




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(		lowercase__       ,	unittest.TestCase ):





     """simple docstring"""



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

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			str:






         """simple docstring"""
         return 32

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






         """simple docstring"""
         return 32

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       int    )			->			Any:






         """simple docstring"""
         return self.time_input_dim

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Any:






         """simple docstring"""
         return self.time_input_dim * 4

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			int:






         """simple docstring"""
         return 1_00

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			Dict:






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

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			int:






         """simple docstring"""
         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(A    )

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






         """simple docstring"""
         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(**A    )
         return model

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Any    )			->			Any:






         """simple docstring"""
         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(**A    )
         return model

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			List[Any]:






         """simple docstring"""
         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(**A    )
         return model

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






         """simple docstring"""
         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 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			str:






         """simple docstring"""
         torch.manual_seed(0    )

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

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






         """simple docstring"""
         # 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 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			Dict:






         """simple docstring"""
         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=10_00						,				clip_sample_range=5.0						,				)

         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__      =       {
             """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 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]						,				A :       Union[str, Any]						,				A :       List[Any]=0    )			->			int:






         """simple docstring"""
         if str(A    ).startswith("""mps"""    ):
             a__      =       torch.manual_seed(A    )
         else:
             a__      =       torch.Generator(device=A    ).manual_seed(A    )
         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 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			str:






         """simple docstring"""
         a__      =       """cpu"""

         a__      =       self.get_dummy_components()

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

         pipe.set_progress_bar_config(disable=A    )

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

         a__      =       pipe(
             **self.get_dummy_inputs(A    )						,				return_dict=A						,				)[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,
                 0.9_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 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			Optional[int]:






         """simple docstring"""
         a__      =       torch.device("""cpu"""    )







         class 		__UpperCAmelCase:





              """simple docstring"""



              __UpperCAmelCase	: Optional[Any]						=    1






         a__      =       self.get_dummy_components()

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

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

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

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

         pipe.set_progress_bar_config(disable=A    )

         a__      =       """this is a prompt example"""

         a__      =       torch.Generator(device=A    ).manual_seed(0    )
         a__      =       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"""						,				)
         a__      =       output.images

         a__      =       tokenizer(
             A						,				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=A    ).manual_seed(0    )
         a__      =       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 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			str:






         """simple docstring"""
         a__      =       torch_device == """cpu"""

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

     @skip_mps
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Union[str, Any]:






         """simple docstring"""
         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=A						,				relax_max_difference=A						,				additional_params_copy_to_batched_inputs=A						,				)

     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[int]    )			->			Tuple:






         """simple docstring"""
         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=A						,				additional_params_copy_to_batched_inputs=A						,				)
         else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=A    )

     @skip_mps
     def 				__SCREAMING_SNAKE_CASE		(      self :       Union[str, Any]    )			->			int:






         """simple docstring"""
         return super().test_dict_tuple_outputs_equivalent()

     @skip_mps
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			Any:






         """simple docstring"""
         return super().test_save_load_local()






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






         """simple docstring"""
         return super().test_save_load_optional_components()







@nightly
class 		__UpperCAmelCase(		unittest.TestCase ):





     """simple docstring"""



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






         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			Tuple:






         """simple docstring"""
         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=A    )

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

         a__      =       output.images[0]

         assert image.shape == (2_56, 2_56, 3)
         assert np.abs(expected_image - image    ).max() < 1E-1







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





     """simple docstring"""



     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]    )			->			List[str]:






         """simple docstring"""
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			List[str]:






         """simple docstring"""
         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(A    )
         pipeline.set_progress_bar_config(disable=A    )

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

         a__      =       output.images[0]

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

         assert_mean_pixel_difference(A						,				A    )






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






         """simple docstring"""
         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(A    )
         pipe.set_progress_bar_config(disable=A    )
         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












from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_						:					List[str]			   =       {'''configuration_vit_hybrid''': ['''VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTHybridConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					Tuple			   =       [
     '''VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTHybridForImageClassification''',
     '''ViTHybridModel''',
     '''ViTHybridPreTrainedModel''',
 ]

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE_						:					int			   =       ['''ViTHybridImageProcessor''']


if TYPE_CHECKING:
 from .configuration_vit_hybrid import VIT_HYBRID_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTHybridConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_hybrid import (
      VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTHybridForImageClassification,
      ViTHybridModel,
      ViTHybridPreTrainedModel,
  )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_vit_hybrid import ViTHybridImageProcessor


else:
 import sys

 SCREAMING_SNAKE_CASE_						:					Any			   =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






