




'''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,
    FlaxCrossAttnUpBlockaD,
    FlaxDownBlockaD,
    FlaxUNetMidBlockaDCrossAttn,
    FlaxUpBlockaD,
)

@flax.struct.dataclass
class        _a    (						lowerCamelCase__							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						42







@flax_register_to_config
class        _a    (						nn.Module , lowerCamelCase__ , lowerCamelCase__							):
     SCREAMING_SNAKE_CASE_							:   str       =						32
     SCREAMING_SNAKE_CASE_							:   Any       =						4
     SCREAMING_SNAKE_CASE_							:   List[str]       =						4
     SCREAMING_SNAKE_CASE_							:   str       =						(
         """CrossAttnDownBlock2D""",
         """CrossAttnDownBlock2D""",
         """CrossAttnDownBlock2D""",
         """DownBlock2D""",
     )
     SCREAMING_SNAKE_CASE_							:   Dict       =						("""UpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""")
     SCREAMING_SNAKE_CASE_							:   List[str]       =						False
     SCREAMING_SNAKE_CASE_							:   int       =						(3_20, 6_40, 12_80, 12_80)
     SCREAMING_SNAKE_CASE_							:   Any       =						2
     SCREAMING_SNAKE_CASE_							:   Any       =						8
     SCREAMING_SNAKE_CASE_							:   Tuple       =						None
     SCREAMING_SNAKE_CASE_							:   int       =						12_80
     SCREAMING_SNAKE_CASE_							:   Any       =						0.0
     SCREAMING_SNAKE_CASE_							:   Dict       =						False
     SCREAMING_SNAKE_CASE_							:   List[str]       =						jnp.floataa
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						True
     SCREAMING_SNAKE_CASE_							:   int       =						0
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						False






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          # init input tensors
          _snake_case							=				(1, self.in_channels, self.sample_size, self.sample_size)
          _snake_case							=				jnp.zeros(__snake_case ,dtype=jnp.floataa   )
          _snake_case							=				jnp.ones((1,) ,dtype=jnp.intaa   )
          _snake_case							=				jnp.zeros((1, 1, self.cross_attention_dim) ,dtype=jnp.floataa   )

          _snake_case							=				jax.random.split(__snake_case   )
          _snake_case							=				{'params': params_rng, 'dropout': dropout_rng}

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






     def     _lowercase     (			self   )							->      str:
          _snake_case							=				self.block_out_channels
          _snake_case							=				block_out_channels[0] * 4

          if self.num_attention_heads is not None:
               raise ValueError(
                   "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19."   )

          # 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.
          _snake_case							=				self.num_attention_heads or self.attention_head_dim

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

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

          _snake_case							=				self.only_cross_attention
          if isinstance(__snake_case ,__snake_case   ):
               _snake_case							=				(only_cross_attention,) * len(self.down_block_types   )

          if isinstance(__snake_case ,__snake_case   ):
               _snake_case							=				(num_attention_heads,) * len(self.down_block_types   )

          # down
          _snake_case							=				[]
          _snake_case							=				block_out_channels[0]
          for i, down_block_type in enumerate(self.down_block_types   ):
               _snake_case							=				output_channel
               _snake_case							=				block_out_channels[i]
               _snake_case							=				i == len(__snake_case   ) - 1

               if down_block_type == "CrossAttnDownBlock2D":
                    _snake_case							=				FlaxCrossAttnDownBlockaD(
                        in_channels=__snake_case ,out_channels=__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] ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
               else:
                    _snake_case							=				FlaxDownBlockaD(
                        in_channels=__snake_case ,out_channels=__snake_case ,dropout=self.dropout ,num_layers=self.layers_per_block ,add_downsample=not is_final_block ,dtype=self.dtype ,)

               down_blocks.append(__snake_case   )
          _snake_case							=				down_blocks

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

          # up
          _snake_case							=				[]
          _snake_case							=				list(reversed(__snake_case   )   )
          _snake_case							=				list(reversed(__snake_case   )   )
          _snake_case							=				list(reversed(__snake_case   )   )
          _snake_case							=				reversed_block_out_channels[0]
          for i, up_block_type in enumerate(self.up_block_types   ):
               _snake_case							=				output_channel
               _snake_case							=				reversed_block_out_channels[i]
               _snake_case							=				reversed_block_out_channels[min(i + 1 ,len(__snake_case   ) - 1   )]

               _snake_case							=				i == len(__snake_case   ) - 1

               if up_block_type == "CrossAttnUpBlock2D":
                    _snake_case							=				FlaxCrossAttnUpBlockaD(
                        in_channels=__snake_case ,out_channels=__snake_case ,prev_output_channel=__snake_case ,num_layers=self.layers_per_block + 1 ,num_attention_heads=reversed_num_attention_heads[i] ,add_upsample=not is_final_block ,dropout=self.dropout ,use_linear_projection=self.use_linear_projection ,only_cross_attention=only_cross_attention[i] ,use_memory_efficient_attention=self.use_memory_efficient_attention ,dtype=self.dtype ,)
               else:
                    _snake_case							=				FlaxUpBlockaD(
                        in_channels=__snake_case ,out_channels=__snake_case ,prev_output_channel=__snake_case ,num_layers=self.layers_per_block + 1 ,add_upsample=not is_final_block ,dropout=self.dropout ,dtype=self.dtype ,)

               up_blocks.append(__snake_case   )
               _snake_case							=				output_channel
          _snake_case							=				up_blocks

          # out
          _snake_case							=				nn.GroupNorm(num_groups=32 ,epsilon=1e-5   )
          _snake_case							=				nn.Conv(
              self.out_channels ,kernel_size=(3, 3) ,strides=(1, 1) ,padding=((1, 1), (1, 1)) ,dtype=self.dtype ,)






     def __call__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE=None ,_SCREAMING_SNAKE_CASE = True ,_SCREAMING_SNAKE_CASE = False ,)							->      str:
          # 1. time
          if not isinstance(__snake_case ,jnp.ndarray   ):
               _snake_case							=				jnp.array([timesteps] ,dtype=jnp.intaa   )
          elif isinstance(__snake_case ,jnp.ndarray   ) and len(timesteps.shape   ) == 0:
               _snake_case							=				timesteps.astype(dtype=jnp.floataa   )
               _snake_case							=				jnp.expand_dims(__snake_case ,0   )

          _snake_case							=				self.time_proj(__snake_case   )
          _snake_case							=				self.time_embedding(__snake_case   )

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

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

          if down_block_additional_residuals is not None:
               _snake_case							=				()

               for down_block_res_sample, down_block_additional_residual in zip(
                   __snake_case ,__snake_case   ):
                    down_block_res_sample += down_block_additional_residual
                    new_down_block_res_samples += (down_block_res_sample,)

               _snake_case							=				new_down_block_res_samples

          # 4. mid
          _snake_case							=				self.mid_block(__snake_case ,__snake_case ,__snake_case ,deterministic=not train   )

          if mid_block_additional_residual is not None:
               sample += mid_block_additional_residual

          # 5. up
          for up_block in self.up_blocks:
               _snake_case							=				down_block_res_samples[-(self.layers_per_block + 1) :]
               _snake_case							=				down_block_res_samples[: -(self.layers_per_block + 1)]
               if isinstance(__snake_case ,__snake_case   ):
                    _snake_case							=				up_block(
                        __snake_case ,temb=__snake_case ,encoder_hidden_states=__snake_case ,res_hidden_states_tuple=__snake_case ,deterministic=not train ,)
               else:
                    _snake_case							=				up_block(__snake_case ,temb=__snake_case ,res_hidden_states_tuple=__snake_case ,deterministic=not train   )

               # 6. post-process
          _snake_case							=				self.conv_norm_out(__snake_case   )
          _snake_case							=				nn.silu(__snake_case   )
          _snake_case							=				self.conv_out(__snake_case   )
          _snake_case							=				jnp.transpose(__snake_case ,(0, 3, 1, 2)   )

          if not return_dict:
               return (sample,)

          return FlaxUNetaDConditionOutput(sample=__snake_case   )








'''simple docstring'''





import fire

from utils import calculate_rouge, save_json





def      __a     (       _UpperCamelCase:   Tuple ,						_UpperCamelCase:   Optional[int] ,						_UpperCamelCase:   Optional[int]=None ,						**_UpperCamelCase:   Any   )						->			Optional[Any]:

     """simple docstring"""




     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()]
     _snake_case							=				[x.strip() for x in open(_UpperCamelCase   ).readlines()][: len(_UpperCamelCase   )]
     _snake_case							=				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)


