






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'''




	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 							_UpperCamelCase      (	self )	->   Any:
								return 32




	@property
	def 							_UpperCamelCase      (	self )	->   List[str]:
								return 32




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




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




	@property
	def 							_UpperCamelCase      (	self )	->   int:
								return 1_00




	@property
	def 							_UpperCamelCase      (	self )	->   Dict:
								snake_case_           =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
								return tokenizer




	@property
	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								torch.manual_seed(0 )
								snake_case_           =							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(UpperCamelCase_ )




	@property
	def 							_UpperCamelCase      (	self )	->   int:
								torch.manual_seed(0 )

								snake_case_           =							{
								    'num_attention_heads': 2,
								    'attention_head_dim': 12,
								    'embedding_dim': self.text_embedder_hidden_size,
								    'num_layers': 1,
								}

								snake_case_           =							PriorTransformer(**UpperCamelCase_ )
								return model




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

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

								snake_case_           =							UnCLIPTextProjModel(**UpperCamelCase_ )
								return model




	@property
	def 							_UpperCamelCase      (	self )	->   int:
								torch.manual_seed(0 )

								snake_case_           =							{
								    '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',
								}

								snake_case_           =							UNetaDConditionModel(**UpperCamelCase_ )
								return model




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

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




	@property
	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								torch.manual_seed(1 )

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




	def 							_UpperCamelCase      (	self )	->   str:
								snake_case_           =							self.dummy_prior
								snake_case_           =							self.dummy_decoder
								snake_case_           =							self.dummy_text_proj
								snake_case_           =							self.dummy_text_encoder
								snake_case_           =							self.dummy_tokenizer
								snake_case_           =							self.dummy_super_res_first
								snake_case_           =							self.dummy_super_res_last

								snake_case_           =							UnCLIPScheduler(
								    variance_type='fixed_small_log'	,      prediction_type='sample'	,      num_train_timesteps=10_00	,      clip_sample_range=5.0	,      )

								snake_case_           =							UnCLIPScheduler(
								    variance_type='learned_range'	,      prediction_type='epsilon'	,      num_train_timesteps=10_00	,      )

								snake_case_           =							UnCLIPScheduler(
								    variance_type='fixed_small_log'	,      prediction_type='epsilon'	,      num_train_timesteps=10_00	,      )

								snake_case_           =							{
								    '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 							_UpperCamelCase      (	self	,      a	,      a=0 )	->   str:



								if str(UpperCamelCase_ ).startswith('mps' ):
															snake_case_           =							torch.manual_seed(UpperCamelCase_ )
								else:
															snake_case_           =							torch.Generator(device=UpperCamelCase_ ).manual_seed(UpperCamelCase_ )
								snake_case_           =							{
								    '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 							_UpperCamelCase      (	self )	->   Dict:
								snake_case_           =							'cpu'

								snake_case_           =							self.get_dummy_components()

								snake_case_           =							self.pipeline_class(**UpperCamelCase_ )
								snake_case_           =							pipe.to(UpperCamelCase_ )

								pipe.set_progress_bar_config(disable=UpperCamelCase_ )

								snake_case_           =							pipe(**self.get_dummy_inputs(UpperCamelCase_ ) )
								snake_case_           =							output.images

								snake_case_           =							pipe(
								    **self.get_dummy_inputs(UpperCamelCase_ )	,      return_dict=UpperCamelCase_	,      )[0]

								snake_case_           =							image[0, -3:, -3:, -1]
								snake_case_           =							image_from_tuple[0, -3:, -3:, -1]

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

								snake_case_           =							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 							_UpperCamelCase      (	self )	->   Optional[Any]:
								snake_case_           =							torch.device('cpu' )




								class 			UpperCamelCase_				:



									'''simple docstring'''




									lowerCAmelCase													=			1






								snake_case_           =							self.get_dummy_components()

								snake_case_           =							self.pipeline_class(**UpperCamelCase_ )
								snake_case_           =							pipe.to(UpperCamelCase_ )

								snake_case_           =							components['prior']
								snake_case_           =							components['decoder']
								snake_case_           =							components['super_res_first']
								snake_case_           =							components['tokenizer']
								snake_case_           =							components['text_encoder']

								snake_case_           =							torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
								snake_case_           =							prior.dtype
								snake_case_           =							1

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

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

								pipe.set_progress_bar_config(disable=UpperCamelCase_ )

								snake_case_           =							'this is a prompt example'

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

								snake_case_           =							tokenizer(
								    UpperCamelCase_	,      padding='max_length'	,      max_length=tokenizer.model_max_length	,      return_tensors='pt'	,      )
								snake_case_           =							text_encoder(text_inputs.input_ids )
								snake_case_           =							text_inputs.attention_mask

								snake_case_           =							torch.Generator(device=UpperCamelCase_ ).manual_seed(0 )
								snake_case_           =							pipe(
								    generator=UpperCamelCase_	,      prior_num_inference_steps=2	,      decoder_num_inference_steps=2	,      super_res_num_inference_steps=2	,      prior_latents=UpperCamelCase_	,      decoder_latents=UpperCamelCase_	,      super_res_latents=UpperCamelCase_	,      text_model_output=UpperCamelCase_	,      text_attention_mask=UpperCamelCase_	,      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 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_           =							torch_device == 'cpu'

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




	@skip_mps
	def 							_UpperCamelCase      (	self )	->   List[Any]:
								snake_case_           =							torch_device == 'cpu'
								snake_case_           =							True
								snake_case_           =							[
								    'prior_num_inference_steps',
								    'decoder_num_inference_steps',
								    'super_res_num_inference_steps',
								]

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




	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_           =							[
								    'prior_num_inference_steps',
								    'decoder_num_inference_steps',
								    'super_res_num_inference_steps',
								]

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




	@skip_mps
	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								return super().test_dict_tuple_outputs_equivalent()




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






	@skip_mps
	def 							_UpperCamelCase      (	self )	->   Union[str, Any]:
								return super().test_save_load_optional_components()






@nightly
class 			UpperCamelCase_				(      unittest.TestCase    ):



	'''simple docstring'''




	def 							_UpperCamelCase      (	self )	->   Tuple:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






	def 							_UpperCamelCase      (	self )	->   Optional[int]:
								snake_case_           =							load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/unclip/karlo_v1_alpha_horse_cpu.npy' )

								snake_case_           =							UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha' )
								pipeline.set_progress_bar_config(disable=UpperCamelCase_ )

								snake_case_           =							torch.manual_seed(0 )
								snake_case_           =							pipeline(
								    'horse'	,      num_images_per_prompt=1	,      generator=UpperCamelCase_	,      output_type='np'	,      )

								snake_case_           =							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 							_UpperCamelCase      (	self )	->   Dict:
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()




	def 							_UpperCamelCase      (	self )	->   Tuple:
								snake_case_           =							load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/unclip/karlo_v1_alpha_horse_fp16.npy' )

								snake_case_           =							UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'	,      torch_dtype=torch.floataa )
								snake_case_           =							pipeline.to(UpperCamelCase_ )
								pipeline.set_progress_bar_config(disable=UpperCamelCase_ )

								snake_case_           =							torch.Generator(device='cpu' ).manual_seed(0 )
								snake_case_           =							pipeline(
								    'horse'	,      generator=UpperCamelCase_	,      output_type='np'	,      )

								snake_case_           =							output.images[0]

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

								assert_mean_pixel_difference(UpperCamelCase_	,      UpperCamelCase_ )






	def 							_UpperCamelCase      (	self )	->   Any:
								torch.cuda.empty_cache()
								torch.cuda.reset_max_memory_allocated()
								torch.cuda.reset_peak_memory_stats()

								snake_case_           =							UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'	,      torch_dtype=torch.floataa )
								snake_case_           =							pipe.to(UpperCamelCase_ )
								pipe.set_progress_bar_config(disable=UpperCamelCase_ )
								pipe.enable_attention_slicing()
								pipe.enable_sequential_cpu_offload()

								snake_case_           =							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'	,      )

								snake_case_           =							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


lowercase  							= {
    "configuration_deformable_detr": ["DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DeformableDetrConfig"],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= ["DeformableDetrFeatureExtractor"]
	lowercase  							= ["DeformableDetrImageProcessor"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase  							= [
	    "DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "DeformableDetrForObjectDetection",
	    "DeformableDetrModel",
	    "DeformableDetrPreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
		from .image_processing_deformable_detr import DeformableDetrImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_deformable_detr import (
		    DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    DeformableDetrForObjectDetection,
		    DeformableDetrModel,
		    DeformableDetrPreTrainedModel,
		)

else:
	import sys

	lowercase  							= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





