






'''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  _SCREAMING_SNAKE_CASE				(		SCREAMING_SNAKE_CASE							,unittest.TestCase						):


			'''simple docstring'''





			__a				:				int         =       UnCLIPPipeline
			__a				:				str         =       TEXT_TO_IMAGE_PARAMS - {
			    "negative_prompt",
			    "height",
			    "width",
			    "negative_prompt_embeds",
			    "guidance_scale",
			    "prompt_embeds",
			    "cross_attention_kwargs",
			}
			__a				:				Dict         =       TEXT_TO_IMAGE_BATCH_PARAMS
			__a				:				Dict         =       [
			    "generator",
			    "return_dict",
			    "prior_num_inference_steps",
			    "decoder_num_inference_steps",
			    "super_res_num_inference_steps",
			]
			__a				:				Any         =       False






			@property
			def 					A   ( self				:    Optional[Any]      )				->     List[Any]:

									'''simple docstring'''







									return 3_2






			@property
			def 					A   ( self				:    int      )				->     str:

									'''simple docstring'''







									return 3_2






			@property
			def 					A   ( self				:    int      )				->     List[Any]:

									'''simple docstring'''







									return self.time_input_dim






			@property
			def 					A   ( self				:    Optional[Any]      )				->     Optional[int]:

									'''simple docstring'''







									return self.time_input_dim * 4






			@property
			def 					A   ( self				:    int      )				->     List[str]:

									'''simple docstring'''







									return 1_0_0






			@property
			def 					A   ( self				:    Tuple      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )
									return tokenizer






			@property
			def 					A   ( self				:    Dict      )				->     Union[str, Any]:

									'''simple docstring'''







									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				:    int      )				->     Dict:

									'''simple docstring'''







									torch.manual_seed(0      )

									UpperCamelCase__       =			{
									    """num_attention_heads""": 2,
									    """attention_head_dim""": 1_2,
									    """embedding_dim""": self.text_embedder_hidden_size,
									    """num_layers""": 1,
									}

									UpperCamelCase__       =			PriorTransformer(**lowercase      )
									return model






			@property
			def 					A   ( self				:    Dict      )				->     Union[str, Any]:

									'''simple docstring'''







									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				:    Optional[Any]      )				->     Any:

									'''simple docstring'''







									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				:    List[Any]      )				->     Optional[Any]:

									'''simple docstring'''







									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      )				->     Optional[int]:

									'''simple docstring'''







									torch.manual_seed(0      )

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






			@property
			def 					A   ( self				:    Dict      )				->     Optional[Any]:

									'''simple docstring'''







									torch.manual_seed(1      )

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






			def 					A   ( self				:    Optional[Any]      )				->     Any:

									'''simple docstring'''







									UpperCamelCase__       =			self.dummy_prior
									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="""fixed_small_log"""			,   prediction_type="""sample"""			,   num_train_timesteps=1_0_0_0			,   clip_sample_range=5.0			,   )

									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__       =			{
									    """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				:    List[str]			,   lowercase				:    Dict			,   lowercase				:    Dict=0      )				->     Dict:

									'''simple docstring'''







									if str(lowercase      ).startswith("""mps"""      ):
															UpperCamelCase__       =			torch.manual_seed(lowercase      )
									else:
															UpperCamelCase__       =			torch.Generator(device=lowercase      ).manual_seed(lowercase      )
									UpperCamelCase__       =			{
									    """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				:    Optional[int]      )				->     Optional[Any]:

									'''simple docstring'''







									UpperCamelCase__       =			"""cpu"""

									UpperCamelCase__       =			self.get_dummy_components()

									UpperCamelCase__       =			self.pipeline_class(**lowercase      )
									UpperCamelCase__       =			pipe.to(lowercase      )

									pipe.set_progress_bar_config(disable=lowercase      )

									UpperCamelCase__       =			pipe(**self.get_dummy_inputs(lowercase      )      )
									UpperCamelCase__       =			output.images

									UpperCamelCase__       =			pipe(
									    **self.get_dummy_inputs(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,
									        0.9_9_8_8,
									        0.0_0_2_8,
									        0.9_9_9_7,
									        0.9_9_8_4,
									        0.9_9_6_5,
									        0.0_0_2_9,
									        0.9_9_8_6,
									        0.0_0_2_5,
									    ]      )

									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				:    int      )				->     Dict:

									'''simple docstring'''







									UpperCamelCase__       =			torch.device("""cpu"""      )
									class  _SCREAMING_SNAKE_CASE				:


												'''simple docstring'''





												__a				:				Dict         =       1
									UpperCamelCase__       =			self.get_dummy_components()

									UpperCamelCase__       =			self.pipeline_class(**lowercase      )
									UpperCamelCase__       =			pipe.to(lowercase      )

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

									UpperCamelCase__       =			torch.Generator(device=lowercase      ).manual_seed(0      )
									UpperCamelCase__       =			prior.dtype
									UpperCamelCase__       =			1

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

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

									pipe.set_progress_bar_config(disable=lowercase      )

									UpperCamelCase__       =			"""this is a prompt example"""

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

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

									UpperCamelCase__       =			torch.Generator(device=lowercase      ).manual_seed(0      )
									UpperCamelCase__       =			pipe(
									    generator=lowercase			,   prior_num_inference_steps=2			,   decoder_num_inference_steps=2			,   super_res_num_inference_steps=2			,   prior_latents=lowercase			,   decoder_latents=lowercase			,   super_res_latents=lowercase			,   text_model_output=lowercase			,   text_attention_mask=lowercase			,   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[Any]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			torch_device == """cpu"""

									self._test_attention_slicing_forward_pass(test_max_difference=lowercase			,   expected_max_diff=0.0_1      )






			@skip_mps
			def 					A   ( self				:    Dict      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			torch_device == """cpu"""
									UpperCamelCase__       =			True
									UpperCamelCase__       =			[
									    """prior_num_inference_steps""",
									    """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				:    Optional[int]      )				->     str:

									'''simple docstring'''







									UpperCamelCase__       =			[
									    """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__       =			[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				:    Any      )				->     Dict:

									'''simple docstring'''







									return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def 					A   ( self				:    List[Any]      )				->     Optional[Any]:

									'''simple docstring'''







									return super().test_save_load_local()







			@skip_mps
			def 					A   ( self				:    List[str]      )				->     Optional[Any]:

									'''simple docstring'''







									return super().test_save_load_optional_components()
@nightly
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def 					A   ( self				:    Tuple      )				->     Dict:

									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







			def 					A   ( self				:    Optional[Any]      )				->     Optional[int]:

									'''simple docstring'''







									UpperCamelCase__       =			load_numpy(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/unclip/karlo_v1_alpha_horse_cpu.npy"""      )

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

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

									UpperCamelCase__       =			output.images[0]

									assert image.shape == (2_5_6, 2_5_6, 3)
									assert np.abs(expected_image - image      ).max() < 1e-1
@slow
@require_torch_gpu
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





			def 					A   ( self				:    Optional[int]      )				->     List[str]:

									'''simple docstring'''







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()






			def 					A   ( self				:    List[Any]      )				->     Tuple:

									'''simple docstring'''







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

									UpperCamelCase__       =			UnCLIPPipeline.from_pretrained("""kakaobrain/karlo-v1-alpha"""			,   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(
									    """horse"""			,   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      )







			def 					A   ( self				:    Union[str, Any]      )				->     Optional[int]:

									'''simple docstring'''







									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

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

									UpperCamelCase__       =			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__       =			torch.cuda.max_memory_allocated()
									# make sure that less than 7 GB is allocated
									assert mem_bytes < 7 * 1_0**9








'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


lowerCamelCase_					:			str     						=				{'''configuration_hubert''': ['''HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''HubertConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Union[str, Any]     						=				[
					    '''HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''HubertForCTC''',
					    '''HubertForSequenceClassification''',
					    '''HubertModel''',
					    '''HubertPreTrainedModel''',
					]


try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase_					:			Optional[Any]     						=				[
					    '''TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFHubertForCTC''',
					    '''TFHubertModel''',
					    '''TFHubertPreTrainedModel''',
					]

if TYPE_CHECKING:
					from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_hubert import (
										    HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    HubertForCTC,
										    HubertForSequenceClassification,
										    HubertModel,
										    HubertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_hubert import (
										    TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFHubertForCTC,
										    TFHubertModel,
										    TFHubertPreTrainedModel,
										)


else:
					import sys

					lowerCamelCase_					:			Dict     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
