



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 			lowerCamelCase__     (				__A ,							unittest.TestCase):




		'''simple docstring'''



		snake_case_      =UnCLIPImageVariationPipeline
		snake_case_      =IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
		snake_case_      =IMAGE_VARIATION_BATCH_PARAMS

		snake_case_      =[
		    """generator""",
		    """return_dict""",
		    """decoder_num_inference_steps""",
		    """super_res_num_inference_steps""",
		]
		snake_case_      =False



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

						"""simple docstring"""






						return 32



		@property
		def 						lowerCAmelCase__ (self					)	->     Any:

						"""simple docstring"""






						return 32



		@property
		def 						lowerCAmelCase__ (self					)	->     Any:

						"""simple docstring"""






						return self.time_input_dim



		@property
		def 						lowerCAmelCase__ (self					)	->     Tuple:

						"""simple docstring"""






						return self.time_input_dim * 4



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

						"""simple docstring"""






						return 1_00



		@property
		def 						lowerCAmelCase__ (self					)	->     Tuple:

						"""simple docstring"""






						lowerCAmelCase__	:							Optional[Any]				=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
						return tokenizer



		@property
		def 						lowerCAmelCase__ (self					)	->     int:

						"""simple docstring"""






						torch.manual_seed(0					)
						lowerCAmelCase__	:							Tuple				=	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 						lowerCAmelCase__ (self					)	->     Dict:

						"""simple docstring"""






						torch.manual_seed(0					)
						lowerCAmelCase__	:							Optional[int]				=	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(__A					)



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

						"""simple docstring"""






						torch.manual_seed(0					)

						lowerCAmelCase__	:							Tuple				=	{
						    '''clip_embeddings_dim''': self.text_embedder_hidden_size,
						    '''time_embed_dim''': self.time_embed_dim,
						    '''cross_attention_dim''': self.cross_attention_dim,
						}

						lowerCAmelCase__	:							str				=	UnCLIPTextProjModel(**__A					)
						return model



		@property
		def 						lowerCAmelCase__ (self					)	->     int:

						"""simple docstring"""






						torch.manual_seed(0					)

						lowerCAmelCase__	:							Any				=	{
						    '''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''',
						}

						lowerCAmelCase__	:							Optional[int]				=	UNetaDConditionModel(**__A					)
						return model



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

						"""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 						lowerCAmelCase__ (self					)	->     Any:

						"""simple docstring"""






						torch.manual_seed(0					)

						lowerCAmelCase__	:							str				=	UNetaDModel(**self.dummy_super_res_kwargs					)
						return model



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

						"""simple docstring"""






						torch.manual_seed(1					)

						lowerCAmelCase__	:							List[str]				=	UNetaDModel(**self.dummy_super_res_kwargs					)
						return model



		def 						lowerCAmelCase__ (self					)	->     Dict:

						"""simple docstring"""






						lowerCAmelCase__	:							Dict				=	self.dummy_decoder
						lowerCAmelCase__	:							Optional[Any]				=	self.dummy_text_proj
						lowerCAmelCase__	:							int				=	self.dummy_text_encoder
						lowerCAmelCase__	:							int				=	self.dummy_tokenizer
						lowerCAmelCase__	:							List[Any]				=	self.dummy_super_res_first
						lowerCAmelCase__	:							Union[str, Any]				=	self.dummy_super_res_last

						lowerCAmelCase__	:							List[Any]				=	UnCLIPScheduler(
						    variance_type='''learned_range'''						,prediction_type='''epsilon'''						,num_train_timesteps=10_00						,)

						lowerCAmelCase__	:							Any				=	UnCLIPScheduler(
						    variance_type='''fixed_small_log'''						,prediction_type='''epsilon'''						,num_train_timesteps=10_00						,)

						lowerCAmelCase__	:							str				=	CLIPImageProcessor(crop_size=32						,size=32					)

						lowerCAmelCase__	:							Dict				=	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 						lowerCAmelCase__ (self						,__lowerCamelCase						,__lowerCamelCase=0						,__lowerCamelCase=True					)	->     Dict:

						"""simple docstring"""






						lowerCAmelCase__	:							Union[str, Any]				=	floats_tensor((1, 3, 32, 32)						,rng=random.Random(__A					)					).to(__A					)
						if str(__A					).startswith('''mps'''					):
										lowerCAmelCase__	:							List[str]				=	torch.manual_seed(__A					)
						else:
										lowerCAmelCase__	:							Optional[Any]				=	torch.Generator(device=__A					).manual_seed(__A					)

						if pil_image:
										lowerCAmelCase__	:							Optional[int]				=	input_image * 0.5 + 0.5
										lowerCAmelCase__	:							Union[str, Any]				=	input_image.clamp(0						,1					)
										lowerCAmelCase__	:							Optional[int]				=	input_image.cpu().permute(0						,2						,3						,1					).float().numpy()
										lowerCAmelCase__	:							Dict				=	DiffusionPipeline.numpy_to_pil(__A					)[0]

						return {
						    "image": input_image,
						    "generator": generator,
						    "decoder_num_inference_steps": 2,
						    "super_res_num_inference_steps": 2,
						    "output_type": "np",
						}



		def 						lowerCAmelCase__ (self					)	->     Any:

						"""simple docstring"""






						lowerCAmelCase__	:							int				=	'''cpu'''

						lowerCAmelCase__	:							Union[str, Any]				=	self.get_dummy_components()

						lowerCAmelCase__	:							str				=	self.pipeline_class(**__A					)
						lowerCAmelCase__	:							int				=	pipe.to(__A					)

						pipe.set_progress_bar_config(disable=__A					)

						lowerCAmelCase__	:							Union[str, Any]				=	self.get_dummy_inputs(__A						,pil_image=__A					)

						lowerCAmelCase__	:							Tuple				=	pipe(**__A					)
						lowerCAmelCase__	:							Union[str, Any]				=	output.images

						lowerCAmelCase__	:							Tuple				=	self.get_dummy_inputs(__A						,pil_image=__A					)

						lowerCAmelCase__	:							List[str]				=	pipe(
						    **__A						,return_dict=__A						,)[0]

						lowerCAmelCase__	:							Optional[int]				=	image[0, -3:, -3:, -1]
						lowerCAmelCase__	:							Tuple				=	image_from_tuple[0, -3:, -3:, -1]

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

						lowerCAmelCase__	:							List[str]				=	np.array(
						    [
						        0.9997,
						        0.0002,
						        0.9997,
						        0.9997,
						        0.9969,
						        0.0023,
						        0.9997,
						        0.9969,
						        0.9970,
						    ]					)

						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 						lowerCAmelCase__ (self					)	->     List[str]:

						"""simple docstring"""






						lowerCAmelCase__	:							List[str]				=	'''cpu'''

						lowerCAmelCase__	:							Dict				=	self.get_dummy_components()

						lowerCAmelCase__	:							List[str]				=	self.pipeline_class(**__A					)
						lowerCAmelCase__	:							Tuple				=	pipe.to(__A					)

						pipe.set_progress_bar_config(disable=__A					)

						lowerCAmelCase__	:							List[str]				=	self.get_dummy_inputs(__A						,pil_image=__A					)

						lowerCAmelCase__	:							Tuple				=	pipe(**__A					)
						lowerCAmelCase__	:							Union[str, Any]				=	output.images

						lowerCAmelCase__	:							List[Any]				=	self.get_dummy_inputs(__A						,pil_image=__A					)

						lowerCAmelCase__	:							List[Any]				=	pipe(
						    **__A						,return_dict=__A						,)[0]

						lowerCAmelCase__	:							Optional[int]				=	image[0, -3:, -3:, -1]
						lowerCAmelCase__	:							Dict				=	image_from_tuple[0, -3:, -3:, -1]

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

						lowerCAmelCase__	:							List[str]				=	np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]					)

						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 						lowerCAmelCase__ (self					)	->     int:

						"""simple docstring"""






						lowerCAmelCase__	:							Union[str, Any]				=	'''cpu'''

						lowerCAmelCase__	:							Tuple				=	self.get_dummy_components()

						lowerCAmelCase__	:							Tuple				=	self.pipeline_class(**__A					)
						lowerCAmelCase__	:							str				=	pipe.to(__A					)

						pipe.set_progress_bar_config(disable=__A					)

						lowerCAmelCase__	:							Tuple				=	self.get_dummy_inputs(__A						,pil_image=__A					)
						lowerCAmelCase__	:							Union[str, Any]				=	[
						    pipeline_inputs['''image'''],
						    pipeline_inputs['''image'''],
						]

						lowerCAmelCase__	:							Union[str, Any]				=	pipe(**__A					)
						lowerCAmelCase__	:							Optional[int]				=	output.images

						lowerCAmelCase__	:							int				=	self.get_dummy_inputs(__A						,pil_image=__A					)
						lowerCAmelCase__	:							List[Any]				=	[
						    tuple_pipeline_inputs['''image'''],
						    tuple_pipeline_inputs['''image'''],
						]

						lowerCAmelCase__	:							Optional[int]				=	pipe(
						    **__A						,return_dict=__A						,)[0]

						lowerCAmelCase__	:							List[str]				=	image[0, -3:, -3:, -1]
						lowerCAmelCase__	:							Dict				=	image_from_tuple[0, -3:, -3:, -1]

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

						lowerCAmelCase__	:							List[str]				=	np.array(
						    [
						        0.9997,
						        0.9989,
						        0.0008,
						        0.0021,
						        0.9960,
						        0.0018,
						        0.0014,
						        0.0002,
						        0.9933,
						    ]					)

						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 						lowerCAmelCase__ (self					)	->     Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__	:							List[Any]				=	torch.device('''cpu'''					)



						class 			lowerCamelCase__     :




								'''simple docstring'''



								snake_case_      =1


						lowerCAmelCase__	:							Union[str, Any]				=	self.get_dummy_components()

						lowerCAmelCase__	:							Optional[Any]				=	self.pipeline_class(**__A					)
						lowerCAmelCase__	:							List[Any]				=	pipe.to(__A					)

						pipe.set_progress_bar_config(disable=__A					)

						lowerCAmelCase__	:							Dict				=	torch.Generator(device=__A					).manual_seed(0					)
						lowerCAmelCase__	:							List[str]				=	pipe.decoder.dtype
						lowerCAmelCase__	:							Union[str, Any]				=	1

						lowerCAmelCase__	:							Optional[Any]				=	(
						    batch_size,
						    pipe.decoder.config.in_channels,
						    pipe.decoder.config.sample_size,
						    pipe.decoder.config.sample_size,
						)
						lowerCAmelCase__	:							str				=	pipe.prepare_latents(
						    __A						,dtype=__A						,device=__A						,generator=__A						,latents=__A						,scheduler=DummyScheduler()					)

						lowerCAmelCase__	:							Optional[Any]				=	(
						    batch_size,
						    pipe.super_res_first.config.in_channels // 2,
						    pipe.super_res_first.config.sample_size,
						    pipe.super_res_first.config.sample_size,
						)
						lowerCAmelCase__	:							Tuple				=	pipe.prepare_latents(
						    __A						,dtype=__A						,device=__A						,generator=__A						,latents=__A						,scheduler=DummyScheduler()					)

						lowerCAmelCase__	:							Union[str, Any]				=	self.get_dummy_inputs(__A						,pil_image=__A					)

						lowerCAmelCase__	:							int				=	pipe(
						    **__A						,decoder_latents=__A						,super_res_latents=__A					).images

						lowerCAmelCase__	:							Dict				=	self.get_dummy_inputs(__A						,pil_image=__A					)
						# Don't pass image, instead pass embedding
						lowerCAmelCase__	:							Tuple				=	pipeline_inputs.pop('''image'''					)
						lowerCAmelCase__	:							Optional[int]				=	pipe.image_encoder(__A					).image_embeds

						lowerCAmelCase__	:							Optional[Any]				=	pipe(
						    **__A						,decoder_latents=__A						,super_res_latents=__A						,image_embeddings=__A						,).images

						# make sure passing text embeddings manually is identical
						assert np.abs(img_out_a - img_out_a					).max() < 1e-4



		@skip_mps
		def 						lowerCAmelCase__ (self					)	->     Dict:

						"""simple docstring"""






						lowerCAmelCase__	:							str				=	torch_device == '''cpu'''

						# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
						lowerCAmelCase__	:							Any				=	1e-2

						self._test_attention_slicing_forward_pass(
						    test_max_difference=__A						,expected_max_diff=__A					)



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

						"""simple docstring"""






						lowerCAmelCase__	:							Any				=	torch_device == '''cpu'''
						lowerCAmelCase__	:							Tuple				=	True
						lowerCAmelCase__	:							Dict				=	[
						    '''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 						lowerCAmelCase__ (self					)	->     Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__	:							List[str]				=	[
						    '''decoder_num_inference_steps''',
						    '''super_res_num_inference_steps''',
						]

						if torch_device == "mps":
										# TODO: MPS errors with larger batch sizes
										lowerCAmelCase__	:							Optional[int]				=	[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 						lowerCAmelCase__ (self					)	->     Optional[Any]:

						"""simple docstring"""






						return super().test_dict_tuple_outputs_equivalent()



		@skip_mps
		def 						lowerCAmelCase__ (self					)	->     Any:

						"""simple docstring"""






						return super().test_save_load_local()



		@skip_mps
		def 						lowerCAmelCase__ (self					)	->     Dict:

						"""simple docstring"""






						return super().test_save_load_optional_components()



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




		'''simple docstring'''



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

						"""simple docstring"""






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



		def 						lowerCAmelCase__ (self					)	->     Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__	:							int				=	load_image(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'''					)
						lowerCAmelCase__	:							str				=	load_numpy(
						    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
						    '''/unclip/karlo_v1_alpha_cat_variation_fp16.npy'''					)

						lowerCAmelCase__	:							List[str]				=	UnCLIPImageVariationPipeline.from_pretrained(
						    '''kakaobrain/karlo-v1-alpha-image-variations'''						,torch_dtype=torch.floataa					)
						lowerCAmelCase__	:							List[Any]				=	pipeline.to(__A					)
						pipeline.set_progress_bar_config(disable=__A					)

						lowerCAmelCase__	:							int				=	torch.Generator(device='''cpu'''					).manual_seed(0					)
						lowerCAmelCase__	:							Tuple				=	pipeline(
						    __A						,generator=__A						,output_type='''np'''						,)

						lowerCAmelCase__	:							Tuple				=	output.images[0]

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

						assert_mean_pixel_difference(__A						,__A						,15					)





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



