



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 							lowerCAmelCase_						(    a__				):
       UpperCAmelCase__							:   jnp.ndarray
       UpperCAmelCase__							:   jnp.ndarray






class 							lowerCAmelCase_						(    nn.Module				):
       UpperCAmelCase__							:   int
       UpperCAmelCase__							:   Tuple[int]  	=    (16, 32, 96, 256)
       UpperCAmelCase__							:   jnp.dtype  	=    jnp.floataa
       def    snake_case_			(     self			)	->						Optional[Any]:
          UpperCamelCase       :    Union[str, Any]		      =      nn.Conv(
              self.block_out_channels[0],     kernel_size=(3, 3),     padding=((1, 1), (1, 1)),     dtype=self.dtype,     )

          UpperCamelCase       :    str		      =      []
          for i in range(len(self.block_out_channels			) - 1			):
             UpperCamelCase       :    Optional[Any]		      =      self.block_out_channels[i]
             UpperCamelCase       :    Optional[Any]		      =      self.block_out_channels[i + 1]
             UpperCamelCase       :    List[Any]		      =      nn.Conv(
                 SCREAMING_SNAKE_CASE_,     kernel_size=(3, 3),     padding=((1, 1), (1, 1)),     dtype=self.dtype,     )
             blocks.append(SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    int		      =      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       :    Tuple		      =      blocks

          UpperCamelCase       :    List[Any]		      =      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_			)	->						Dict:
          UpperCamelCase       :    Optional[int]		      =      self.conv_in(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      nn.silu(SCREAMING_SNAKE_CASE_			)

          for block in self.blocks:
             UpperCamelCase       :    Any		      =      block(SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    Optional[Any]		      =      nn.silu(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Union[str, Any]		      =      self.conv_out(SCREAMING_SNAKE_CASE_			)

          return embedding






@flax_register_to_config
class 							lowerCAmelCase_						(    nn.Module			,       a__			,       a__				):
       UpperCAmelCase__							:   int  	=    32
       UpperCAmelCase__							:   int  	=    4
       UpperCAmelCase__							:   Tuple[str]  	=    (
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "CrossAttnDownBlock2D",
           "DownBlock2D",
       )
       UpperCAmelCase__							:   Union[bool, Tuple[bool]]  	=    False
       UpperCAmelCase__							:   Tuple[int]  	=    (320, 640, 1280, 1280)
       UpperCAmelCase__							:   int  	=    2
       UpperCAmelCase__							:   Union[int, Tuple[int]]  	=    8
       UpperCAmelCase__							:   Optional[Union[int, Tuple[int]]]  	=    None
       UpperCAmelCase__							:   int  	=    1280
       UpperCAmelCase__							:   float  	=    0.0
       UpperCAmelCase__							:   bool  	=    False
       UpperCAmelCase__							:   jnp.dtype  	=    jnp.floataa
       UpperCAmelCase__							:   bool  	=    True
       UpperCAmelCase__							:   int  	=    0
       UpperCAmelCase__							:   str  	=    "rgb"
       UpperCAmelCase__							:   Tuple[int]  	=    (16, 32, 96, 256)
       def    snake_case_			(     self,     SCREAMING_SNAKE_CASE_			)	->						FrozenDict:
          # init input tensors
          UpperCamelCase       :    Tuple		      =      (1, self.in_channels, self.sample_size, self.sample_size)
          UpperCamelCase       :    List[str]		      =      jnp.zeros(SCREAMING_SNAKE_CASE_,     dtype=jnp.floataa			)
          UpperCamelCase       :    List[str]		      =      jnp.ones((1,),     dtype=jnp.intaa			)
          UpperCamelCase       :    Any		      =      jnp.zeros((1, 1, self.cross_attention_dim),     dtype=jnp.floataa			)
          UpperCamelCase       :    Any		      =      (1, 3, self.sample_size * 8, self.sample_size * 8)
          UpperCamelCase       :    Union[str, Any]		      =      jnp.zeros(SCREAMING_SNAKE_CASE_,     dtype=jnp.floataa			)

          UpperCamelCase      ,	UpperCamelCase       :    Tuple		      =      jax.random.split(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    int		      =      {'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    snake_case_			(     self			)	->						Tuple:
          UpperCamelCase       :    Optional[int]		      =      self.block_out_channels
          UpperCamelCase       :    Optional[int]		      =      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       :    List[str]		      =      self.num_attention_heads or self.attention_head_dim

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

          # time
          UpperCamelCase       :    List[str]		      =      FlaxTimesteps(
              block_out_channels[0],     flip_sin_to_cos=self.flip_sin_to_cos,     freq_shift=self.config.freq_shift			)
          UpperCamelCase       :    Optional[int]		      =      FlaxTimestepEmbedding(SCREAMING_SNAKE_CASE_,     dtype=self.dtype			)

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

          UpperCamelCase       :    str		      =      self.only_cross_attention
          if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
             UpperCamelCase       :    Dict		      =      (only_cross_attention,) * len(self.down_block_types			)

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

          # down
          UpperCamelCase       :    Optional[int]		      =      []
          UpperCamelCase       :    Any		      =      []

          UpperCamelCase       :    str		      =      block_out_channels[0]

          UpperCamelCase       :    str		      =      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       :    Any		      =      output_channel
             UpperCamelCase       :    Optional[Any]		      =      block_out_channels[i]
             UpperCamelCase       :    int		      =      i == len(SCREAMING_SNAKE_CASE_			) - 1

             if down_block_type == "CrossAttnDownBlock2D":
                UpperCamelCase       :    Tuple		      =      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       :    List[str]		      =      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       :    Dict		      =      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       :    str		      =      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       :    Any		      =      down_blocks
          UpperCamelCase       :    Any		      =      controlnet_down_blocks

          # mid
          UpperCamelCase       :    Any		      =      block_out_channels[-1]
          UpperCamelCase       :    Optional[int]		      =      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       :    Union[str, Any]		      =      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]:
          UpperCamelCase       :    List[str]		      =      self.controlnet_conditioning_channel_order
          if channel_order == "bgr":
             UpperCamelCase       :    int		      =      jnp.flip(SCREAMING_SNAKE_CASE_,     axis=1			)

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

          UpperCamelCase       :    Optional[Any]		      =      self.time_proj(SCREAMING_SNAKE_CASE_			)
          UpperCamelCase       :    Tuple		      =      self.time_embedding(SCREAMING_SNAKE_CASE_			)

          # 2. pre-process
          UpperCamelCase       :    Tuple		      =      jnp.transpose(SCREAMING_SNAKE_CASE_,     (0, 2, 3, 1)			)
          UpperCamelCase       :    List[Any]		      =      self.conv_in(SCREAMING_SNAKE_CASE_			)

          UpperCamelCase       :    Union[str, Any]		      =      jnp.transpose(SCREAMING_SNAKE_CASE_,     (0, 2, 3, 1)			)
          UpperCamelCase       :    List[Any]		      =      self.controlnet_cond_embedding(SCREAMING_SNAKE_CASE_			)
          sample += controlnet_cond

          # 3. down
          UpperCamelCase       :    List[Any]		      =      (sample,)
          for down_block in self.down_blocks:
             if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
                UpperCamelCase      ,	UpperCamelCase       :    Optional[int]		      =      down_block(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     deterministic=not train			)
             else:
                UpperCamelCase      ,	UpperCamelCase       :    List[str]		      =      down_block(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     deterministic=not train			)
             down_block_res_samples += res_samples

          # 4. mid
          UpperCamelCase       :    List[Any]		      =      self.mid_block(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     deterministic=not train			)

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

          UpperCamelCase       :    Dict		      =      controlnet_down_block_res_samples

          UpperCamelCase       :    Optional[int]		      =      self.controlnet_mid_block(SCREAMING_SNAKE_CASE_			)

          # 6. scaling
          UpperCamelCase       :    List[str]		      =      [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_			)




from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup




def    lowerCAmelCase_  (		lowercase:				str = ""		)     ->					dict[str, float]:







 '''simple docstring'''
 _UpperCamelCase:  Tuple    						=			url or '''https://www.imdb.com/chart/top/?ref_=nv_mv_250'''
 _UpperCamelCase:  Union[str, Any]    						=			BeautifulSoup(requests.get(lowercase		).text	,       '''html.parser'''		)
 _UpperCamelCase:  List[Any]    						=			soup.find_all('''td'''	,       attrs='''titleColumn'''		)
 _UpperCamelCase:  str    						=			soup.find_all('''td'''	,       class_='''ratingColumn imdbRating'''		)
 return {
     title.a.text: float(rating.strong.text		)
     for title, rating in zip(lowercase	,       lowercase		)
 }




def    lowerCAmelCase_  (		lowercase:				str = "IMDb_Top_250_Movies.csv"		)     ->					None:







 '''simple docstring'''
 _UpperCamelCase:  Any    						=			get_imdb_top_aaa_movies()
 with open(lowercase	,       '''w'''	,       newline=''''''		) as out_file:
  _UpperCamelCase:  Optional[Any]    						=			csv.writer(lowercase		)
  writer.writerow(['''Movie title''', '''IMDb rating''']		)
  for title, rating in movies.items():
   writer.writerow([title, rating]		)


if __name__ == "__main__":
  write_movies()