6_4,
      '''num_inference_steps''': 2,
      '''guidance_scale''': 4.0,
      '''output_type''': '''np''',
  }
  return inputs

 def 						_UpperCAmelCase       (	self      ) ->			List[str]:
  a__												= '''cpu'''

  a__												= self.get_dummy_components()

  a__												= self.pipeline_class(**SCREAMING_SNAKE_CASE      )
  a__												= pipe.to(SCREAMING_SNAKE_CASE      )

  pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )      )
  a__												= output.images

  a__												= pipe(
      **self.get_dummy_inputs(SCREAMING_SNAKE_CASE      )		,				return_dict=SCREAMING_SNAKE_CASE		,				)[0]

  a__												= image[0, -3:, -3:, -1]
  a__												= image_from_tuple[0, -3:, -3:, -1]

  print(f"image.shape {image.shape}"      )

  assert image.shape == (1, 6_4, 6_4, 3)

  a__												= np.array(
      [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]      )

  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()}"



 def 						_UpperCAmelCase       (	self      ) ->			List[Any]:
  super().test_inference_batch_single_identical(expected_max_diff=3e-3      )





@slow
@require_torch_gpu
class    __UpperCamelCase			(       unittest.TestCase				):

 """simple docstring"""



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



 def 						_UpperCAmelCase       (	self      ) ->			int:
  a__												= load_numpy(
      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
      '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''      )

  a__												= load_image(
      '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
  a__												= np.ones((7_6_8, 7_6_8)		,				dtype=np.floataa      )
  a__												= 0

  a__												= '''a hat'''

  a__												= KandinskyPriorPipeline.from_pretrained(
      '''kandinsky-community/kandinsky-2-1-prior'''		,				torch_dtype=torch.floataa      )
  pipe_prior.to(SCREAMING_SNAKE_CASE      )

  a__												= KandinskyInpaintPipeline.from_pretrained(
      '''kandinsky-community/kandinsky-2-1-inpaint'''		,				torch_dtype=torch.floataa      )
  a__												= pipeline.to(SCREAMING_SNAKE_CASE      )
  pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE      )

  a__												= torch.Generator(device='''cpu'''      ).manual_seed(0      )
  a__							,			a__												= pipe_prior(
      SCREAMING_SNAKE_CASE		,				generator=SCREAMING_SNAKE_CASE		,				num_inference_steps=5		,				negative_prompt=''''''		,				).to_tuple()

  a__												= pipeline(
      SCREAMING_SNAKE_CASE		,				image=SCREAMING_SNAKE_CASE		,				mask_image=SCREAMING_SNAKE_CASE		,				image_embeds=SCREAMING_SNAKE_CASE		,				negative_image_embeds=SCREAMING_SNAKE_CASE		,				generator=SCREAMING_SNAKE_CASE		,				num_inference_steps=1_0_0		,				height=7_6_8		,				width=7_6_8		,				output_type='''np'''		,				)

  a__												= output.images[0]

  assert image.shape == (7_6_8, 7_6_8, 3)

  assert_mean_pixel_difference(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )










import torch
from torch import nn
class    __UpperCamelCase			(       nn.Module				):

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=1		,				SCREAMING_SNAKE_CASE=False      ) ->			Tuple:
  super().__init__()

  a__												= n_token
  a__												= d_embed
  a__												= d_proj

  a__												= cutoffs + [n_token]
  a__												= [0] + self.cutoffs
  a__												= div_val

  a__												= self.cutoffs[0]
  a__												= len(self.cutoffs      ) - 1
  a__												= self.shortlist_size + self.n_clusters

  if self.n_clusters > 0:
   a__												= nn.Parameter(torch.zeros(self.n_clusters		,				self.d_embed      )      )
   a__												= nn.Parameter(torch.zeros(self.n_clusters      )      )

  a__												= nn.ModuleList()
  a__												= nn.ParameterList()

  if div_val == 1:
   for i in range(len(self.cutoffs      )      ):
    if d_proj != d_embed:
     self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )      )
    else:
     self.out_projs.append(SCREAMING_SNAKE_CASE      )

   self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )
  else:
   for i in range(len(self.cutoffs      )      ):
    a__							,			a__												= self.cutoff_ends[i], self.cutoff_ends[i + 1]
    a__												= d_embed // (div_val**i)

    self.out_projs.append(nn.Parameter(torch.FloatTensor(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )      )      )

    self.out_layers.append(nn.Linear(SCREAMING_SNAKE_CASE		,				r_idx - l_idx      )      )

  a__												= keep_order

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			str:
  if proj is None:
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE      )
  else:
   # if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				proj.t().contiguous()      )
   a__												= nn.functional.linear(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				bias=SCREAMING_SNAKE_CASE      )
   # else:
   #     logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
   #     if bias is not None:
   #         logit = logit + bias

  return logit

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=None		,				SCREAMING_SNAKE_CASE=False      ) ->			Optional[int]:

  if labels is not None:
   # Shift so that tokens < n predict n
   a__												= hidden[..., :-1, :].contiguous()
   a__												= labels[..., 1:].contiguous()
   a__												= hidden.view(-1		,				hidden.size(-1      )      )
   a__												= labels.view(-1      )
   if hidden.size(0      ) != labels.size(0      ):
    raise RuntimeError('''Input and labels should have the same size in the batch dimension.'''      )
  else:
   a__												= hidden.view(-1		,				hidden.size(-1      )      )

  if self.n_clusters == 0:
   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]      )
   if labels is not None:
    a__												= labels != -1_0_0
    a__												= torch.zeros_like(SCREAMING_SNAKE_CASE		,				dtype=hidden.dtype		,				device=hidden.device      )
    a__												= (
        -nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=-1      )[mask].gather(1		,				labels[mask].unsqueeze(1      )      ).squeeze(1      )
    )
   else:
    a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=-1      )
  else:
   # construct weights and biases
   a__							,			a__												= [], []
   for i in range(len(self.cutoffs      )      ):
    if self.div_val == 1:
     a__							,			a__												= self.cutoff_ends[i], self.cutoff_ends[i + 1]
     a__												= self.out_layers[0].weight[l_idx:r_idx]
     a__												= self.out_layers[0].bias[l_idx:r_idx]
    else:
     a__												= self.out_layers[i].weight
     a__												= self.out_layers[i].bias

    if i == 0:
     a__												= torch.cat([weight_i, self.cluster_weight]		,				dim=0      )
     a__												= torch.cat([bias_i, self.cluster_bias]		,				dim=0      )

    weights.append(SCREAMING_SNAKE_CASE      )
    biases.append(SCREAMING_SNAKE_CASE      )

   a__							,			a__							,			a__												= weights[0], biases[0], self.out_projs[0]

   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
   a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )

   if labels is None:
    a__												= hidden.new_empty((head_logit.size(0      ), self.n_token)      )
   else:
    a__												= torch.zeros_like(SCREAMING_SNAKE_CASE		,				dtype=hidden.dtype		,				device=hidden.device      )

   a__												= 0
   a__												= [0] + self.cutoffs
   for i in range(len(SCREAMING_SNAKE_CASE      ) - 1      ):
    a__							,			a__												= cutoff_values[i], cutoff_values[i + 1]

    if labels is not None:
     a__												= (labels >= l_idx) & (labels < r_idx)
     a__												= mask_i.nonzero().squeeze()

     if indices_i.numel() == 0:
      continue

     a__												= labels.index_select(0		,				SCREAMING_SNAKE_CASE      ) - l_idx
     a__												= head_logprob.index_select(0		,				SCREAMING_SNAKE_CASE      )
     a__												= hidden.index_select(0		,				SCREAMING_SNAKE_CASE      )
    else:
     a__												= hidden

    if i == 0:
     if labels is not None:
      a__												= head_logprob_i.gather(1		,				target_i[:, None]      ).squeeze(1      )
     else:
      a__												= head_logprob[:, : self.cutoffs[0]]
    else:
     a__							,			a__							,			a__												= weights[i], biases[i], self.out_projs[i]

     a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
     a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )
     a__												= self.cutoffs[0] + i - 1  # No probability for the head cluster
     if labels is not None:
      a__												= head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
          1		,				target_i[:, None]      ).squeeze(1      )
     else:
      a__												= head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
      a__												= logprob_i

    if labels is not None:
     if (hasattr(self		,				'''keep_order'''      ) and self.keep_order) or keep_order:
      out.index_copy_(0		,				SCREAMING_SNAKE_CASE		,				-logprob_i      )
     else:
      out[offset : offset + logprob_i.size(0      )].copy_(-logprob_i      )
     offset += logprob_i.size(0      )

  return out



 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			List[str]:
  if self.n_clusters == 0:
   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				self.out_layers[0].weight		,				self.out_layers[0].bias		,				self.out_projs[0]      )
   return nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=-1      )
  else:
   # construct weights and biases
   a__							,			a__												= [], []
   for i in range(len(self.cutoffs      )      ):
    if self.div_val == 1:
     a__							,			a__												= self.cutoff_ends[i], self.cutoff_ends[i + 1]
     a__												= self.out_layers[0].weight[l_idx:r_idx]
     a__												= self.out_layers[0].bias[l_idx:r_idx]
    else:
     a__												= self.out_layers[i].weight
     a__												= self.out_layers[i].bias

    if i == 0:
     a__												= torch.cat([weight_i, self.cluster_weight]		,				dim=0      )
     a__												= torch.cat([bias_i, self.cluster_bias]		,				dim=0      )

    weights.append(SCREAMING_SNAKE_CASE      )
    biases.append(SCREAMING_SNAKE_CASE      )

   a__							,			a__							,			a__												= weights[0], biases[0], self.out_projs[0]
   a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )

   a__												= hidden.new_empty((head_logit.size(0      ), self.n_token)      )
   a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )

   a__												= [0] + self.cutoffs
   for i in range(len(SCREAMING_SNAKE_CASE      ) - 1      ):
    a__							,			a__												= cutoff_values[i], cutoff_values[i + 1]

    if i == 0:
     a__												= head_logprob[:, : self.cutoffs[0]]
    else:
     a__							,			a__							,			a__												= weights[i], biases[i], self.out_projs[i]

     a__												= self._compute_logit(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      )
     a__												= nn.functional.log_softmax(SCREAMING_SNAKE_CASE		,				dim=1      )

     a__												= head_logprob[:, -i] + tail_logprob_i
     a__												= logprob_i

   return out





