




"""simple docstring"""
from typing import Any, Dict, List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       from ..models.auto.modeling_auto import (
           MODEL_FOR_IMAGE_SEGMENTATION_MAPPING,
           MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING,
           MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING,
           MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING,
       )


__UpperCamelCase    :	str						=     logging.get_logger(__name__)


__UpperCamelCase    :	Optional[int]						=     Dict[str, Any]
__UpperCamelCase    :	List[str]						=     List[Prediction]







@add_end_docstrings(a__     )
class 			a       (      a__     ):



 def __init__(    self							,					*_snake_case							,					**_snake_case    ):







  """simple docstring"""






  super().__init__(*_snake_case							,					**_snake_case    )

  if self.framework == "tf":
   raise ValueError(F'The {self.__class__} is only available in PyTorch.'    )

  requires_backends(self							,					'vision'    )
  self.check_model_type(
      dict(
          MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()
          + MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items()
          + MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items()
          + MODEL_FOR_UNIVERSAL_SEGMENTATION_MAPPING.items()    )    )



 def       UpperCamelCase__     (    self							,					**_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   {}
  lowerCAmelCase													=   {}
  if "subtask" in kwargs:
   lowerCAmelCase													=   kwargs['subtask']
   lowerCAmelCase													=   kwargs['subtask']
  if "threshold" in kwargs:
   lowerCAmelCase													=   kwargs['threshold']
  if "mask_threshold" in kwargs:
   lowerCAmelCase													=   kwargs['mask_threshold']
  if "overlap_mask_area_threshold" in kwargs:
   lowerCAmelCase													=   kwargs['overlap_mask_area_threshold']

  return preprocess_kwargs, {}, postprocess_kwargs



 def __call__(    self							,					_snake_case							,					**_snake_case    ):







  """simple docstring"""






  return super().__call__(_snake_case							,					**_snake_case    )



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None    ):







  """simple docstring"""






  lowerCAmelCase													=   load_image(_snake_case    )
  lowerCAmelCase													=   [(image.height, image.width)]
  if self.model.config.__class__.__name__ == "OneFormerConfig":
   if subtask is None:
    lowerCAmelCase													=   {}
   else:
    lowerCAmelCase													=   {'task_inputs': [subtask]}
   lowerCAmelCase													=   self.image_processor(images=[image]							,					return_tensors='pt'							,					**_snake_case    )
   lowerCAmelCase													=   self.tokenizer(
       inputs['task_inputs']							,					padding='max_length'							,					max_length=self.model.config.task_seq_len							,					return_tensors=self.framework							,					)['input_ids']
  else:
   lowerCAmelCase													=   self.image_processor(images=[image]							,					return_tensors='pt'    )
  lowerCAmelCase													=   target_size
  return inputs



 def       UpperCamelCase__     (    self							,					_snake_case    ):







  """simple docstring"""






  lowerCAmelCase													=   model_inputs.pop('target_size'    )
  lowerCAmelCase													=   self.model(**_snake_case    )
  lowerCAmelCase													=   target_size
  return model_outputs



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=None							,					_snake_case=0.9							,					_snake_case=0.5							,					_snake_case=0.5    ):







  """simple docstring"""






  lowerCAmelCase													=   None
  if subtask in {"panoptic", None} and hasattr(self.image_processor							,					'post_process_panoptic_segmentation'    ):
   lowerCAmelCase													=   self.image_processor.post_process_panoptic_segmentation
  elif subtask in {"instance", None} and hasattr(self.image_processor							,					'post_process_instance_segmentation'    ):
   lowerCAmelCase													=   self.image_processor.post_process_instance_segmentation

  if fn is not None:
   lowerCAmelCase													=   fn(
       _snake_case							,					threshold=_snake_case							,					mask_threshold=_snake_case							,					overlap_mask_area_threshold=_snake_case							,					target_sizes=model_outputs['target_size']							,					)[0]

   lowerCAmelCase													=   []
   lowerCAmelCase													=   outputs['segmentation']

   for segment in outputs["segments_info"]:
    lowerCAmelCase													=   (segmentation == segment['id']) * 2_55
    lowerCAmelCase													=   Image.fromarray(mask.numpy().astype(np.uinta    )							,					mode='L'    )
    lowerCAmelCase													=   self.model.config.idalabel[segment['label_id']]
    lowerCAmelCase													=   segment['score']
    annotation.append({'score': score, 'label': label, 'mask': mask}    )

  elif subtask in {"semantic", None} and hasattr(self.image_processor							,					'post_process_semantic_segmentation'    ):
   lowerCAmelCase													=   self.image_processor.post_process_semantic_segmentation(
       _snake_case							,					target_sizes=model_outputs['target_size']    )[0]

   lowerCAmelCase													=   []
   lowerCAmelCase													=   outputs.numpy()
   lowerCAmelCase													=   np.unique(_snake_case    )

   for label in labels:
    lowerCAmelCase													=   (segmentation == label) * 2_55
    lowerCAmelCase													=   Image.fromarray(mask.astype(np.uinta    )							,					mode='L'    )
    lowerCAmelCase													=   self.model.config.idalabel[label]
    annotation.append({'score': None, 'label': label, 'mask': mask}    )
  else:
   raise ValueError(F'Subtask {subtask} is not supported for model {type(self.model    )}'    )
  return annotation












"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class 			a       (      a__      ,   unittest.TestCase     ):
 snake_case__								=					UnCLIPPipeline
 snake_case__								=					TEXT_TO_IMAGE_PARAMS - {
     '''negative_prompt''',
     '''height''',
     '''width''',
     '''negative_prompt_embeds''',
     '''guidance_scale''',
     '''prompt_embeds''',
     '''cross_attention_kwargs''',
 }
 snake_case__								=					TEXT_TO_IMAGE_BATCH_PARAMS
 snake_case__								=					[
     '''generator''',
     '''return_dict''',
     '''prior_num_inference_steps''',
     '''decoder_num_inference_steps''',
     '''super_res_num_inference_steps''',
 ]
 snake_case__								=					False



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return 32



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return 32



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return self.time_input_dim



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return self.time_input_dim * 4



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return 1_00



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )
  return tokenizer



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  torch.manual_seed(0    )
  lowerCAmelCase													=   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       UpperCamelCase__     (    self    ):







  """simple docstring"""






  torch.manual_seed(0    )

  lowerCAmelCase													=   {
      'num_attention_heads': 2,
      'attention_head_dim': 12,
      'embedding_dim': self.text_embedder_hidden_size,
      'num_layers': 1,
  }

  lowerCAmelCase													=   PriorTransformer(**_snake_case    )
  return model



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  torch.manual_seed(0    )

  lowerCAmelCase													=   {
      'clip_embeddings_dim': self.text_embedder_hidden_size,
      'time_embed_dim': self.time_embed_dim,
      'cross_attention_dim': self.cross_attention_dim,
  }

  lowerCAmelCase													=   UnCLIPTextProjModel(**_snake_case    )
  return model



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  torch.manual_seed(0    )

  lowerCAmelCase													=   {
      'sample_size': 32,
      # RGB in channels
      'in_channels': 3,
      # Out channels is double in channels because predicts mean and variance
      'out_channels': 6,
      '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,
      'cross_attention_dim': self.cross_attention_dim,
      'attention_head_dim': 4,
      'resnet_time_scale_shift': 'scale_shift',
      'class_embed_type': 'identity',
  }

  lowerCAmelCase													=   UNetaDConditionModel(**_snake_case    )
  return model



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return {
      "sample_size": 64,
      "layers_per_block": 1,
      "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
      "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
      "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
      "in_channels": 6,
      "out_channels": 3,
  }



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  torch.manual_seed(0    )

  lowerCAmelCase													=   UNetaDModel(**self.dummy_super_res_kwargs    )
  return model



 @property
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  torch.manual_seed(1    )

  lowerCAmelCase													=   UNetaDModel(**self.dummy_super_res_kwargs    )
  return model



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   self.dummy_prior
  lowerCAmelCase													=   self.dummy_decoder
  lowerCAmelCase													=   self.dummy_text_proj
  lowerCAmelCase													=   self.dummy_text_encoder
  lowerCAmelCase													=   self.dummy_tokenizer
  lowerCAmelCase													=   self.dummy_super_res_first
  lowerCAmelCase													=   self.dummy_super_res_last

  lowerCAmelCase													=   UnCLIPScheduler(
      variance_type='fixed_small_log'							,					prediction_type='sample'							,					num_train_timesteps=10_00							,					clip_sample_range=5.0							,					)

  lowerCAmelCase													=   UnCLIPScheduler(
      variance_type='learned_range'							,					prediction_type='epsilon'							,					num_train_timesteps=10_00							,					)

  lowerCAmelCase													=   UnCLIPScheduler(
      variance_type='fixed_small_log'							,					prediction_type='epsilon'							,					num_train_timesteps=10_00							,					)

  lowerCAmelCase													=   {
      'prior': prior,
      'decoder': decoder,
      'text_proj': text_proj,
      'text_encoder': text_encoder,
      'tokenizer': tokenizer,
      'super_res_first': super_res_first,
      'super_res_last': super_res_last,
      'prior_scheduler': prior_scheduler,
      'decoder_scheduler': decoder_scheduler,
      'super_res_scheduler': super_res_scheduler,
  }

  return components



 def       UpperCamelCase__     (    self							,					_snake_case							,					_snake_case=0    ):







  """simple docstring"""






  if str(_snake_case    ).startswith('mps'    ):
   lowerCAmelCase													=   torch.manual_seed(_snake_case    )
  else:
   lowerCAmelCase													=   torch.Generator(device=_snake_case    ).manual_seed(_snake_case    )
  lowerCAmelCase													=   {
      'prompt': 'horse',
      'generator': generator,
      'prior_num_inference_steps': 2,
      'decoder_num_inference_steps': 2,
      'super_res_num_inference_steps': 2,
      'output_type': 'numpy',
  }
  return inputs



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   'cpu'

  lowerCAmelCase													=   self.get_dummy_components()

  lowerCAmelCase													=   self.pipeline_class(**_snake_case    )
  lowerCAmelCase													=   pipe.to(_snake_case    )

  pipe.set_progress_bar_config(disable=_snake_case    )

  lowerCAmelCase													=   pipe(**self.get_dummy_inputs(_snake_case    )    )
  lowerCAmelCase													=   output.images

  lowerCAmelCase													=   pipe(
      **self.get_dummy_inputs(_snake_case    )							,					return_dict=_snake_case							,					)[0]

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

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

  lowerCAmelCase													=   np.array(
      [
          0.9_997,
          0.9_988,
          0.0_028,
          0.9_997,
          0.9_984,
          0.9_965,
          0.0_029,
          0.9_986,
          0.0_025,
      ]    )

  assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1E-2
  assert np.abs(image_from_tuple_slice.flatten() - expected_slice    ).max() < 1E-2



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   torch.device('cpu'    )







  class 			a       :
   snake_case__								=					1


  lowerCAmelCase													=   self.get_dummy_components()

  lowerCAmelCase													=   self.pipeline_class(**_snake_case    )
  lowerCAmelCase													=   pipe.to(_snake_case    )

  lowerCAmelCase													=   components['prior']
  lowerCAmelCase													=   components['decoder']
  lowerCAmelCase													=   components['super_res_first']
  lowerCAmelCase													=   components['tokenizer']
  lowerCAmelCase													=   components['text_encoder']

  lowerCAmelCase													=   torch.Generator(device=_snake_case    ).manual_seed(0    )
  lowerCAmelCase													=   prior.dtype
  lowerCAmelCase													=   1

  lowerCAmelCase													=   (batch_size, prior.config.embedding_dim)
  lowerCAmelCase													=   pipe.prepare_latents(
      _snake_case							,					dtype=_snake_case							,					device=_snake_case							,					generator=_snake_case							,					latents=_snake_case							,					scheduler=DummyScheduler()    )
  lowerCAmelCase													=   (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
  lowerCAmelCase													=   pipe.prepare_latents(
      _snake_case							,					dtype=_snake_case							,					device=_snake_case							,					generator=_snake_case							,					latents=_snake_case							,					scheduler=DummyScheduler()    )

  lowerCAmelCase													=   (
      batch_size,
      super_res_first.config.in_channels // 2,
      super_res_first.config.sample_size,
      super_res_first.config.sample_size,
  )
  lowerCAmelCase													=   pipe.prepare_latents(
      _snake_case							,					dtype=_snake_case							,					device=_snake_case							,					generator=_snake_case							,					latents=_snake_case							,					scheduler=DummyScheduler()    )

  pipe.set_progress_bar_config(disable=_snake_case    )

  lowerCAmelCase													=   'this is a prompt example'

  lowerCAmelCase													=   torch.Generator(device=_snake_case    ).manual_seed(0    )
  lowerCAmelCase													=   pipe(
      [prompt]							,					generator=_snake_case							,					prior_num_inference_steps=2							,					decoder_num_inference_steps=2							,					super_res_num_inference_steps=2							,					prior_latents=_snake_case							,					decoder_latents=_snake_case							,					super_res_latents=_snake_case							,					output_type='np'							,					)
  lowerCAmelCase													=   output.images

  lowerCAmelCase													=   tokenizer(
      _snake_case							,					padding='max_length'							,					max_length=tokenizer.model_max_length							,					return_tensors='pt'							,					)
  lowerCAmelCase													=   text_encoder(text_inputs.input_ids    )
  lowerCAmelCase													=   text_inputs.attention_mask

  lowerCAmelCase													=   torch.Generator(device=_snake_case    ).manual_seed(0    )
  lowerCAmelCase													=   pipe(
      generator=_snake_case							,					prior_num_inference_steps=2							,					decoder_num_inference_steps=2							,					super_res_num_inference_steps=2							,					prior_latents=_snake_case							,					decoder_latents=_snake_case							,					super_res_latents=_snake_case							,					text_model_output=_snake_case							,					text_attention_mask=_snake_case							,					output_type='np'							,					)[0]

  # make sure passing text embeddings manually is identical
  assert np.abs(image - image_from_text    ).max() < 1E-4



 @skip_mps
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   torch_device == 'cpu'

  self._test_attention_slicing_forward_pass(test_max_difference=_snake_case							,					expected_max_diff=0.01    )



 @skip_mps
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   torch_device == 'cpu'
  lowerCAmelCase													=   True
  lowerCAmelCase													=   [
      'prior_num_inference_steps',
      'decoder_num_inference_steps',
      'super_res_num_inference_steps',
  ]

  self._test_inference_batch_single_identical(
      test_max_difference=_snake_case							,					relax_max_difference=_snake_case							,					additional_params_copy_to_batched_inputs=_snake_case							,					)



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   [
      'prior_num_inference_steps',
      'decoder_num_inference_steps',
      'super_res_num_inference_steps',
  ]

  if torch_device == "mps":
   # TODO: MPS errors with larger batch sizes
   lowerCAmelCase													=   [2, 3]
   self._test_inference_batch_consistent(
       batch_sizes=_snake_case							,					additional_params_copy_to_batched_inputs=_snake_case							,					)
  else:
   self._test_inference_batch_consistent(
       additional_params_copy_to_batched_inputs=_snake_case    )



 @skip_mps
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return super().test_dict_tuple_outputs_equivalent()



 @skip_mps
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return super().test_save_load_local()



 @skip_mps
 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  return super().test_save_load_optional_components()







@nightly
class 			a       (      unittest.TestCase     ):



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/unclip/karlo_v1_alpha_horse_cpu.npy'    )

  lowerCAmelCase													=   UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'    )
  pipeline.set_progress_bar_config(disable=_snake_case    )

  lowerCAmelCase													=   torch.manual_seed(0    )
  lowerCAmelCase													=   pipeline(
      'horse'							,					num_images_per_prompt=1							,					generator=_snake_case							,					output_type='np'							,					)

  lowerCAmelCase													=   output.images[0]

  assert image.shape == (2_56, 2_56, 3)
  assert np.abs(expected_image - image    ).max() < 1E-1







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



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  super().tearDown()
  gc.collect()
  torch.cuda.empty_cache()



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  lowerCAmelCase													=   load_numpy(
      'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
      '/unclip/karlo_v1_alpha_horse_fp16.npy'    )

  lowerCAmelCase													=   UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,					torch_dtype=torch.floataa    )
  lowerCAmelCase													=   pipeline.to(_snake_case    )
  pipeline.set_progress_bar_config(disable=_snake_case    )

  lowerCAmelCase													=   torch.Generator(device='cpu'    ).manual_seed(0    )
  lowerCAmelCase													=   pipeline(
      'horse'							,					generator=_snake_case							,					output_type='np'							,					)

  lowerCAmelCase													=   output.images[0]

  assert image.shape == (2_56, 2_56, 3)

  assert_mean_pixel_difference(_snake_case							,					_snake_case    )



 def       UpperCamelCase__     (    self    ):







  """simple docstring"""






  torch.cuda.empty_cache()
  torch.cuda.reset_max_memory_allocated()
  torch.cuda.reset_peak_memory_stats()

  lowerCAmelCase													=   UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'							,					torch_dtype=torch.floataa    )
  lowerCAmelCase													=   pipe.to(_snake_case    )
  pipe.set_progress_bar_config(disable=_snake_case    )
  pipe.enable_attention_slicing()
  pipe.enable_sequential_cpu_offload()

  lowerCAmelCase													=   pipe(
      'horse'							,					num_images_per_prompt=1							,					prior_num_inference_steps=2							,					decoder_num_inference_steps=2							,					super_res_num_inference_steps=2							,					output_type='np'							,					)

  lowerCAmelCase													=   torch.cuda.max_memory_allocated()
  # make sure that less than 7 GB is allocated
  assert mem_bytes < 7 * 10**9





