






'''simple docstring'''

import tempfile

import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel

from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device

from ..test_pipelines_common import to_np




class     A    :






	"""simple docstring"""





	def   _UpperCAmelCase       (							self ):
							torch.manual_seed(0 )
							UpperCamelCase_				:   List[Any]					    =							TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

							torch.manual_seed(0 )
							UpperCamelCase_				:   List[str]					    =							AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

							torch.manual_seed(0 )
							UpperCamelCase_				:   Optional[Any]					    =							UNetaDConditionModel(
							    sample_size=32			,     layers_per_block=1			,     block_out_channels=[32, 64]			,     down_block_types=[
							        """ResnetDownsampleBlock2D""",
							        """SimpleCrossAttnDownBlock2D""",
							    ]			,     mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""			,     up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]			,     in_channels=3			,     out_channels=6			,     cross_attention_dim=32			,     encoder_hid_dim=32			,     attention_head_dim=8			,     addition_embed_type="""text"""			,     addition_embed_type_num_heads=2			,     cross_attention_norm="""group_norm"""			,     resnet_time_scale_shift="""scale_shift"""			,     act_fn="""gelu"""			,     )
							unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

							torch.manual_seed(0 )
							UpperCamelCase_				:   Optional[int]					    =							DDPMScheduler(
							    num_train_timesteps=10_00			,     beta_schedule="""squaredcos_cap_v2"""			,     beta_start=0.00_01			,     beta_end=0.02			,     thresholding=__lowerCAmelCase			,     dynamic_thresholding_ratio=0.95			,     sample_max_value=1.0			,     prediction_type="""epsilon"""			,     variance_type="""learned_range"""			,     )

							torch.manual_seed(0 )
							UpperCamelCase_				:   Dict					    =							IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
	def   _UpperCAmelCase       (							self ):
							torch.manual_seed(0 )
							UpperCamelCase_				:   Union[str, Any]					    =							TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

							torch.manual_seed(0 )
							UpperCamelCase_				:   Any					    =							AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )

							torch.manual_seed(0 )
							UpperCamelCase_				:   Union[str, Any]					    =							UNetaDConditionModel(
							    sample_size=32			,     layers_per_block=[1, 2]			,     block_out_channels=[32, 64]			,     down_block_types=[
							        """ResnetDownsampleBlock2D""",
							        """SimpleCrossAttnDownBlock2D""",
							    ]			,     mid_block_type="""UNetMidBlock2DSimpleCrossAttn"""			,     up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""]			,     in_channels=6			,     out_channels=6			,     cross_attention_dim=32			,     encoder_hid_dim=32			,     attention_head_dim=8			,     addition_embed_type="""text"""			,     addition_embed_type_num_heads=2			,     cross_attention_norm="""group_norm"""			,     resnet_time_scale_shift="""scale_shift"""			,     act_fn="""gelu"""			,     class_embed_type="""timestep"""			,     mid_block_scale_factor=1.4_14			,     time_embedding_act_fn="""gelu"""			,     time_embedding_dim=32			,     )
							unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

							torch.manual_seed(0 )
							UpperCamelCase_				:   int					    =							DDPMScheduler(
							    num_train_timesteps=10_00			,     beta_schedule="""squaredcos_cap_v2"""			,     beta_start=0.00_01			,     beta_end=0.02			,     thresholding=__lowerCAmelCase			,     dynamic_thresholding_ratio=0.95			,     sample_max_value=1.0			,     prediction_type="""epsilon"""			,     variance_type="""learned_range"""			,     )

							torch.manual_seed(0 )
							UpperCamelCase_				:   Any					    =							DDPMScheduler(
							    num_train_timesteps=10_00			,     beta_schedule="""squaredcos_cap_v2"""			,     beta_start=0.00_01			,     beta_end=0.02			,     )

							torch.manual_seed(0 )
							UpperCamelCase_				:   int					    =							IFWatermarker()

							return {
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "unet": unet,
							    "scheduler": scheduler,
							    "image_noising_scheduler": image_noising_scheduler,
							    "watermarker": watermarker,
							    "safety_checker": None,
							    "feature_extractor": None,
							}
	def   _UpperCAmelCase       (							self ):
							UpperCamelCase_				:   str					    =							self.get_dummy_components()
							UpperCamelCase_				:   Tuple					    =							self.pipeline_class(**__lowerCAmelCase )
							pipe.to(__lowerCAmelCase )
							pipe.set_progress_bar_config(disable=__lowerCAmelCase )

							UpperCamelCase_				:   List[str]					    =							self.get_dummy_inputs(__lowerCAmelCase )

							UpperCamelCase_				:   List[str]					    =							inputs["""prompt"""]
							UpperCamelCase_				:   Optional[int]					    =							inputs["""generator"""]
							UpperCamelCase_				:   str					    =							inputs["""num_inference_steps"""]
							UpperCamelCase_				:   Optional[Any]					    =							inputs["""output_type"""]

							if "image" in inputs:
													UpperCamelCase_				:   Optional[int]					    =							inputs["""image"""]
							else:
													UpperCamelCase_				:   Optional[int]					    =							None

							if "mask_image" in inputs:
													UpperCamelCase_				:   Any					    =							inputs["""mask_image"""]
							else:
													UpperCamelCase_				:   Union[str, Any]					    =							None

							if "original_image" in inputs:
													UpperCamelCase_				:   Any					    =							inputs["""original_image"""]
							else:
													UpperCamelCase_				:   Optional[int]					    =							None

							UpperCamelCase_				:   Any					    =							pipe.encode_prompt(__lowerCAmelCase )

							# inputs with prompt converted to embeddings
							UpperCamelCase_				:   Union[str, Any]					    =							{
							    """prompt_embeds""": prompt_embeds,
							    """negative_prompt_embeds""": negative_prompt_embeds,
							    """generator""": generator,
							    """num_inference_steps""": num_inference_steps,
							    """output_type""": output_type,
							}

							if image is not None:
													UpperCamelCase_				:   Dict					    =							image

							if mask_image is not None:
													UpperCamelCase_				:   str					    =							mask_image

							if original_image is not None:
													UpperCamelCase_				:   int					    =							original_image

							# set all optional components to None
							for optional_component in pipe._optional_components:
													setattr(__lowerCAmelCase			,     __lowerCAmelCase			,     __lowerCAmelCase )

							UpperCamelCase_				:   Any					    =							pipe(**__lowerCAmelCase )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(__lowerCAmelCase )
													UpperCamelCase_				:   Tuple					    =							self.pipeline_class.from_pretrained(__lowerCAmelCase )
													pipe_loaded.to(__lowerCAmelCase )
													pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

							for optional_component in pipe._optional_components:
													self.assertTrue(
													    getattr(__lowerCAmelCase			,     __lowerCAmelCase ) is None			,     F"`{optional_component}` did not stay set to None after loading."			,     )

							UpperCamelCase_				:   Dict					    =							self.get_dummy_inputs(__lowerCAmelCase )

							UpperCamelCase_				:   Union[str, Any]					    =							inputs["""generator"""]
							UpperCamelCase_				:   int					    =							inputs["""num_inference_steps"""]
							UpperCamelCase_				:   Optional[int]					    =							inputs["""output_type"""]

							# inputs with prompt converted to embeddings
							UpperCamelCase_				:   str					    =							{
							    """prompt_embeds""": prompt_embeds,
							    """negative_prompt_embeds""": negative_prompt_embeds,
							    """generator""": generator,
							    """num_inference_steps""": num_inference_steps,
							    """output_type""": output_type,
							}

							if image is not None:
													UpperCamelCase_				:   str					    =							image

							if mask_image is not None:
													UpperCamelCase_				:   int					    =							mask_image

							if original_image is not None:
													UpperCamelCase_				:   List[Any]					    =							original_image

							UpperCamelCase_				:   List[Any]					    =							pipe_loaded(**__lowerCAmelCase )[0]

							UpperCamelCase_				:   Any					    =							np.abs(to_np(__lowerCAmelCase ) - to_np(__lowerCAmelCase ) ).max()
							self.assertLess(__lowerCAmelCase			,     1E-4 )





	def   _UpperCAmelCase       (							self ):
							UpperCamelCase_				:   int					    =							self.get_dummy_components()
							UpperCamelCase_				:   List[Any]					    =							self.pipeline_class(**__lowerCAmelCase )
							pipe.to(__lowerCAmelCase )
							pipe.set_progress_bar_config(disable=__lowerCAmelCase )

							UpperCamelCase_				:   Any					    =							self.get_dummy_inputs(__lowerCAmelCase )
							UpperCamelCase_				:   str					    =							pipe(**__lowerCAmelCase )[0]

							with tempfile.TemporaryDirectory() as tmpdir:
													pipe.save_pretrained(__lowerCAmelCase )
													UpperCamelCase_				:   List[Any]					    =							self.pipeline_class.from_pretrained(__lowerCAmelCase )
													pipe_loaded.to(__lowerCAmelCase )
													pipe_loaded.set_progress_bar_config(disable=__lowerCAmelCase )

							pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() )  # For reproducibility tests

							UpperCamelCase_				:   Tuple					    =							self.get_dummy_inputs(__lowerCAmelCase )
							UpperCamelCase_				:   Optional[int]					    =							pipe_loaded(**__lowerCAmelCase )[0]

							UpperCamelCase_				:   str					    =							np.abs(to_np(__lowerCAmelCase ) - to_np(__lowerCAmelCase ) ).max()
							self.assertLess(__lowerCAmelCase			,     1E-4 )














'''simple docstring'''

def 							snake_case					(     a_							: int	)       ->				int:





      """simple docstring"""

      assert (
          isinstance(a_    ,   a_	) and number_of_steps > 0
      ), f"number_of_steps needs to be positive integer, your input {number_of_steps}"
      if number_of_steps == 1:
            return 1
      UpperCamelCase_    ,			UpperCamelCase_				:   str					    =							1, 1
      for _ in range(number_of_steps - 1	):
            UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							current + previous, current
      return current


if __name__ == "__main__":
      import doctest

      doctest.testmod()






