





"""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 		lowercase ( __lowerCamelCase					,  unittest.TestCase ):
      _SCREAMING_SNAKE_CASE         =				UnCLIPPipeline
      _SCREAMING_SNAKE_CASE         =				TEXT_TO_IMAGE_PARAMS - {
          "negative_prompt",
          "height",
          "width",
          "negative_prompt_embeds",
          "guidance_scale",
          "prompt_embeds",
          "cross_attention_kwargs",
      }
      _SCREAMING_SNAKE_CASE         =				TEXT_TO_IMAGE_BATCH_PARAMS
      _SCREAMING_SNAKE_CASE         =				[
          "generator",
          "return_dict",
          "prior_num_inference_steps",
          "decoder_num_inference_steps",
          "super_res_num_inference_steps",
      ]
      _SCREAMING_SNAKE_CASE         =				False





      @property
      def    _snake_case      (	self			)		->					int:
            return 32





      @property
      def    _snake_case      (	self			)		->					List[str]:
            return 32





      @property
      def    _snake_case      (	self			)		->					List[Any]:
            return self.time_input_dim





      @property
      def    _snake_case      (	self			)		->					Union[str, Any]:
            return self.time_input_dim * 4





      @property
      def    _snake_case      (	self			)		->					List[str]:
            return 100





      @property
      def    _snake_case      (	self			)		->					int:
            lowerCAmelCase  						=	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""			)
            return tokenizer





      @property
      def    _snake_case      (	self			)		->					Optional[Any]:
            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=1_000				,						)
            return CLIPTextModelWithProjection(_UpperCamelCase			)





      @property
      def    _snake_case      (	self			)		->					Optional[int]:
            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(**_UpperCamelCase			)
            return model





      @property
      def    _snake_case      (	self			)		->					Optional[int]:
            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(**_UpperCamelCase			)
            return model





      @property
      def    _snake_case      (	self			)		->					Union[str, Any]:
            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(**_UpperCamelCase			)
            return model





      @property
      def    _snake_case      (	self			)		->					List[str]:
            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    _snake_case      (	self			)		->					Tuple:
            torch.manual_seed(0			)

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





      @property
      def    _snake_case      (	self			)		->					int:
            torch.manual_seed(1			)

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





      def    _snake_case      (	self			)		->					str:
            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=1_000				,						clip_sample_range=5.0				,						)

            lowerCAmelCase  						=	UnCLIPScheduler(
                variance_type="""learned_range"""				,						prediction_type="""epsilon"""				,						num_train_timesteps=1_000				,						)

            lowerCAmelCase  						=	UnCLIPScheduler(
                variance_type="""fixed_small_log"""				,						prediction_type="""epsilon"""				,						num_train_timesteps=1_000				,						)

            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    _snake_case      (	self				,						lowercase				,						lowercase=0			)		->					Optional[int]:





            if str(_UpperCamelCase			).startswith("""mps"""			):
                  lowerCAmelCase  						=	torch.manual_seed(_UpperCamelCase			)
            else:
                  lowerCAmelCase  						=	torch.Generator(device=_UpperCamelCase			).manual_seed(_UpperCamelCase			)
            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    _snake_case      (	self			)		->					Tuple:
            lowerCAmelCase  						=	"""cpu"""

            lowerCAmelCase  						=	self.get_dummy_components()

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

            pipe.set_progress_bar_config(disable=_UpperCamelCase			)

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

            lowerCAmelCase  						=	pipe(
                **self.get_dummy_inputs(_UpperCamelCase			)				,						return_dict=_UpperCamelCase				,						)[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    _snake_case      (	self			)		->					Tuple:
            lowerCAmelCase  						=	torch.device("""cpu"""			)

            class 		lowercase :
                  _SCREAMING_SNAKE_CASE         =				1







            lowerCAmelCase  						=	self.get_dummy_components()

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

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

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

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

            pipe.set_progress_bar_config(disable=_UpperCamelCase			)

            lowerCAmelCase  						=	"""this is a prompt example"""

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

            lowerCAmelCase  						=	tokenizer(
                _UpperCamelCase				,						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=_UpperCamelCase			).manual_seed(0			)
            lowerCAmelCase  						=	pipe(
                generator=_UpperCamelCase				,						prior_num_inference_steps=2				,						decoder_num_inference_steps=2				,						super_res_num_inference_steps=2				,						prior_latents=_UpperCamelCase				,						decoder_latents=_UpperCamelCase				,						super_res_latents=_UpperCamelCase				,						text_model_output=_UpperCamelCase				,						text_attention_mask=_UpperCamelCase				,						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    _snake_case      (	self			)		->					Dict:
            lowerCAmelCase  						=	torch_device == """cpu"""

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





      @skip_mps
      def    _snake_case      (	self			)		->					Union[str, Any]:
            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=_UpperCamelCase				,						relax_max_difference=_UpperCamelCase				,						additional_params_copy_to_batched_inputs=_UpperCamelCase				,						)





      def    _snake_case      (	self			)		->					Any:
            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=_UpperCamelCase				,						additional_params_copy_to_batched_inputs=_UpperCamelCase				,						)
            else:
                  self._test_inference_batch_consistent(
                      additional_params_copy_to_batched_inputs=_UpperCamelCase			)





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





      @skip_mps
      def    _snake_case      (	self			)		->					Any:
            return super().test_save_load_local()





      @skip_mps
      def    _snake_case      (	self			)		->					int:
            return super().test_save_load_optional_components()







@nightly
class 		lowercase ( unittest.TestCase ):





      def    _snake_case      (	self			)		->					str:
            super().tearDown()
            gc.collect()
            torch.cuda.empty_cache()





      def    _snake_case      (	self			)		->					str:
            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=_UpperCamelCase			)

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

            lowerCAmelCase  						=	output.images[0]

            assert image.shape == (256, 256, 3)
            assert np.abs(expected_image - image			).max() < 1e-1







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





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





      def    _snake_case      (	self			)		->					Union[str, Any]:
            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(_UpperCamelCase			)
            pipeline.set_progress_bar_config(disable=_UpperCamelCase			)

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

            lowerCAmelCase  						=	output.images[0]

            assert image.shape == (256, 256, 3)

            assert_mean_pixel_difference(_UpperCamelCase				,						_UpperCamelCase			)





      def    _snake_case      (	self			)		->					Optional[Any]:
            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(_UpperCamelCase			)
            pipe.set_progress_bar_config(disable=_UpperCamelCase			)
            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













"""simple docstring"""





SCREAMING_SNAKE_CASE__ =       [0.33, 0.44, 0.55, 0.44, 0.33]
SCREAMING_SNAKE_CASE__ =       3
SCREAMING_SNAKE_CASE__ =       5

# Buffer Space (with Parameters Space)
SCREAMING_SNAKE_CASE__ =       []
SCREAMING_SNAKE_CASE__ =       []

# Machine Time
SCREAMING_SNAKE_CASE__ =       0


def       UpperCAmelCase__							(  SCREAMING_SNAKE_CASE					:						Tuple      ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      # Choosing Dynamical Systems (All)
      for key, value in enumerate(SCREAMING_SNAKE_CASE      ):
            # Evolution Parameter
            lowerCAmelCase  						=	float(seed / value      )

            # Control Theory: Orbit Change
            lowerCAmelCase  						=	(buffer_space[(key + 1) % m] + e) % 1

            # Control Theory: Trajectory Change
            lowerCAmelCase  						=	(params_space[key] + e) % 1 + 3

            # Modification (Transition Function) - Jumps
            lowerCAmelCase  						=	round(float(r * value * (1 - value)      )							,						10      )
            lowerCAmelCase  						=	r  # Saving to Parameters Space

      # Logistic Map
      assert max(SCREAMING_SNAKE_CASE      ) < 1
      assert max(SCREAMING_SNAKE_CASE      ) < 4

      # Machine Time
      machine_time += 1


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      # PRNG (Xorshift by George Marsaglia)
      def xorshift(SCREAMING_SNAKE_CASE					:						List[str]							,						SCREAMING_SNAKE_CASE					:						Tuple      ):
            x ^= y >> 13
            y ^= x << 17
            x ^= y >> 5
            return x

      # Choosing Dynamical Systems (Increment)
      lowerCAmelCase  						=	machine_time % m

      # Evolution (Time Length)
      for _ in range(0							,						SCREAMING_SNAKE_CASE      ):
            # Variables (Position + Parameters)
            lowerCAmelCase  						=	params_space[key]
            lowerCAmelCase  						=	buffer_space[key]

            # Modification (Transition Function) - Flow
            lowerCAmelCase  						=	round(float(r * value * (1 - value)      )							,						10      )
            lowerCAmelCase  						=	(machine_time * 0.01 + r * 1.01) % 1 + 3

      # Choosing Chaotic Data
      lowerCAmelCase  						=	int(buffer_space[(key + 2) % m] * (10**10)      )
      lowerCAmelCase  						=	int(buffer_space[(key - 2) % m] * (10**10)      )

      # Machine Time
      machine_time += 1

      return xorshift(SCREAMING_SNAKE_CASE							,						SCREAMING_SNAKE_CASE      ) % 0xff_fff_fff


def       UpperCAmelCase__							(  ):







      '''simple docstring'''




      global buffer_space, params_space, machine_time, K, m, t

      lowerCAmelCase  						=	K
      lowerCAmelCase  						=	[0] * m
      lowerCAmelCase  						=	0


if __name__ == "__main__":
       # Initialization
       reset()

       # Pushing Data (Input)
       import random

       SCREAMING_SNAKE_CASE__ =       random.sample(range(0Xf_f_f_f_f_f_f_f), 100)
       for chunk in message:
              push(chunk)

       # for controlling
       SCREAMING_SNAKE_CASE__ =       ""

       # Pulling Data (Output)
       while inp in ("e", "E"):
              print(f'{format(pull(), "#04x")}')
              print(buffer_space)
              print(params_space)
              SCREAMING_SNAKE_CASE__ =       input("(e)exit? ").strip()






