





'''simple docstring'''

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	(       UpperCamelCase__			,       unittest.TestCase				):
 __lowercase   		=				UnCLIPImageVariationPipeline
 __lowercase   		=				IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
 __lowercase   		=				IMAGE_VARIATION_BATCH_PARAMS

 __lowercase   		=				[
     """generator""",
     """return_dict""",
     """decoder_num_inference_steps""",
     """super_res_num_inference_steps""",
 ]
 __lowercase   		=				False
 @property
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     return 32
 @property
 def      UpperCAmelCase_							(				self      :str							)->							Dict:
     return self.time_input_dim
 @property
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Optional[int]:
     return self.time_input_dim * 4
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Any:
     return 1_00
 @property
 def      UpperCAmelCase_							(				self      :int							)->							Union[str, Any]:
     A__				=			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"							)
     return tokenizer
 @property
 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Union[str, Any]:
     torch.manual_seed(0							)
     A__				=			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(lowercase_							)
 @property
 def      UpperCAmelCase_							(				self      :Tuple							)->							Optional[int]:
     torch.manual_seed(0							)
     A__				=			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=32						,       intermediate_size=37						,       patch_size=1						,       )
     return CLIPVisionModelWithProjection(lowercase_							)
 @property
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							Union[str, Any]:
     torch.manual_seed(0							)

     A__				=			{
         "clip_embeddings_dim": self.text_embedder_hidden_size,
         "time_embed_dim": self.time_embed_dim,
         "cross_attention_dim": self.cross_attention_dim,
     }

     A__				=			UnCLIPTextProjModel(**lowercase_							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Any							)->							str:
     torch.manual_seed(0							)

     A__				=			{
         "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",
     }

     A__				=			UNetaDConditionModel(**lowercase_							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							int:
     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      :Tuple							)->							List[str]:
     torch.manual_seed(0							)

     A__				=			UNetaDModel(**self.dummy_super_res_kwargs							)
     return model
 @property
 def      UpperCAmelCase_							(				self      :Dict							)->							Tuple:
     # seeded differently to get different unet than `self.dummy_super_res_first`
     torch.manual_seed(1							)

     A__				=			UNetaDModel(**self.dummy_super_res_kwargs							)
     return model
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Union[str, Any]:
     A__				=			self.dummy_decoder
     A__				=			self.dummy_text_proj
     A__				=			self.dummy_text_encoder
     A__				=			self.dummy_tokenizer
     A__				=			self.dummy_super_res_first
     A__				=			self.dummy_super_res_last

     A__				=			UnCLIPScheduler(
         variance_type="learned_range"						,       prediction_type="epsilon"						,       num_train_timesteps=10_00						,       )

     A__				=			UnCLIPScheduler(
         variance_type="fixed_small_log"						,       prediction_type="epsilon"						,       num_train_timesteps=10_00						,       )

     A__				=			CLIPImageProcessor(crop_size=32						,       size=32							)

     A__				=			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      UpperCAmelCase_							(				self      :Tuple						,       lowercase_      :List[str]						,       lowercase_      :List[Any]=0						,       lowercase_      :List[str]=True							)->							List[str]:
     A__				=			floats_tensor((1, 3, 32, 32)						,       rng=random.Random(lowercase_							)							).to(lowercase_							)
     if str(lowercase_							).startswith("mps"							):
         A__				=			torch.manual_seed(lowercase_							)
     else:
         A__				=			torch.Generator(device=lowercase_							).manual_seed(lowercase_							)

     if pil_image:
         A__				=			input_image * 0.5 + 0.5
         A__				=			input_image.clamp(0						,       1							)
         A__				=			input_image.cpu().permute(0						,       2						,       3						,       1							).float().numpy()
         A__				=			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      UpperCAmelCase_							(				self      :Optional[int]							)->							int:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=lowercase_							)

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

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

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

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

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

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

     A__				=			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      UpperCAmelCase_							(				self      :str							)->							Optional[Any]:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=lowercase_							)

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

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

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

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

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

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

     A__				=			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      UpperCAmelCase_							(				self      :Optional[Any]							)->							Union[str, Any]:
     A__				=			"cpu"

     A__				=			self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=lowercase_							)

     A__				=			self.get_dummy_inputs(lowercase_						,       pil_image=lowercase_							)
     A__				=			[
         pipeline_inputs["image"],
         pipeline_inputs["image"],
     ]

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

     A__				=			self.get_dummy_inputs(lowercase_						,       pil_image=lowercase_							)
     A__				=			[
         tuple_pipeline_inputs["image"],
         tuple_pipeline_inputs["image"],
     ]

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

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

     assert image.shape == (2, 64, 64, 3)

     A__				=			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      UpperCAmelCase_							(				self      :Optional[Any]							)->							str:
     A__				=			torch.device("cpu"							)






     class    UpperCAmelCase	:
      __lowercase   		=				1

     A__				=			self.get_dummy_components()

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

     pipe.set_progress_bar_config(disable=lowercase_							)

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

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

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

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

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

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

     A__				=			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      UpperCAmelCase_							(				self      :Dict							)->							Tuple:
     A__				=			torch_device == "cpu"

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

     self._test_attention_slicing_forward_pass(
         test_max_difference=lowercase_						,       expected_max_diff=lowercase_							)
 @skip_mps
 def      UpperCAmelCase_							(				self      :str							)->							str:
     A__				=			torch_device == "cpu"
     A__				=			True
     A__				=			[
         "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      UpperCAmelCase_							(				self      :int							)->							Union[str, Any]:
     A__				=			[
         "decoder_num_inference_steps",
         "super_res_num_inference_steps",
     ]

     if torch_device == "mps":
         # TODO: MPS errors with larger batch sizes
         A__				=			[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      UpperCAmelCase_							(				self      :Any							)->							Dict:
     return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def      UpperCAmelCase_							(				self      :List[Any]							)->							int:
     return super().test_save_load_local()



 @skip_mps
 def      UpperCAmelCase_							(				self      :Tuple							)->							List[Any]:
     return super().test_save_load_optional_components()






@slow
@require_torch_gpu
class    UpperCAmelCase	(       unittest.TestCase				):
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Union[str, Any]:
     # clean up the VRAM after each test
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()



 def      UpperCAmelCase_							(				self      :List[str]							)->							Any:
     A__				=			load_image(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png"							)
     A__				=			load_numpy(
         "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
         "/unclip/karlo_v1_alpha_cat_variation_fp16.npy"							)

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

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

     A__				=			output.images[0]

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

     assert_mean_pixel_difference(lowercase_						,       lowercase_						,       15							)










'''simple docstring'''

from __future__ import annotations

import time

import numpy as np

__lowerCAmelCase							:	Optional[Any]			=[8, 5, 9, 7]
__lowerCAmelCase							:	Optional[int]			=[
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__lowerCAmelCase							:	List[Any]			=[
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]






class    UpperCAmelCase	:
 def __init__(				self      :Optional[int]						,       lowercase_      :list[int]						,       lowercase_      :list[list[int]]						,       lowercase_      :list[list[int]]						,       )->							None:
     A__				=			claim_vector
     A__				=			allocated_resources_table
     A__				=			maximum_claim_table
 def      UpperCAmelCase_							(				self      :Union[str, Any]							)->							list[int]:
     return [
         sum(p_item[i] for p_item in self.__allocated_resources_table							)
         for i in range(len(self.__allocated_resources_table[0]							)							)
     ]
 def      UpperCAmelCase_							(				self      :Tuple							)->							list[int]:
     return np.array(self.__claim_vector							) - np.array(
         self.__processes_resource_summation()							)
 def      UpperCAmelCase_							(				self      :Dict							)->							list[list[int]]:
     return [
         list(np.array(self.__maximum_claim_table[i]							) - np.array(lowercase_							)							)
         for i, allocated_resource in enumerate(self.__allocated_resources_table							)
     ]
 def      UpperCAmelCase_							(				self      :Any							)->							dict[int, list[int]]:
     return {self.__need().index(lowercase_							): i for i in self.__need()}
 def      UpperCAmelCase_							(				self      :Tuple						,       **lowercase_      :List[str]							)->							None:
     A__				=			self.__need()
     A__				=			self.__allocated_resources_table
     A__				=			self.__available_resources()
     A__				=			self.__need_index_manager()
     for kw, val in kwargs.items():
         if kw and val is True:
             self.__pretty_data()
     print("_" * 50 + "\n"							)
     while need_list:
         A__				=			False
         for each_need in need_list:
             A__				=			True
             for index, need in enumerate(lowercase_							):
                 if need > available_resources[index]:
                     A__				=			False
                     break
             if execution:
                 A__				=			True
                 # get the original index of the process from ind_ctrl db
                 for original_need_index, need_clone in need_index_manager.items():
                     if each_need == need_clone:
                         A__				=			original_need_index
                 print(F"Process {process_number + 1} is executing."							)
                 # remove the process run from stack
                 need_list.remove(lowercase_							)
                 # update available/freed resources stack
                 A__				=			np.array(lowercase_							) + np.array(
                     alloc_resources_table[process_number]							)
                 print(
                     "Updated available resource stack for processes: "
                     + " ".join([str(lowercase_							) for x in available_resources]							)							)
                 break
         if safe:
             print("The process is in a safe state.\n"							)
         else:
             print("System in unsafe state. Aborting...\n"							)
             break



 def      UpperCAmelCase_							(				self      :Optional[Any]							)->							Tuple:
     print(" " * 9 + "Allocated Resource Table"							)
     for item in self.__allocated_resources_table:
         print(
             F"P{self.__allocated_resources_table.index(lowercase_							) + 1}"
             + " ".join(F"{it:>8}" for it in item							)
             + "\n"							)
     print(" " * 9 + "System Resource Table"							)
     for item in self.__maximum_claim_table:
         print(
             F"P{self.__maximum_claim_table.index(lowercase_							) + 1}"
             + " ".join(F"{it:>8}" for it in item							)
             + "\n"							)
     print(
         "Current Usage by Active Processes: "
         + " ".join(str(lowercase_							) for x in self.__claim_vector							)							)
     print(
         "Initial Available Resources:       "
         + " ".join(str(lowercase_							) for x in self.__available_resources()							)							)
     time.sleep(1							)


if __name__ == "__main__":
  import doctest

  doctest.testmod()



