

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 						__magic_name__			(							lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	jnp.ndarray
   lowerCamelCase__ :	jnp.ndarray







class 						__magic_name__			(							nn.Module							):

   '''simple docstring'''




   lowerCamelCase__ :	int
   lowerCamelCase__ :	Tuple[int]    =   (16, 32, 96, 2_56)
   lowerCamelCase__ :	jnp.dtype    =   jnp.floataa



   def  _UpperCAmelCase (				self       )							->    int:






     """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(
           SCREAMING_SNAKE_CASE__,    kernel_size=(3, 3),    padding=((1, 1), (1, 1)),    dtype=self.dtype,    )
       blocks.append(SCREAMING_SNAKE_CASE__       )
       a__  =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__       )
     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,    lowercase_       )							->    Dict:






     """simple docstring"""






     a__  =self.conv_in(SCREAMING_SNAKE_CASE__       )
     a__  =nn.silu(SCREAMING_SNAKE_CASE__       )

     for block in self.blocks:
       a__  =block(SCREAMING_SNAKE_CASE__       )
       a__  =nn.silu(SCREAMING_SNAKE_CASE__       )

     a__  =self.conv_out(SCREAMING_SNAKE_CASE__       )

     return embedding







@flax_register_to_config
class 						__magic_name__			(							nn.Module		,       lowerCamelCase__		,       lowerCamelCase__							):

   '''simple docstring'''




   lowerCamelCase__ :	int    =   32
   lowerCamelCase__ :	int    =   4
   lowerCamelCase__ :	Tuple[str]    =   (
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "CrossAttnDownBlock2D",
       "DownBlock2D",
   )
   lowerCamelCase__ :	Union[bool, Tuple[bool]]    =   False
   lowerCamelCase__ :	Tuple[int]    =   (3_20, 6_40, 12_80, 12_80)
   lowerCamelCase__ :	int    =   2
   lowerCamelCase__ :	Union[int, Tuple[int]]    =   8
   lowerCamelCase__ :	Optional[Union[int, Tuple[int]]]    =   None
   lowerCamelCase__ :	int    =   12_80
   lowerCamelCase__ :	float    =   0.0
   lowerCamelCase__ :	bool    =   False
   lowerCamelCase__ :	jnp.dtype    =   jnp.floataa
   lowerCamelCase__ :	bool    =   True
   lowerCamelCase__ :	int    =   0
   lowerCamelCase__ :	str    =   "rgb"
   lowerCamelCase__ :	Tuple[int]    =   (16, 32, 96, 2_56)



   def  _UpperCAmelCase (				self,    lowercase_       )							->    FrozenDict:






     """simple docstring"""






     a__  =(1, self.in_channels, self.sample_size, self.sample_size)
     a__  =jnp.zeros(SCREAMING_SNAKE_CASE__,    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(SCREAMING_SNAKE_CASE__,    dtype=jnp.floataa       )

     a__  =jax.random.split(SCREAMING_SNAKE_CASE__       )
     a__  ={'''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  _UpperCAmelCase (				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(SCREAMING_SNAKE_CASE__,    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(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       ):
       a__  =(only_cross_attention,) * len(self.down_block_types       )

     if isinstance(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__       ):
       a__  =(num_attention_heads,) * len(self.down_block_types       )

     # down
     a__  =[]
     a__  =[]

     a__  =block_out_channels[0]

     a__  =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       ):
       a__  =output_channel
       a__  =block_out_channels[i]
       a__  =i == len(SCREAMING_SNAKE_CASE__       ) - 1

       if down_block_type == "CrossAttnDownBlock2D":
         a__  =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:
         a__  =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       ):
         a__  =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:
         a__  =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__       )

     a__  =down_blocks
     a__  =controlnet_down_blocks

     # mid
     a__  =block_out_channels[-1]
     a__  =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,    )

     a__  =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,    lowercase_,    lowercase_,    lowercase_,    lowercase_,    lowercase_ = 1.0,    lowercase_ = True,    lowercase_ = False,    )							->    Union[FlaxControlNetOutput, Tuple]:






     """simple docstring"""






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

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

     a__  =self.time_proj(SCREAMING_SNAKE_CASE__       )
     a__  =self.time_embedding(SCREAMING_SNAKE_CASE__       )

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

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

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

     # 4. mid
     a__  =self.mid_block(SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    deterministic=not train       )

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

     a__  =controlnet_down_block_res_samples

     a__  =self.controlnet_mid_block(SCREAMING_SNAKE_CASE__       )

     # 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=SCREAMING_SNAKE_CASE__,    mid_block_res_sample=SCREAMING_SNAKE_CASE__       )







"""simple docstring"""
import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
lowerCamelCase_								=		logging.getLogger(__name__)



def      __lowerCamelCase   (							)			->      int:
      __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  argparse.ArgumentParser(
          description='''Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids).'''							)
      parser.add_argument('''--file_path''' ,   type=a_ ,   default='''data/dump.txt''' ,   help='''The path to the data.'''							)
      parser.add_argument('''--tokenizer_type''' ,   type=a_ ,   default='''bert''' ,   choices=['''bert''', '''roberta''', '''gpt2''']							)
      parser.add_argument('''--tokenizer_name''' ,   type=a_ ,   default='''bert-base-uncased''' ,   help='''The tokenizer to use.'''							)
      parser.add_argument('''--dump_file''' ,   type=a_ ,   default='''data/dump''' ,   help='''The dump file prefix.'''							)
      __SCREAMING_SNAKE_CASE   :Any						=  parser.parse_args()

      logger.info(f'''Loading Tokenizer ({args.tokenizer_name})'''							)
      if args.tokenizer_type == "bert":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  BertTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''cls_token''']  # `[CLS]`
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  tokenizer.special_tokens_map['''sep_token''']  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            __SCREAMING_SNAKE_CASE   :str						=  RobertaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''cls_token''']  # `<s>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''sep_token''']  # `</s>`
      elif args.tokenizer_type == "gpt2":
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  GPTaTokenizer.from_pretrained(args.tokenizer_name							)
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  tokenizer.special_tokens_map['''bos_token''']  # `<|endoftext|>`
            __SCREAMING_SNAKE_CASE   :str						=  tokenizer.special_tokens_map['''eos_token''']  # `<|endoftext|>`

      logger.info(f'''Loading text from {args.file_path}'''							)
      with open(args.file_path ,   '''r''' ,   encoding='''utf8'''							) as fp:
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  fp.readlines()

      logger.info('''Start encoding'''							)
      logger.info(f'''{len(a_							)} examples to process.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  []
      __SCREAMING_SNAKE_CASE   :List[str]						=  0
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  1_00_00
      __SCREAMING_SNAKE_CASE   :List[Any]						=  time.time()
      for text in data:
            __SCREAMING_SNAKE_CASE   :Any						=  f'''{bos} {text.strip()} {sep}'''
            __SCREAMING_SNAKE_CASE   :int						=  tokenizer.encode(a_ ,   add_special_tokens=a_							)
            rslt.append(a_							)

            iter += 1
            if iter % interval == 0:
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
                  logger.info(f'''{iter} examples processed. - {(end-start):.2f}s/{interval}expl'''							)
                  __SCREAMING_SNAKE_CASE   :Any						=  time.time()
      logger.info('''Finished binarization'''							)
      logger.info(f'''{len(a_							)} examples processed.'''							)

      __SCREAMING_SNAKE_CASE   :Optional[int]						=  f'''{args.dump_file}.{args.tokenizer_name}.pickle'''
      __SCREAMING_SNAKE_CASE   :str						=  tokenizer.vocab_size
      if vocab_size < (1 << 16):
            __SCREAMING_SNAKE_CASE   :Union[str, Any]						=  [np.uintaa(a_							) for d in rslt]
      else:
            __SCREAMING_SNAKE_CASE   :List[Any]						=  [np.intaa(a_							) for d in rslt]
      random.shuffle(rslt_							)
      logger.info(f'''Dump to {dp_file}'''							)
      with open(a_ ,   '''wb'''							) as handle:
            pickle.dump(rslt_ ,   a_ ,   protocol=pickle.HIGHEST_PROTOCOL							)


if __name__ == "__main__":
  main()