

'''simple docstring'''







from typing import Optional, Tuple, Union

import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict

from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
    FlaxCrossAttnDownBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
)


@flax.struct.dataclass
class 					A__      (      _snake_case			):
    lowercase						=		42
    lowercase						=		42


class 					A__      (      nn.Module			):
    lowercase						=		42
    lowercase						=		(16, 32, 96, 256)
    lowercase						=		jnp.floataa



    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      nn.Conv(
               self.block_out_channels[0]		,				kernel_size=(3, 3)		,				padding=((1, 1), (1, 1))		,				dtype=self.dtype		,				)

           A_						=      []
           for i in range(len(self.block_out_channels       ) - 1       ):
                  A_						=      self.block_out_channels[i]
                  A_						=      self.block_out_channels[i + 1]
                  A_						=      nn.Conv(
                      UpperCamelCase__		,				kernel_size=(3, 3)		,				padding=((1, 1), (1, 1))		,				dtype=self.dtype		,				)
                  blocks.append(UpperCamelCase__       )
                  A_						=      nn.Conv(
                      UpperCamelCase__		,				kernel_size=(3, 3)		,				strides=(2, 2)		,				padding=((1, 1), (1, 1))		,				dtype=self.dtype		,				)
                  blocks.append(UpperCamelCase__       )
           A_						=      blocks

           A_						=      nn.Conv(
               self.conditioning_embedding_channels		,				kernel_size=(3, 3)		,				padding=((1, 1), (1, 1))		,				kernel_init=nn.initializers.zeros_init()		,				bias_init=nn.initializers.zeros_init()		,				dtype=self.dtype		,				)







    def __call__(					self		,				UpperCamelCase__       )						->      Optional[int]:





           '''simple docstring'''
           A_						=      self.conv_in(UpperCamelCase__       )
           A_						=      nn.silu(UpperCamelCase__       )

           for block in self.blocks:
                  A_						=      block(UpperCamelCase__       )
                  A_						=      nn.silu(UpperCamelCase__       )

           A_						=      self.conv_out(UpperCamelCase__       )

           return embedding


@flax_register_to_config
class 					A__      (      nn.Module						,						_snake_case						,						_snake_case			):
    lowercase						=		32
    lowercase						=		4
    lowercase						=		(
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "CrossAttnDownBlock2D",
        "DownBlock2D",
    )
    lowercase						=		False
    lowercase						=		(320, 640, 1_280, 1_280)
    lowercase						=		2
    lowercase						=		8
    lowercase						=		None
    lowercase						=		1_280
    lowercase						=		0.0
    lowercase						=		False
    lowercase						=		jnp.floataa
    lowercase						=		True
    lowercase						=		0
    lowercase						=		"rgb"
    lowercase						=		(16, 32, 96, 256)



    def 					snake_case_			(					self		,				UpperCamelCase__       )						->      FrozenDict:





           '''simple docstring'''
           A_						=      (1, self.in_channels, self.sample_size, self.sample_size)
           A_						=      jnp.zeros(UpperCamelCase__		,				dtype=jnp.floataa       )
           A_						=      jnp.ones((1,)		,				dtype=jnp.intaa       )
           A_						=      jnp.zeros((1, 1, self.cross_attention_dim)		,				dtype=jnp.floataa       )
           A_						=      (1, 3, self.sample_size * 8, self.sample_size * 8)
           A_						=      jnp.zeros(UpperCamelCase__		,				dtype=jnp.floataa       )

           A_					,			A_						=      jax.random.split(UpperCamelCase__       )
           A_						=      {"""params""": params_rng, """dropout""": dropout_rng}

           return self.init(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__       )["params"]



    def 					snake_case_			(					self       )						->      str:





           '''simple docstring'''
           A_						=      self.block_out_channels
           A_						=      block_out_channels[0] * 4

           # If `num_attention_heads` is not defined (which is the case for most models)
           # it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
           # The reason for this behavior is to correct for incorrectly named variables that were introduced
           # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
           # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
           # which is why we correct for the naming here.
           A_						=      self.num_attention_heads or self.attention_head_dim

           # input
           A_						=      nn.Conv(
               block_out_channels[0]		,				kernel_size=(3, 3)		,				strides=(1, 1)		,				padding=((1, 1), (1, 1))		,				dtype=self.dtype		,				)

           # time
           A_						=      FlaxTimesteps(
               block_out_channels[0]		,				flip_sin_to_cos=self.flip_sin_to_cos		,				freq_shift=self.config.freq_shift       )
           A_						=      FlaxTimestepEmbedding(UpperCamelCase__		,				dtype=self.dtype       )

           A_						=      FlaxControlNetConditioningEmbedding(
               conditioning_embedding_channels=block_out_channels[0]		,				block_out_channels=self.conditioning_embedding_out_channels		,				)

           A_						=      self.only_cross_attention
           if isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
                  A_						=      (only_cross_attention,) * len(self.down_block_types       )

           if isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
                  A_						=      (num_attention_heads,) * len(self.down_block_types       )

           # down
           A_						=      []
           A_						=      []

           A_						=      block_out_channels[0]

           A_						=      nn.Conv(
               UpperCamelCase__		,				kernel_size=(1, 1)		,				padding="""VALID"""		,				kernel_init=nn.initializers.zeros_init()		,				bias_init=nn.initializers.zeros_init()		,				dtype=self.dtype		,				)
           controlnet_down_blocks.append(UpperCamelCase__       )

           for i, down_block_type in enumerate(self.down_block_types       ):
                  A_						=      output_channel
                  A_						=      block_out_channels[i]
                  A_						=      i == len(UpperCamelCase__       ) - 1

                  if down_block_type == "CrossAttnDownBlock2D":
                         A_						=      FlaxCrossAttnDownBlockaD(
                             in_channels=UpperCamelCase__		,				out_channels=UpperCamelCase__		,				dropout=self.dropout		,				num_layers=self.layers_per_block		,				num_attention_heads=num_attention_heads[i]		,				add_downsample=not is_final_block		,				use_linear_projection=self.use_linear_projection		,				only_cross_attention=only_cross_attention[i]		,				dtype=self.dtype		,				)
                  else:
                         A_						=      FlaxDownBlockaD(
                             in_channels=UpperCamelCase__		,				out_channels=UpperCamelCase__		,				dropout=self.dropout		,				num_layers=self.layers_per_block		,				add_downsample=not is_final_block		,				dtype=self.dtype		,				)

                  down_blocks.append(UpperCamelCase__       )

                  for _ in range(self.layers_per_block       ):
                         A_						=      nn.Conv(
                             UpperCamelCase__		,				kernel_size=(1, 1)		,				padding="""VALID"""		,				kernel_init=nn.initializers.zeros_init()		,				bias_init=nn.initializers.zeros_init()		,				dtype=self.dtype		,				)
                         controlnet_down_blocks.append(UpperCamelCase__       )

                  if not is_final_block:
                         A_						=      nn.Conv(
                             UpperCamelCase__		,				kernel_size=(1, 1)		,				padding="""VALID"""		,				kernel_init=nn.initializers.zeros_init()		,				bias_init=nn.initializers.zeros_init()		,				dtype=self.dtype		,				)
                         controlnet_down_blocks.append(UpperCamelCase__       )

           A_						=      down_blocks
           A_						=      controlnet_down_blocks

           # mid
           A_						=      block_out_channels[-1]
           A_						=      FlaxUNetMidBlockaDCrossAttn(
               in_channels=UpperCamelCase__		,				dropout=self.dropout		,				num_attention_heads=num_attention_heads[-1]		,				use_linear_projection=self.use_linear_projection		,				dtype=self.dtype		,				)

           A_						=      nn.Conv(
               UpperCamelCase__		,				kernel_size=(1, 1)		,				padding="""VALID"""		,				kernel_init=nn.initializers.zeros_init()		,				bias_init=nn.initializers.zeros_init()		,				dtype=self.dtype		,				)







    def __call__(					self		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__ = 1.0		,				UpperCamelCase__ = True		,				UpperCamelCase__ = False		,				)						->      Union[FlaxControlNetOutput, Tuple]:





           '''simple docstring'''
           A_						=      self.controlnet_conditioning_channel_order
           if channel_order == "bgr":
                  A_						=      jnp.flip(UpperCamelCase__		,				axis=1       )

           # 1. time
           if not isinstance(UpperCamelCase__		,				jnp.ndarray       ):
                  A_						=      jnp.array([timesteps]		,				dtype=jnp.intaa       )
           elif isinstance(UpperCamelCase__		,				jnp.ndarray       ) and len(timesteps.shape       ) == 0:
                  A_						=      timesteps.astype(dtype=jnp.floataa       )
                  A_						=      jnp.expand_dims(UpperCamelCase__		,				0       )

           A_						=      self.time_proj(UpperCamelCase__       )
           A_						=      self.time_embedding(UpperCamelCase__       )

           # 2. pre-process
           A_						=      jnp.transpose(UpperCamelCase__		,				(0, 2, 3, 1)       )
           A_						=      self.conv_in(UpperCamelCase__       )

           A_						=      jnp.transpose(UpperCamelCase__		,				(0, 2, 3, 1)       )
           A_						=      self.controlnet_cond_embedding(UpperCamelCase__       )
           sample += controlnet_cond

           # 3. down
           A_						=      (sample,)
           for down_block in self.down_blocks:
                  if isinstance(UpperCamelCase__		,				UpperCamelCase__       ):
                         A_					,			A_						=      down_block(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				deterministic=not train       )
                  else:
                         A_					,			A_						=      down_block(UpperCamelCase__		,				UpperCamelCase__		,				deterministic=not train       )
                  down_block_res_samples += res_samples

           # 4. mid
           A_						=      self.mid_block(UpperCamelCase__		,				UpperCamelCase__		,				UpperCamelCase__		,				deterministic=not train       )

           # 5. contronet blocks
           A_						=      ()
           for down_block_res_sample, controlnet_block in zip(UpperCamelCase__		,				self.controlnet_down_blocks       ):
                  A_						=      controlnet_block(UpperCamelCase__       )
                  controlnet_down_block_res_samples += (down_block_res_sample,)

           A_						=      controlnet_down_block_res_samples

           A_						=      self.controlnet_mid_block(UpperCamelCase__       )

           # 6. scaling
           A_						=      [sample * conditioning_scale for sample in down_block_res_samples]
           mid_block_res_sample *= conditioning_scale

           if not return_dict:
                  return (down_block_res_samples, mid_block_res_sample)

           return FlaxControlNetOutput(
               down_block_res_samples=UpperCamelCase__		,				mid_block_res_sample=UpperCamelCase__       )



'''simple docstring'''







import os

__lowerCamelCase		       =    {'''I''': 1, '''V''': 5, '''X''': 10, '''L''': 50, '''C''': 100, '''D''': 500, '''M''': 1000}
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			int:
       A_						=      0

       A_						=      0
       while index < len(UpperCAmelCase__				) - 1:
              A_						=      SYMBOLS[numerals[index]]
              A_						=      SYMBOLS[numerals[index + 1]]
              if current_value < next_value:
                     total_value -= current_value
              else:
                     total_value += current_value
              index += 1
       total_value += SYMBOLS[numerals[index]]

       return total_value
def       UpperCAmelCase__    (      UpperCAmelCase__				)			->			str:
       A_						=      """"""

       A_						=      num // 10_00
       numerals += m_count * "M"
       num %= 10_00

       A_						=      num // 1_00
       if c_count == 9:
              numerals += "CM"
              c_count -= 9
       elif c_count == 4:
              numerals += "CD"
              c_count -= 4
       if c_count >= 5:
              numerals += "D"
              c_count -= 5
       numerals += c_count * "C"
       num %= 1_00

       A_						=      num // 10
       if x_count == 9:
              numerals += "XC"
              x_count -= 9
       elif x_count == 4:
              numerals += "XL"
              x_count -= 4
       if x_count >= 5:
              numerals += "L"
              x_count -= 5
       numerals += x_count * "X"
       num %= 10

       if num == 9:
              numerals += "IX"
              num -= 9
       elif num == 4:
              numerals += "IV"
              num -= 4
       if num >= 5:
              numerals += "V"
              num -= 5
       numerals += num * "I"

       return numerals
def       UpperCAmelCase__    (      UpperCAmelCase__ = "/p089_roman.txt"				)			->			int:
       A_						=      0

       with open(os.path.dirname(UpperCAmelCase__				) + roman_numerals_filename				) as filea:
              A_						=      filea.readlines()

       for line in lines:
              A_						=      line.strip()
              A_						=      parse_roman_numerals(UpperCAmelCase__				)
              A_						=      generate_roman_numerals(UpperCAmelCase__				)
              savings += len(UpperCAmelCase__				) - len(UpperCAmelCase__				)

       return savings


if __name__ == "__main__":
      print(f"""{solution() = }""")
