





'''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 				__UpperCAmelCase	(							A__		,       unittest.TestCase    ):







		'''simple docstring'''







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







		@property
		def 							A  (self    :   Union[str, Any]   ):
			return 32







		@property
		def 							A  (self    :   Optional[Any]   ):
			return 32







		@property
		def 							A  (self    :   List[Any]   ):
			return self.time_input_dim







		@property
		def 							A  (self    :   List[Any]   ):
			return self.time_input_dim * 4







		@property
		def 							A  (self    :   Optional[Any]   ):
			return 100







		@property
		def 							A  (self    :   Optional[Any]   ):
			A					=   CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
			return tokenizer







		@property
		def 							A  (self    :   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=1000   ,   )
			return CLIPTextModelWithProjection(_lowerCAmelCase   )







		@property
		def 							A  (self    :   str   ):
			torch.manual_seed(0   )

			A					=   {
			    """num_attention_heads""": 2,
			    """attention_head_dim""": 12,
			    """embedding_dim""": self.text_embedder_hidden_size,
			    """num_layers""": 1,
			}

			A					=   PriorTransformer(**_lowerCAmelCase   )
			return model







		@property
		def 							A  (self    :   List[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(**_lowerCAmelCase   )
			return model







		@property
		def 							A  (self    :   List[Any]   ):
			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(**_lowerCAmelCase   )
			return model







		@property
		def 							A  (self    :   Optional[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 							A  (self    :   Dict   ):
			torch.manual_seed(0   )

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







		@property
		def 							A  (self    :   List[str]   ):
			# 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 							A  (self    :   int   ):
			A					=   self.dummy_prior
			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="""fixed_small_log"""   ,   prediction_type="""sample"""   ,   num_train_timesteps=1000   ,   clip_sample_range=5.0   ,   )

			A					=   UnCLIPScheduler(
			    variance_type="""learned_range"""   ,   prediction_type="""epsilon"""   ,   num_train_timesteps=1000   ,   )

			A					=   UnCLIPScheduler(
			    variance_type="""fixed_small_log"""   ,   prediction_type="""epsilon"""   ,   num_train_timesteps=1000   ,   )

			A					=   {
			    """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 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   Tuple   ,   _lowerCAmelCase    :   int=0   ):
			if str(_lowerCAmelCase   ).startswith("""mps"""   ):
				A					=   torch.manual_seed(_lowerCAmelCase   )
			else:
				A					=   torch.Generator(device=_lowerCAmelCase   ).manual_seed(_lowerCAmelCase   )
			A					=   {
			    """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 							A  (self    :   Dict   ):
			A					=   """cpu"""

			A					=   self.get_dummy_components()

			A					=   self.pipeline_class(**_lowerCAmelCase   )
			A					=   pipe.to(_lowerCAmelCase   )

			pipe.set_progress_bar_config(disable=_lowerCAmelCase   )

			A					=   pipe(**self.get_dummy_inputs(_lowerCAmelCase   )   )
			A					=   output.images

			A					=   pipe(
			    **self.get_dummy_inputs(_lowerCAmelCase   )   ,   return_dict=_lowerCAmelCase   ,   )[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_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 							A  (self    :   Optional[int]   ):
			A					=   torch.device("""cpu"""   )



			class 				__UpperCAmelCase	:







					'''simple docstring'''







					__lowerCAmelCase								=     1






			A					=   self.get_dummy_components()

			A					=   self.pipeline_class(**_lowerCAmelCase   )
			A					=   pipe.to(_lowerCAmelCase   )

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

			A					=   torch.Generator(device=_lowerCAmelCase   ).manual_seed(0   )
			A					=   prior.dtype
			A					=   1

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

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

			pipe.set_progress_bar_config(disable=_lowerCAmelCase   )

			A					=   """this is a prompt example"""

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

			A					=   tokenizer(
			    _lowerCAmelCase   ,   padding="""max_length"""   ,   max_length=tokenizer.model_max_length   ,   return_tensors="""pt"""   ,   )
			A					=   text_encoder(text_inputs.input_ids   )
			A					=   text_inputs.attention_mask

			A					=   torch.Generator(device=_lowerCAmelCase   ).manual_seed(0   )
			A					=   pipe(
			    generator=_lowerCAmelCase   ,   prior_num_inference_steps=2   ,   decoder_num_inference_steps=2   ,   super_res_num_inference_steps=2   ,   prior_latents=_lowerCAmelCase   ,   decoder_latents=_lowerCAmelCase   ,   super_res_latents=_lowerCAmelCase   ,   text_model_output=_lowerCAmelCase   ,   text_attention_mask=_lowerCAmelCase   ,   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 							A  (self    :   Optional[int]   ):
			A					=   torch_device == """cpu"""

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







		@skip_mps
		def 							A  (self    :   List[str]   ):
			A					=   torch_device == """cpu"""
			A					=   True
			A					=   [
			    """prior_num_inference_steps""",
			    """decoder_num_inference_steps""",
			    """super_res_num_inference_steps""",
			]

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







		def 							A  (self    :   Tuple   ):
			A					=   [
			    """prior_num_inference_steps""",
			    """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=_lowerCAmelCase   ,   additional_params_copy_to_batched_inputs=_lowerCAmelCase   ,   )
			else:
				self._test_inference_batch_consistent(
				    additional_params_copy_to_batched_inputs=_lowerCAmelCase   )







		@skip_mps
		def 							A  (self    :   List[Any]   ):
			return super().test_dict_tuple_outputs_equivalent()







		@skip_mps
		def 							A  (self    :   Any   ):
			return super().test_save_load_local()







		@skip_mps
		def 							A  (self    :   Any   ):
			return super().test_save_load_optional_components()






@nightly
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







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







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

			A					=   UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""   )
			pipeline.set_progress_bar_config(disable=_lowerCAmelCase   )

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

			A					=   output.images[0]

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






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







		'''simple docstring'''







		def 							A  (self    :   int   ):
			# clean up the VRAM after each test
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()







		def 							A  (self    :   Dict   ):
			A					=   load_numpy(
			    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
			    """/unclip/karlo_v1_alpha_horse_fp16.npy"""   )

			A					=   UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""   ,   torch_dtype=torch.floataa   )
			A					=   pipeline.to(_lowerCAmelCase   )
			pipeline.set_progress_bar_config(disable=_lowerCAmelCase   )

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

			A					=   output.images[0]

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

			assert_mean_pixel_difference(_lowerCAmelCase   ,   _lowerCAmelCase   )







		def 							A  (self    :   Any   ):
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			A					=   UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""   ,   torch_dtype=torch.floataa   )
			A					=   pipe.to(_lowerCAmelCase   )
			pipe.set_progress_bar_config(disable=_lowerCAmelCase   )
			pipe.enable_attention_slicing()
			pipe.enable_sequential_cpu_offload()

			A					=   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"""   ,   )

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



'''simple docstring'''







from ..utils import DummyObject, requires_backends







class 		snake_case_       (						metaclass=A__						):



   """simple docstring"""





   __lowerCAmelCase	:							List[str]								=['''torch''', '''librosa''']







   def __init__( self			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(self			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







class 		snake_case_       (						metaclass=A__						):



   """simple docstring"""





   __lowerCAmelCase	:							Union[str, Any]								=['''torch''', '''librosa''']







   def __init__( self			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(self			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])







   @classmethod
   def     __UpperCAmelCase      ( cls			,   *UpperCamelCase			,   **UpperCamelCase):
          requires_backends(cls			,   ["torch", "librosa"])

