
import gc
import random
import unittest

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

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class      _UpperCAmelCase							(		SCREAMING_SNAKE_CASE_     ,			unittest.TestCase							):
      __SCREAMING_SNAKE_CASE   :							Tuple							      =	UnCLIPImageVariationPipeline
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
      __SCREAMING_SNAKE_CASE   :							Any							      =	IMAGE_VARIATION_BATCH_PARAMS

      __SCREAMING_SNAKE_CASE   :							Dict							      =	[
          'generator',
          'return_dict',
          'decoder_num_inference_steps',
          'super_res_num_inference_steps',
      ]
      __SCREAMING_SNAKE_CASE   :							List[str]							      =	False




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




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




      @property
      def     a_      (							self							)						->					Dict:
          return self.time_input_dim




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




      @property
      def     a_      (							self							)						->					Dict:
          return 1_0_0




      @property
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'							)
          return tokenizer




      @property
      def     a_      (							self							)						->					Optional[Any]:
          torch.manual_seed(0							)
          UpperCAmelCase									=					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=3_7   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1_0_0_0   ,       )
          return CLIPTextModelWithProjection(lowercase_							)




      @property
      def     a_      (							self							)						->					Dict:
          torch.manual_seed(0							)
          UpperCAmelCase									=					CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size   ,       projection_dim=self.text_embedder_hidden_size   ,       num_hidden_layers=5   ,       num_attention_heads=4   ,       image_size=3_2   ,       intermediate_size=3_7   ,       patch_size=1   ,       )
          return CLIPVisionModelWithProjection(lowercase_							)




      @property
      def     a_      (							self							)						->					str:
          torch.manual_seed(0							)

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

          UpperCAmelCase									=					UnCLIPTextProjModel(**lowercase_							)
          return model




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

          UpperCAmelCase									=					{
              'sample_size': 3_2,
              # 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',
          }

          UpperCAmelCase									=					UNetaDConditionModel(**lowercase_							)
          return model




      @property
      def     a_      (							self							)						->					Tuple:
          return {
              "sample_size": 6_4,
              "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     a_      (							self							)						->					str:
          torch.manual_seed(0							)

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




      @property
      def     a_      (							self							)						->					List[Any]:
          torch.manual_seed(1							)

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




      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					self.dummy_decoder
          UpperCAmelCase									=					self.dummy_text_proj
          UpperCAmelCase									=					self.dummy_text_encoder
          UpperCAmelCase									=					self.dummy_tokenizer
          UpperCAmelCase									=					self.dummy_super_res_first
          UpperCAmelCase									=					self.dummy_super_res_last

          UpperCAmelCase									=					UnCLIPScheduler(
              variance_type='learned_range'   ,       prediction_type='epsilon'   ,       num_train_timesteps=1_0_0_0   ,       )

          UpperCAmelCase									=					UnCLIPScheduler(
              variance_type='fixed_small_log'   ,       prediction_type='epsilon'   ,       num_train_timesteps=1_0_0_0   ,       )

          UpperCAmelCase									=					CLIPImageProcessor(crop_size=3_2   ,       size=3_2							)

          UpperCAmelCase									=					self.dummy_image_encoder

          return {
              "decoder": decoder,
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "text_proj": text_proj,
              "feature_extractor": feature_extractor,
              "image_encoder": image_encoder,
              "super_res_first": super_res_first,
              "super_res_last": super_res_last,
              "decoder_scheduler": decoder_scheduler,
              "super_res_scheduler": super_res_scheduler,
          }




      def     a_      (							self   ,       lowercase_   ,       lowercase_=0   ,       lowercase_=True							)						->					Optional[int]:
          UpperCAmelCase									=					floats_tensor((1, 3, 3_2, 3_2)   ,       rng=random.Random(lowercase_							)							).to(lowercase_							)
          if str(lowercase_							).startswith('mps'							):
              UpperCAmelCase									=					torch.manual_seed(lowercase_							)
          else:
              UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(lowercase_							)

          if pil_image:
              UpperCAmelCase									=					input_image * 0.5 + 0.5
              UpperCAmelCase									=					input_image.clamp(0   ,       1							)
              UpperCAmelCase									=					input_image.cpu().permute(0   ,       2   ,       3   ,       1							).float().numpy()
              UpperCAmelCase									=					DiffusionPipeline.numpy_to_pil(lowercase_							)[0]

          return {
              "image": input_image,
              "generator": generator,
              "decoder_num_inference_steps": 2,
              "super_res_num_inference_steps": 2,
              "output_type": "np",
          }




      def     a_      (							self							)						->					List[Any]:
          UpperCAmelCase									=					'cpu'

          UpperCAmelCase									=					self.get_dummy_components()

          UpperCAmelCase									=					self.pipeline_class(**lowercase_							)
          UpperCAmelCase									=					pipe.to(lowercase_							)

          pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)

          UpperCAmelCase									=					pipe(**lowercase_							)
          UpperCAmelCase									=					output.images

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)

          UpperCAmelCase									=					pipe(
              **lowercase_   ,       return_dict=lowercase_   ,       )[0]

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

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

          UpperCAmelCase									=					np.array(
              [
                  0.9_9_9_7,
                  0.0_0_0_2,
                  0.9_9_9_7,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.0_0_2_3,
                  0.9_9_9_7,
                  0.9_9_6_9,
                  0.9_9_7_0,
              ]							)

          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     a_      (							self							)						->					Any:
          UpperCAmelCase									=					'cpu'

          UpperCAmelCase									=					self.get_dummy_components()

          UpperCAmelCase									=					self.pipeline_class(**lowercase_							)
          UpperCAmelCase									=					pipe.to(lowercase_							)

          pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)

          UpperCAmelCase									=					pipe(**lowercase_							)
          UpperCAmelCase									=					output.images

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)

          UpperCAmelCase									=					pipe(
              **lowercase_   ,       return_dict=lowercase_   ,       )[0]

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

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

          UpperCAmelCase									=					np.array([0.9_9_9_7, 0.0_0_0_3, 0.9_9_9_7, 0.9_9_9_7, 0.9_9_7_0, 0.0_0_2_4, 0.9_9_9_7, 0.9_9_7_1, 0.9_9_7_1]							)

          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     a_      (							self							)						->					Optional[int]:
          UpperCAmelCase									=					'cpu'

          UpperCAmelCase									=					self.get_dummy_components()

          UpperCAmelCase									=					self.pipeline_class(**lowercase_							)
          UpperCAmelCase									=					pipe.to(lowercase_							)

          pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)
          UpperCAmelCase									=					[
              pipeline_inputs['image'],
              pipeline_inputs['image'],
          ]

          UpperCAmelCase									=					pipe(**lowercase_							)
          UpperCAmelCase									=					output.images

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)
          UpperCAmelCase									=					[
              tuple_pipeline_inputs['image'],
              tuple_pipeline_inputs['image'],
          ]

          UpperCAmelCase									=					pipe(
              **lowercase_   ,       return_dict=lowercase_   ,       )[0]

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

          assert image.shape == (2, 6_4, 6_4, 3)

          UpperCAmelCase									=					np.array(
              [
                  0.9_9_9_7,
                  0.9_9_8_9,
                  0.0_0_0_8,
                  0.0_0_2_1,
                  0.9_9_6_0,
                  0.0_0_1_8,
                  0.0_0_1_4,
                  0.0_0_0_2,
                  0.9_9_3_3,
              ]							)

          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     a_      (							self							)						->					Any:
          UpperCAmelCase									=					torch.device('cpu'							)
          class      _UpperCAmelCase							:
                __SCREAMING_SNAKE_CASE   :							Tuple							      =	1

          UpperCAmelCase									=					self.get_dummy_components()

          UpperCAmelCase									=					self.pipeline_class(**lowercase_							)
          UpperCAmelCase									=					pipe.to(lowercase_							)

          pipe.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					torch.Generator(device=lowercase_							).manual_seed(0							)
          UpperCAmelCase									=					pipe.decoder.dtype
          UpperCAmelCase									=					1

          UpperCAmelCase									=					(
              batch_size,
              pipe.decoder.config.in_channels,
              pipe.decoder.config.sample_size,
              pipe.decoder.config.sample_size,
          )
          UpperCAmelCase									=					pipe.prepare_latents(
              lowercase_   ,       dtype=lowercase_   ,       device=lowercase_   ,       generator=lowercase_   ,       latents=lowercase_   ,       scheduler=DummyScheduler()							)

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

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)

          UpperCAmelCase									=					pipe(
              **lowercase_   ,       decoder_latents=lowercase_   ,       super_res_latents=lowercase_							).images

          UpperCAmelCase									=					self.get_dummy_inputs(lowercase_   ,       pil_image=lowercase_							)
          # Don't pass image, instead pass embedding
          UpperCAmelCase									=					pipeline_inputs.pop('image'							)
          UpperCAmelCase									=					pipe.image_encoder(lowercase_							).image_embeds

          UpperCAmelCase									=					pipe(
              **lowercase_   ,       decoder_latents=lowercase_   ,       super_res_latents=lowercase_   ,       image_embeddings=lowercase_   ,       ).images

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




      @skip_mps
      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					torch_device == 'cpu'

          # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
          UpperCAmelCase									=					1E-2

          self._test_attention_slicing_forward_pass(
              test_max_difference=lowercase_   ,       expected_max_diff=lowercase_							)




      @skip_mps
      def     a_      (							self							)						->					Tuple:
          UpperCAmelCase									=					torch_device == 'cpu'
          UpperCAmelCase									=					True
          UpperCAmelCase									=					[
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]

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




      def     a_      (							self							)						->					Union[str, Any]:
          UpperCAmelCase									=					[
              'decoder_num_inference_steps',
              'super_res_num_inference_steps',
          ]

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




      @skip_mps
      def     a_      (							self							)						->					int:
          return super().test_dict_tuple_outputs_equivalent()




      @skip_mps
      def     a_      (							self							)						->					Union[str, Any]:
          return super().test_save_load_local()




      @skip_mps
      def     a_      (							self							)						->					str:
          return super().test_save_load_optional_components()

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




      def     a_      (							self							)						->					Dict:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def     a_      (							self							)						->					Dict:
          UpperCAmelCase									=					load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'							)
          UpperCAmelCase									=					load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'							)

          UpperCAmelCase									=					UnCLIPImageVariationPipeline.from_pretrained(
              'kakaobrain/karlo-v1-alpha-image-variations'   ,       torch_dtype=torch.floataa							)
          UpperCAmelCase									=					pipeline.to(lowercase_							)
          pipeline.set_progress_bar_config(disable=lowercase_							)

          UpperCAmelCase									=					torch.Generator(device='cpu'							).manual_seed(0							)
          UpperCAmelCase									=					pipeline(
              lowercase_   ,       generator=lowercase_   ,       output_type='np'   ,       )

          UpperCAmelCase									=					output.images[0]

          assert image.shape == (2_5_6, 2_5_6, 3)

          assert_mean_pixel_difference(lowercase_   ,       lowercase_   ,       1_5							)





"""simple docstring"""

from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE_    					=						Lock()




def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Union[str, Any] ,				lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					str ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					Optional[Any] ) ->      Optional[int]:






    """simple docstring"""




    global process_lock

    # we perform n swaps since after n swaps we know we are sorted
    # we *could* stop early if we are sorted already, but it takes as long to
    # find out we are sorted as it does to sort the list with this algorithm
    for i in range(0 ,				10 ):
        if (i + position) % 2 == 0 and r_send is not None:
            # send your value to your right neighbor
            process_lock.acquire()
            r_send[1].send(lowerCAmelCase )
            process_lock.release()

            # receive your right neighbor's value
            process_lock.acquire()
            UpperCAmelCase									=					rr_cv[0].recv()
            process_lock.release()

            # take the lower value since you are on the left
            UpperCAmelCase									=					min(lowerCAmelCase ,				lowerCAmelCase )
        elif (i + position) % 2 != 0 and l_send is not None:
            # send your value to your left neighbor
            process_lock.acquire()
            l_send[1].send(lowerCAmelCase )
            process_lock.release()

            # receive your left neighbor's value
            process_lock.acquire()
            UpperCAmelCase									=					lr_cv[0].recv()
            process_lock.release()

            # take the higher value since you are on the right
            UpperCAmelCase									=					max(lowerCAmelCase ,				lowerCAmelCase )
    # after all swaps are performed, send the values back to main
    result_pipe[1].send(lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[str]:






    """simple docstring"""




    UpperCAmelCase									=					[]
    UpperCAmelCase									=					[]
    # initialize the list of pipes where the values will be retrieved
    for _ in arr:
        result_pipe.append(Pipe() )
    # creates the processes
    # the first and last process only have one neighbor so they are made outside
    # of the loop
    UpperCAmelCase									=					Pipe()
    UpperCAmelCase									=					Pipe()
    process_array_.append(
        Process(
            target=lowerCAmelCase ,				args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]) ,				) )
    UpperCAmelCase									=					temp_rs
    UpperCAmelCase									=					temp_rr

    for i in range(1 ,				len(lowerCAmelCase ) - 1 ):
        UpperCAmelCase									=					Pipe()
        UpperCAmelCase									=					Pipe()
        process_array_.append(
            Process(
                target=lowerCAmelCase ,				args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]) ,				) )
        UpperCAmelCase									=					temp_rs
        UpperCAmelCase									=					temp_rr

    process_array_.append(
        Process(
            target=lowerCAmelCase ,				args=(
                len(lowerCAmelCase ) - 1,
                arr[len(lowerCAmelCase ) - 1],
                temp_ls,
                None,
                temp_lr,
                None,
                result_pipe[len(lowerCAmelCase ) - 1],
            ) ,				) )

    # start the processes
    for p in process_array_:
        p.start()

    # wait for the processes to end and write their values to the list
    for p in range(0 ,				len(lowerCAmelCase ) ):
        UpperCAmelCase									=					result_pipe[p][0].recv()
        process_array_[p].join()
    return arr




def     lowercase__						(					) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase									=					list(range(10 ,				0 ,				-1 ) )
    print('Initial List' )
    print(*lowerCAmelCase )
    UpperCAmelCase									=					odd_even_transposition(lowerCAmelCase )
    print('Sorted List\n' )
    print(*lowerCAmelCase )


if __name__ == "__main__":
   main()



