






import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


_a      	=  logging.get_logger(__name__)

enable_full_determinism()





class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      UNetaDModel
	UpperCamelCase__								=      "sample"

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			3
							_UpperCAmelCase									=			(32, 32)

							_UpperCAmelCase									=			floats_tensor((batch_size, num_channels) + sizes    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor([10]    ).to(UpperCAmelCase    )

							return {"sample": noise, "timestep": time_step}

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (3, 32, 32)

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (3, 32, 32)

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{
							    'block_out_channels': (32, 64),
							    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
							    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
							    'attention_head_dim': 3,
							    'out_channels': 3,
							    'in_channels': 3,
							    'layers_per_block': 2,
							    'sample_size': 32,
							}
							_UpperCAmelCase									=			self.dummy_input
							return init_dict, inputs_dict





class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      UNetaDModel
	UpperCamelCase__								=      "sample"

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			(32, 32)

							_UpperCAmelCase									=			floats_tensor((batch_size, num_channels) + sizes    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor([10]    ).to(UpperCAmelCase    )

							return {"sample": noise, "timestep": time_step}

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (4, 32, 32)

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (4, 32, 32)

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{
							    'sample_size': 32,
							    'in_channels': 4,
							    'out_channels': 4,
							    'layers_per_block': 2,
							    'block_out_channels': (32, 64),
							    'attention_head_dim': 32,
							    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
							    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
							}
							_UpperCAmelCase									=			self.dummy_input
							return init_dict, inputs_dict

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'       ,    output_loading_info=UpperCAmelCase    )

							self.assertIsNotNone(UpperCAmelCase    )
							self.assertEqual(len(loading_info['missing_keys']    )       ,    0    )

							model.to(UpperCAmelCase    )
							_UpperCAmelCase									=			model(**self.dummy_input    ).sample

							assert image is not None, "Make sure output is not None"

	@unittest.skipIf(torch_device != 'cuda'       ,    'This test is supposed to run on GPU'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'       ,    output_loading_info=UpperCAmelCase    )
							model.to(UpperCAmelCase    )
							_UpperCAmelCase									=			model(**self.dummy_input    ).sample

							assert image is not None, "Make sure output is not None"

	@unittest.skipIf(torch_device != 'cuda'       ,    'This test is supposed to run on GPU'    )
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'       ,    output_loading_info=UpperCAmelCase    )
							model_accelerate.to(UpperCAmelCase    )
							model_accelerate.eval()

							_UpperCAmelCase									=			torch.randn(
							    1       ,    model_accelerate.config.in_channels       ,    model_accelerate.config.sample_size       ,    model_accelerate.config.sample_size       ,    generator=torch.manual_seed(0    )       ,    )
							_UpperCAmelCase									=			noise.to(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor([10] * noise.shape[0]    ).to(UpperCAmelCase    )

							_UpperCAmelCase									=			model_accelerate(UpperCAmelCase       ,    UpperCAmelCase    )['sample']

							# two models don't need to stay in the device at the same time
							del model_accelerate
							torch.cuda.empty_cache()
							gc.collect()

							_UpperCAmelCase					, _UpperCAmelCase									=			UNetaDModel.from_pretrained(
							    'fusing/unet-ldm-dummy-update'       ,    output_loading_info=UpperCAmelCase       ,    low_cpu_mem_usage=UpperCAmelCase    )
							model_normal_load.to(UpperCAmelCase    )
							model_normal_load.eval()
							_UpperCAmelCase									=			model_normal_load(UpperCAmelCase       ,    UpperCAmelCase    )['sample']

							assert torch_all_close(UpperCAmelCase       ,    UpperCAmelCase       ,    rtol=1e-3    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'    )
							model.eval()
							model.to(UpperCAmelCase    )

							_UpperCAmelCase									=			torch.randn(
							    1       ,    model.config.in_channels       ,    model.config.sample_size       ,    model.config.sample_size       ,    generator=torch.manual_seed(0    )       ,    )
							_UpperCAmelCase									=			noise.to(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor([10] * noise.shape[0]    ).to(UpperCAmelCase    )

							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase       ,    UpperCAmelCase    ).sample

							_UpperCAmelCase									=			output[0, -1, -3:, -3:].flatten().cpu()
							# fmt: off
							_UpperCAmelCase									=			torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]    )
							# fmt: on

							self.assertTrue(torch_all_close(UpperCAmelCase       ,    UpperCAmelCase       ,    rtol=1e-3    )    )





class __lowerCamelCase (      snake_case__     ,       snake_case__     ,       unittest.TestCase):





	"""simple docstring"""






	UpperCamelCase__								=      UNetaDModel
	UpperCamelCase__								=      "sample"

	@property
	def        UpperCamelCase    (							self       ,    UpperCAmelCase=(32, 32)    ):
							"""simple docstring"""







							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			3

							_UpperCAmelCase									=			floats_tensor((batch_size, num_channels) + sizes    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor(batch_size * [10]    ).to(dtype=torch.intaa       ,    device=UpperCAmelCase    )

							return {"sample": noise, "timestep": time_step}

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (3, 32, 32)

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							return (3, 32, 32)

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			{
							    'block_out_channels': [32, 64, 64, 64],
							    'in_channels': 3,
							    'layers_per_block': 1,
							    'out_channels': 3,
							    'time_embedding_type': 'fourier',
							    'norm_eps': 1e-6,
							    'mid_block_scale_factor': math.sqrt(2.0    ),
							    'norm_num_groups': None,
							    'down_block_types': [
							        'SkipDownBlock2D',
							        'AttnSkipDownBlock2D',
							        'SkipDownBlock2D',
							        'SkipDownBlock2D',
							    ],
							    'up_block_types': [
							        'SkipUpBlock2D',
							        'SkipUpBlock2D',
							        'AttnSkipUpBlock2D',
							        'SkipUpBlock2D',
							    ],
							}
							_UpperCAmelCase									=			self.dummy_input
							return init_dict, inputs_dict

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase					, _UpperCAmelCase									=			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'       ,    output_loading_info=UpperCAmelCase    )
							self.assertIsNotNone(UpperCAmelCase    )
							self.assertEqual(len(loading_info['missing_keys']    )       ,    0    )

							model.to(UpperCAmelCase    )
							_UpperCAmelCase									=			self.dummy_input
							_UpperCAmelCase									=			floats_tensor((4, 3) + (256, 256)    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			noise
							_UpperCAmelCase									=			model(**UpperCAmelCase    )

							assert image is not None, "Make sure output is not None"

	@slow
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'    )
							model.to(UpperCAmelCase    )

							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			3
							_UpperCAmelCase									=			(256, 256)

							_UpperCAmelCase									=			torch.ones((batch_size, num_channels) + sizes    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor(batch_size * [1e-4]    ).to(UpperCAmelCase    )

							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase       ,    UpperCAmelCase    ).sample

							_UpperCAmelCase									=			output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							_UpperCAmelCase									=			torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]    )
							# fmt: on

							self.assertTrue(torch_all_close(UpperCAmelCase       ,    UpperCAmelCase       ,    rtol=1e-2    )    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'    )
							model.to(UpperCAmelCase    )

							_UpperCAmelCase									=			4
							_UpperCAmelCase									=			3
							_UpperCAmelCase									=			(32, 32)

							_UpperCAmelCase									=			torch.ones((batch_size, num_channels) + sizes    ).to(UpperCAmelCase    )
							_UpperCAmelCase									=			torch.tensor(batch_size * [1e-4]    ).to(UpperCAmelCase    )

							with torch.no_grad():
													_UpperCAmelCase									=			model(UpperCAmelCase       ,    UpperCAmelCase    ).sample

							_UpperCAmelCase									=			output[0, -3:, -3:, -1].flatten().cpu()
							# fmt: off
							_UpperCAmelCase									=			torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]    )
							# fmt: on

							self.assertTrue(torch_all_close(UpperCAmelCase       ,    UpperCAmelCase       ,    rtol=1e-2    )    )

	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							pass








from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




				def __call__(     self						:    int,  lowerCamelCase						:    ImageInput = None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''  )

								# Get only text
								if images is None:
												lowercase__    =			self.tokenizer
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
												lowercase__    =			None

								if text_encoding is not None:
												encoding_image_processor.update(lowerCamelCase  )

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




