"""simple docstring"""






from collections.abc import Generator
from math import sin


def 			UpperCAmelCase__       (				lowerCAmelCase__     :bytes		) ->    bytes:
      '''simple docstring'''


      if len(lowerCAmelCase__		) != 3_2:
            raise ValueError("""Input must be of length 32"""		)

      lowercase														=	B""""""
      for i in [3, 2, 1, 0]:
            little_endian += string_aa[8 * i : 8 * i + 8]
      return little_endian


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    bytes:
      '''simple docstring'''


      if i < 0:
            raise ValueError("""Input must be non-negative"""		)

      lowercase														=	format(lowerCAmelCase__					,		"""08x"""		)[-8:]
      lowercase														=	B""""""
      for i in [3, 2, 1, 0]:
            little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8"""		)
      return little_endian_hex


def 			UpperCAmelCase__       (				lowerCAmelCase__     :bytes		) ->    bytes:
      '''simple docstring'''


      lowercase														=	B""""""
      for char in message:
            bit_string += format(lowerCAmelCase__					,		"""08b"""		).encode("""utf-8"""		)
      lowercase														=	format(len(lowerCAmelCase__		)					,		"""064b"""		).encode("""utf-8"""		)

      # Pad bit_string to a multiple of 512 chars
      bit_string += b"1"
      while len(lowerCAmelCase__		) % 5_1_2 != 4_4_8:
            bit_string += b"0"
      bit_string += to_little_endian(start_len[3_2:]		) + to_little_endian(start_len[:3_2]		)

      return bit_string


def 			UpperCAmelCase__       (				lowerCAmelCase__     :bytes		) ->    Generator[list[int], None, None]:
      '''simple docstring'''


      if len(lowerCAmelCase__		) % 5_1_2 != 0:
            raise ValueError("""Input must have length that's a multiple of 512"""		)

      for pos in range(0					,		len(lowerCAmelCase__		)					,		5_1_2		):
            lowercase														=	bit_string[pos : pos + 5_1_2]
            lowercase														=	[]
            for i in range(0					,		5_1_2					,		3_2		):
                  block_words.append(int(to_little_endian(block[i : i + 3_2]		)					,		2		)		)
            yield block_words


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      if i < 0:
            raise ValueError("""Input must be non-negative"""		)

      lowercase														=	format(lowerCAmelCase__					,		"""032b"""		)
      lowercase														=	""""""
      for c in i_str:
            new_str += "1" if c == "0" else "0"
      return int(lowerCAmelCase__					,		2		)


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      return (a + b) % 2**3_2


def 			UpperCAmelCase__       (				lowerCAmelCase__     :int					,		lowerCAmelCase__     :int		) ->    int:
      '''simple docstring'''


      if i < 0:
            raise ValueError("""Input must be non-negative"""		)
      if shift < 0:
            raise ValueError("""Shift must be non-negative"""		)
      return ((i << shift) ^ (i >> (3_2 - shift))) % 2**3_2


def 			UpperCAmelCase__       (				lowerCAmelCase__     :bytes		) ->    bytes:
      '''simple docstring'''


      lowercase														=	preprocess(lowerCAmelCase__		)

      lowercase														=	[int(2**3_2 * abs(sin(i + 1		)		)		) for i in range(6_4		)]

      # Starting states
      lowercase														=	0X67_45_23_01
      lowercase														=	0XEF_CD_AB_89
      lowercase														=	0X98_BA_DC_FE
      lowercase														=	0X10_32_54_76

      lowercase														=	[
          7,
          1_2,
          1_7,
          2_2,
          7,
          1_2,
          1_7,
          2_2,
          7,
          1_2,
          1_7,
          2_2,
          7,
          1_2,
          1_7,
          2_2,
          5,
          9,
          1_4,
          2_0,
          5,
          9,
          1_4,
          2_0,
          5,
          9,
          1_4,
          2_0,
          5,
          9,
          1_4,
          2_0,
          4,
          1_1,
          1_6,
          2_3,
          4,
          1_1,
          1_6,
          2_3,
          4,
          1_1,
          1_6,
          2_3,
          4,
          1_1,
          1_6,
          2_3,
          6,
          1_0,
          1_5,
          2_1,
          6,
          1_0,
          1_5,
          2_1,
          6,
          1_0,
          1_5,
          2_1,
          6,
          1_0,
          1_5,
          2_1,
      ]

      # Process bit string in chunks, each with 16 32-char words
      for block_words in get_block_words(lowerCAmelCase__		):
            lowercase														=	aa
            lowercase														=	ba
            lowercase														=	ca
            lowercase														=	da

            # Hash current chunk
            for i in range(6_4		):
                  if i <= 1_5:
                        # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                        lowercase														=	d ^ (b & (c ^ d))
                        lowercase														=	i
                  elif i <= 3_1:
                        # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                        lowercase														=	c ^ (d & (b ^ c))
                        lowercase														=	(5 * i + 1) % 1_6
                  elif i <= 4_7:
                        lowercase														=	b ^ c ^ d
                        lowercase														=	(3 * i + 5) % 1_6
                  else:
                        lowercase														=	c ^ (b | not_aa(lowerCAmelCase__		))
                        lowercase														=	(7 * i) % 1_6
                  lowercase														=	(f + a + added_consts[i] + block_words[g]) % 2**3_2
                  lowercase														=	d
                  lowercase														=	c
                  lowercase														=	b
                  lowercase														=	sum_aa(lowerCAmelCase__					,		left_rotate_aa(lowerCAmelCase__					,		shift_amounts[i]		)		)

            # Add hashed chunk to running total
            lowercase														=	sum_aa(lowerCAmelCase__					,		lowerCAmelCase__		)
            lowercase														=	sum_aa(lowerCAmelCase__					,		lowerCAmelCase__		)
            lowercase														=	sum_aa(lowerCAmelCase__					,		lowerCAmelCase__		)
            lowercase														=	sum_aa(lowerCAmelCase__					,		lowerCAmelCase__		)

      lowercase														=	reformat_hex(lowerCAmelCase__		) + reformat_hex(lowerCAmelCase__		) + reformat_hex(lowerCAmelCase__		) + reformat_hex(lowerCAmelCase__		)
      return digest


if __name__ == "__main__":
       import doctest

       doctest.testmod()




"""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						:

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

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

            torch.manual_seed(0						)
            lowercase														=	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						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	IFWatermarker()

            return {
                "text_encoder": text_encoder,
                "tokenizer": tokenizer,
                "unet": unet,
                "scheduler": scheduler,
                "watermarker": watermarker,
                "safety_checker": None,
                "feature_extractor": None,
            }

      def 				A__       (						self						):







            """simple docstring"""



            torch.manual_seed(0						)
            lowercase														=	TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5"""						)

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

            torch.manual_seed(0						)
            lowercase														=	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_1_4		,       time_embedding_act_fn="""gelu"""		,       time_embedding_dim=32		,       )
            unet.set_attn_processor(AttnAddedKVProcessor()						)  # For reproducibility tests

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       thresholding=__lowerCAmelCase		,       dynamic_thresholding_ratio=0.9_5		,       sample_max_value=1.0		,       prediction_type="""epsilon"""		,       variance_type="""learned_range"""		,       )

            torch.manual_seed(0						)
            lowercase														=	DDPMScheduler(
                num_train_timesteps=1000		,       beta_schedule="""squaredcos_cap_v2"""		,       beta_start=0.0_0_0_1		,       beta_end=0.0_2		,       )

            torch.manual_seed(0						)
            lowercase														=	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 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""prompt"""]
            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            if "image" in inputs:
                  lowercase														=	inputs["""image"""]
            else:
                  lowercase														=	None

            if "mask_image" in inputs:
                  lowercase														=	inputs["""mask_image"""]
            else:
                  lowercase														=	None

            if "original_image" in inputs:
                  lowercase														=	inputs["""original_image"""]
            else:
                  lowercase														=	None

            lowercase							,							lowercase														=	pipe.encode_prompt(__lowerCAmelCase						)

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """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:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

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

            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	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.'		,       )

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)

            lowercase														=	inputs["""generator"""]
            lowercase														=	inputs["""num_inference_steps"""]
            lowercase														=	inputs["""output_type"""]

            # inputs with prompt converted to embeddings
            lowercase														=	{
                """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:
                  lowercase														=	image

            if mask_image is not None:
                  lowercase														=	mask_image

            if original_image is not None:
                  lowercase														=	original_image

            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

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







      def 				A__       (						self						):







            """simple docstring"""



            lowercase														=	self.get_dummy_components()
            lowercase														=	self.pipeline_class(**__lowerCAmelCase						)
            pipe.to(__lowerCAmelCase						)
            pipe.set_progress_bar_config(disable=__lowerCAmelCase						)

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe(**__lowerCAmelCase						)[0]

            with tempfile.TemporaryDirectory() as tmpdir:
                  pipe.save_pretrained(__lowerCAmelCase						)
                  lowercase														=	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

            lowercase														=	self.get_dummy_inputs(__lowerCAmelCase						)
            lowercase														=	pipe_loaded(**__lowerCAmelCase						)[0]

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



