






'''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_		(    lowerCamelCase       ):
  lowercase  	=   42
  lowercase  	=   42






class        a_		(    nn.Module       ):
  lowercase  	=   42
  lowercase  	=   (16, 32, 96, 2_56)
  lowercase  	=   jnp.floataa

  def 						A__		(   self   )						->   Any:



        """simple docstring"""






        UpperCamelCase       						=      nn.Conv(
            self.block_out_channels[0]    ,	kernel_size=(3, 3)    ,	padding=((1, 1), (1, 1))    ,	dtype=self.dtype    ,	)

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

        UpperCamelCase       						=      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    ,	_SCREAMING_SNAKE_CASE   )						->   Union[str, Any]:



        """simple docstring"""






        UpperCamelCase       						=      self.conv_in(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      nn.silu(_SCREAMING_SNAKE_CASE   )

        for block in self.blocks:
              UpperCamelCase       						=      block(_SCREAMING_SNAKE_CASE   )
              UpperCamelCase       						=      nn.silu(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      self.conv_out(_SCREAMING_SNAKE_CASE   )

        return embedding






@flax_register_to_config
class        a_		(    nn.Module			, lowerCamelCase			, lowerCamelCase       ):
  lowercase  	=   32
  lowercase  	=   4
  lowercase  	=   (
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "CrossAttnDownBlock2D",
      "DownBlock2D",
  )
  lowercase  	=   False
  lowercase  	=   (3_20, 6_40, 12_80, 12_80)
  lowercase  	=   2
  lowercase  	=   8
  lowercase  	=   None
  lowercase  	=   12_80
  lowercase  	=   0.0
  lowercase  	=   False
  lowercase  	=   jnp.floataa
  lowercase  	=   True
  lowercase  	=   0
  lowercase  	=   "rgb"
  lowercase  	=   (16, 32, 96, 2_56)

  def 						A__		(   self    ,	_SCREAMING_SNAKE_CASE   )						->   FrozenDict:



        """simple docstring"""






        UpperCamelCase       						=      (1, self.in_channels, self.sample_size, self.sample_size)
        UpperCamelCase       						=      jnp.zeros(_SCREAMING_SNAKE_CASE    ,	dtype=jnp.floataa   )
        UpperCamelCase       						=      jnp.ones((1,)    ,	dtype=jnp.intaa   )
        UpperCamelCase       						=      jnp.zeros((1, 1, self.cross_attention_dim)    ,	dtype=jnp.floataa   )
        UpperCamelCase       						=      (1, 3, self.sample_size * 8, self.sample_size * 8)
        UpperCamelCase       						=      jnp.zeros(_SCREAMING_SNAKE_CASE    ,	dtype=jnp.floataa   )

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

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

  def 						A__		(   self   )						->   Dict:



        """simple docstring"""






        UpperCamelCase       						=      self.block_out_channels
        UpperCamelCase       						=      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.
        UpperCamelCase       						=      self.num_attention_heads or self.attention_head_dim

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

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

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

        UpperCamelCase       						=      self.only_cross_attention
        if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      (only_cross_attention,) * len(self.down_block_types   )

        if isinstance(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE   ):
              UpperCamelCase       						=      (num_attention_heads,) * len(self.down_block_types   )

        # down
        UpperCamelCase       						=      []
        UpperCamelCase       						=      []

        UpperCamelCase       						=      block_out_channels[0]

        UpperCamelCase       						=      nn.Conv(
            _SCREAMING_SNAKE_CASE    ,	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(_SCREAMING_SNAKE_CASE   )

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

              if down_block_type == "CrossAttnDownBlock2D":
                    UpperCamelCase       						=      FlaxCrossAttnDownBlockaD(
                        in_channels=_SCREAMING_SNAKE_CASE    ,	out_channels=_SCREAMING_SNAKE_CASE    ,	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:
                    UpperCamelCase       						=      FlaxDownBlockaD(
                        in_channels=_SCREAMING_SNAKE_CASE    ,	out_channels=_SCREAMING_SNAKE_CASE    ,	dropout=self.dropout    ,	num_layers=self.layers_per_block    ,	add_downsample=not is_final_block    ,	dtype=self.dtype    ,	)

              down_blocks.append(_SCREAMING_SNAKE_CASE   )

              for _ in range(self.layers_per_block   ):
                    UpperCamelCase       						=      nn.Conv(
                        _SCREAMING_SNAKE_CASE    ,	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(_SCREAMING_SNAKE_CASE   )

              if not is_final_block:
                    UpperCamelCase       						=      nn.Conv(
                        _SCREAMING_SNAKE_CASE    ,	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(_SCREAMING_SNAKE_CASE   )

        UpperCamelCase       						=      down_blocks
        UpperCamelCase       						=      controlnet_down_blocks

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

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


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



        """simple docstring"""






        UpperCamelCase       						=      self.controlnet_conditioning_channel_order
        if channel_order == "bgr":
              UpperCamelCase       						=      jnp.flip(_SCREAMING_SNAKE_CASE    ,	axis=1   )

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

        UpperCamelCase       						=      self.time_proj(_SCREAMING_SNAKE_CASE   )
        UpperCamelCase       						=      self.time_embedding(_SCREAMING_SNAKE_CASE   )

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

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

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

        # 4. mid
        UpperCamelCase       						=      self.mid_block(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	deterministic=not train   )

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

        UpperCamelCase       						=      controlnet_down_block_res_samples

        UpperCamelCase       						=      self.controlnet_mid_block(_SCREAMING_SNAKE_CASE   )

        # 6. scaling
        UpperCamelCase       						=      [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=_SCREAMING_SNAKE_CASE    ,	mid_block_res_sample=_SCREAMING_SNAKE_CASE   )








'''simple docstring'''

import fire

from utils import calculate_rouge, save_json







def 					lowercase__				(   __UpperCamelCase	,		__UpperCamelCase	,		__UpperCamelCase=None	,		**__UpperCamelCase					)->				int:
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()]
      UpperCamelCase       						=      [x.strip() for x in open(__UpperCamelCase					).readlines()][: len(__UpperCamelCase					)]
      UpperCamelCase       						=      calculate_rouge(__UpperCamelCase	,		__UpperCamelCase	,		**__UpperCamelCase					)
      if save_path is not None:
            save_json(__UpperCamelCase	,		__UpperCamelCase	,		indent=__UpperCamelCase					)
      return metrics  # these print nicely


if __name__ == "__main__":
     fire.Fire(calculate_rouge_path)
