



'''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							):


 """simple docstring"""


 UpperCAmelCase		      =    UnCLIPPipeline
 UpperCAmelCase		      =    TEXT_TO_IMAGE_PARAMS - {
     """negative_prompt""",
     """height""",
     """width""",
     """negative_prompt_embeds""",
     """guidance_scale""",
     """prompt_embeds""",
     """cross_attention_kwargs""",
 }
 UpperCAmelCase		      =    TEXT_TO_IMAGE_BATCH_PARAMS
 UpperCAmelCase		      =    [
     """generator""",
     """return_dict""",
     """prior_num_inference_steps""",
     """decoder_num_inference_steps""",
     """super_res_num_inference_steps""",
 ]
 UpperCAmelCase		      =    False







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







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







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







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







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        return 100







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







 @property
 def _snake_case					(				self				)						->  Optional[Any]:
        torch.manual_seed(0				)
        _UpperCAmelCase    :		int     	=					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-0_5    ,num_attention_heads=4    ,num_hidden_layers=5    ,pad_token_id=1    ,vocab_size=1_000    ,)
        return CLIPTextModelWithProjection(a_				)







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

        _UpperCAmelCase    :		Optional[int]     	=					{
            """num_attention_heads""": 2,
            """attention_head_dim""": 12,
            """embedding_dim""": self.text_embedder_hidden_size,
            """num_layers""": 1,
        }

        _UpperCAmelCase    :		Any     	=					PriorTransformer(**a_				)
        return model







 @property
 def _snake_case					(				self				)						->  Tuple:
        torch.manual_seed(0				)

        _UpperCAmelCase    :		List[str]     	=					{
            """clip_embeddings_dim""": self.text_embedder_hidden_size,
            """time_embed_dim""": self.time_embed_dim,
            """cross_attention_dim""": self.cross_attention_dim,
        }

        _UpperCAmelCase    :		Optional[Any]     	=					UnCLIPTextProjModel(**a_				)
        return model







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

        _UpperCAmelCase    :		Optional[int]     	=					{
            """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""",
        }

        _UpperCAmelCase    :		Union[str, Any]     	=					UNetaDConditionModel(**a_				)
        return model







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

        _UpperCAmelCase    :		Any     	=					UNetaDModel(**self.dummy_super_res_kwargs				)
        return model







 @property
 def _snake_case					(				self				)						->  str:
        # seeded differently to get different unet than `self.dummy_super_res_first`
        torch.manual_seed(1				)

        _UpperCAmelCase    :		Optional[int]     	=					UNetaDModel(**self.dummy_super_res_kwargs				)
        return model







 def _snake_case					(				self				)						->  List[str]:
        _UpperCAmelCase    :		Tuple     	=					self.dummy_prior
        _UpperCAmelCase    :		str     	=					self.dummy_decoder
        _UpperCAmelCase    :		Optional[int]     	=					self.dummy_text_proj
        _UpperCAmelCase    :		Tuple     	=					self.dummy_text_encoder
        _UpperCAmelCase    :		Any     	=					self.dummy_tokenizer
        _UpperCAmelCase    :		List[Any]     	=					self.dummy_super_res_first
        _UpperCAmelCase    :		Tuple     	=					self.dummy_super_res_last

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPScheduler(
            variance_type="""fixed_small_log"""    ,prediction_type="""sample"""    ,num_train_timesteps=1_000    ,clip_sample_range=5.0    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPScheduler(
            variance_type="""learned_range"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_000    ,)

        _UpperCAmelCase    :		int     	=					UnCLIPScheduler(
            variance_type="""fixed_small_log"""    ,prediction_type="""epsilon"""    ,num_train_timesteps=1_000    ,)

        _UpperCAmelCase    :		List[Any]     	=					{
            """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    ,a_    ,a_=0				)						->  List[Any]:
        if str(a_				).startswith("""mps"""				):
               _UpperCAmelCase    :		int     	=					torch.manual_seed(a_				)
        else:
               _UpperCAmelCase    :		List[str]     	=					torch.Generator(device=a_				).manual_seed(a_				)
        _UpperCAmelCase    :		Any     	=					{
            """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				)						->  List[str]:
        _UpperCAmelCase    :		Optional[int]     	=					"""cpu"""

        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_components()

        _UpperCAmelCase    :		List[Any]     	=					self.pipeline_class(**a_				)
        _UpperCAmelCase    :		Union[str, Any]     	=					pipe.to(a_				)

        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					pipe(**self.get_dummy_inputs(a_				)				)
        _UpperCAmelCase    :		List[Any]     	=					output.images

        _UpperCAmelCase    :		int     	=					pipe(
            **self.get_dummy_inputs(a_				)    ,return_dict=a_    ,)[0]

        _UpperCAmelCase    :		List[Any]     	=					image[0, -3:, -3:, -1]
        _UpperCAmelCase    :		Optional[int]     	=					image_from_tuple[0, -3:, -3:, -1]

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

        _UpperCAmelCase    :		Any     	=					np.array(
            [
                0.9997,
                0.9988,
                0.0028,
                0.9997,
                0.9984,
                0.9965,
                0.0029,
                0.9986,
                0.0025,
            ]				)

        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				)						->  int:
        _UpperCAmelCase    :		Optional[Any]     	=					torch.device("""cpu"""				)



        class 				lowercase			:


         """simple docstring"""


         UpperCAmelCase		      =    1







        _UpperCAmelCase    :		List[Any]     	=					self.get_dummy_components()

        _UpperCAmelCase    :		Tuple     	=					self.pipeline_class(**a_				)
        _UpperCAmelCase    :		Any     	=					pipe.to(a_				)

        _UpperCAmelCase    :		int     	=					components["""prior"""]
        _UpperCAmelCase    :		Optional[Any]     	=					components["""decoder"""]
        _UpperCAmelCase    :		str     	=					components["""super_res_first"""]
        _UpperCAmelCase    :		Any     	=					components["""tokenizer"""]
        _UpperCAmelCase    :		Union[str, Any]     	=					components["""text_encoder"""]

        _UpperCAmelCase    :		Tuple     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		Optional[Any]     	=					prior.dtype
        _UpperCAmelCase    :		Tuple     	=					1

        _UpperCAmelCase    :		str     	=					(batch_size, prior.config.embedding_dim)
        _UpperCAmelCase    :		Union[str, Any]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)
        _UpperCAmelCase    :		List[str]     	=					(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
        _UpperCAmelCase    :		List[str]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)

        _UpperCAmelCase    :		List[Any]     	=					(
            batch_size,
            super_res_first.config.in_channels // 2,
            super_res_first.config.sample_size,
            super_res_first.config.sample_size,
        )
        _UpperCAmelCase    :		List[Any]     	=					pipe.prepare_latents(
            a_    ,dtype=a_    ,device=a_    ,generator=a_    ,latents=a_    ,scheduler=DummyScheduler()				)

        pipe.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[Any]     	=					"""this is a prompt example"""

        _UpperCAmelCase    :		Optional[Any]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					pipe(
            [prompt]    ,generator=a_    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=a_    ,decoder_latents=a_    ,super_res_latents=a_    ,output_type="""np"""    ,)
        _UpperCAmelCase    :		List[str]     	=					output.images

        _UpperCAmelCase    :		Dict     	=					tokenizer(
            a_    ,padding="""max_length"""    ,max_length=tokenizer.model_max_length    ,return_tensors="""pt"""    ,)
        _UpperCAmelCase    :		Optional[int]     	=					text_encoder(text_inputs.input_ids				)
        _UpperCAmelCase    :		int     	=					text_inputs.attention_mask

        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device=a_				).manual_seed(0				)
        _UpperCAmelCase    :		str     	=					pipe(
            generator=a_    ,prior_num_inference_steps=2    ,decoder_num_inference_steps=2    ,super_res_num_inference_steps=2    ,prior_latents=a_    ,decoder_latents=a_    ,super_res_latents=a_    ,text_model_output=a_    ,text_attention_mask=a_    ,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				)						->  str:
        _UpperCAmelCase    :		str     	=					torch_device == """cpu"""

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







 @skip_mps
 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Any     	=					torch_device == """cpu"""
        _UpperCAmelCase    :		Any     	=					True
        _UpperCAmelCase    :		Any     	=					[
            """prior_num_inference_steps""",
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

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







 def _snake_case					(				self				)						->  Dict:
        _UpperCAmelCase    :		Optional[Any]     	=					[
            """prior_num_inference_steps""",
            """decoder_num_inference_steps""",
            """super_res_num_inference_steps""",
        ]

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







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







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







 @skip_mps
 def _snake_case					(				self				)						->  Optional[Any]:
        return super().test_save_load_optional_components()







@nightly
class 				lowercase			(    unittest.TestCase							):


 """simple docstring"""


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







 def _snake_case					(				self				)						->  Tuple:
        _UpperCAmelCase    :		List[str]     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/unclip/karlo_v1_alpha_horse_cpu.npy"""				)

        _UpperCAmelCase    :		Tuple     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""				)
        pipeline.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Any     	=					torch.manual_seed(0				)
        _UpperCAmelCase    :		List[str]     	=					pipeline(
            """horse"""    ,num_images_per_prompt=1    ,generator=a_    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Union[str, Any]     	=					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							):


 """simple docstring"""


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







 def _snake_case					(				self				)						->  int:
        _UpperCAmelCase    :		List[Any]     	=					load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/unclip/karlo_v1_alpha_horse_fp16.npy"""				)

        _UpperCAmelCase    :		List[str]     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa				)
        _UpperCAmelCase    :		str     	=					pipeline.to(a_				)
        pipeline.set_progress_bar_config(disable=a_				)

        _UpperCAmelCase    :		Optional[int]     	=					torch.Generator(device="""cpu"""				).manual_seed(0				)
        _UpperCAmelCase    :		int     	=					pipeline(
            """horse"""    ,generator=a_    ,output_type="""np"""    ,)

        _UpperCAmelCase    :		Optional[Any]     	=					output.images[0]

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

        assert_mean_pixel_difference(a_    ,a_				)







 def _snake_case					(				self				)						->  Optional[Any]:
        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        _UpperCAmelCase    :		Union[str, Any]     	=					UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""    ,torch_dtype=torch.floataa				)
        _UpperCAmelCase    :		str     	=					pipe.to(a_				)
        pipe.set_progress_bar_config(disable=a_				)
        pipe.enable_attention_slicing()
        pipe.enable_sequential_cpu_offload()

        _UpperCAmelCase    :		Union[str, Any]     	=					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"""    ,)

        _UpperCAmelCase    :		Optional[int]     	=					torch.cuda.max_memory_allocated()
        # make sure that less than 7 GB is allocated
        assert mem_bytes < 7 * 10**9








'''simple docstring'''






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





       '''simple docstring'''


       if n < 0:
              return 0
       # table[i] represents the number of ways to get to amount i
       _UpperCAmelCase    :		Union[str, Any]     	=					[0] * (n + 1)

       # There is exactly 1 way to get to zero(You pick no coins).
       _UpperCAmelCase    :		Optional[int]     	=					1

       # Pick all coins one by one and update table[] values
       # after the index greater than or equal to the value of the
       # picked coin
       for coin_val in s:
              for j in range(lowerCAmelCase_			,  n + 1						):
                     table[j] += table[j - coin_val]

       return table[n]


if __name__ == "__main__":
   import doctest

   doctest.testmod()



