
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block


@dataclass
class 			UpperCamelCase_				(  snake_case__     ):




      '''simple docstring'''







      a		:Optional[int]						 =      42


class 			UpperCamelCase_				(  snake_case__					,       snake_case__     ):




      '''simple docstring'''







      @register_to_config
      def __init__( self					,					_UpperCAmelCase = None					,					_UpperCAmelCase = 3					,					_UpperCAmelCase = 3					,					_UpperCAmelCase = False					,					_UpperCAmelCase = "positional"					,					_UpperCAmelCase = 0					,					_UpperCAmelCase = True					,					_UpperCAmelCase = ("DownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D", "AttnDownBlock2D")					,					_UpperCAmelCase = ("AttnUpBlock2D", "AttnUpBlock2D", "AttnUpBlock2D", "UpBlock2D")					,					_UpperCAmelCase = (224, 448, 672, 896)					,					_UpperCAmelCase = 2					,					_UpperCAmelCase = 1					,					_UpperCAmelCase = 1					,					_UpperCAmelCase = "conv"					,					_UpperCAmelCase = "conv"					,					_UpperCAmelCase = "silu"					,					_UpperCAmelCase = 8					,					_UpperCAmelCase = 32					,					_UpperCAmelCase = 1E-5					,					_UpperCAmelCase = "default"					,					_UpperCAmelCase = True					,					_UpperCAmelCase = None					,					_UpperCAmelCase = None					,					):
            super().__init__()

            lowerCAmelCase_						=      sample_size
            lowerCAmelCase_						=      block_out_channels[0] * 4

            # Check inputs
            if len(__SCREAMING_SNAKE_CASE) != len(__SCREAMING_SNAKE_CASE):
                  raise ValueError(
                      f'Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}.')

            if len(__SCREAMING_SNAKE_CASE) != len(__SCREAMING_SNAKE_CASE):
                  raise ValueError(
                      f'Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}.')

            # input
            lowerCAmelCase_						=      nn.Convad(__SCREAMING_SNAKE_CASE					,					block_out_channels[0]					,					kernel_size=3					,					padding=(1, 1))

            # time
            if time_embedding_type == "fourier":
                  lowerCAmelCase_						=      GaussianFourierProjection(embedding_size=block_out_channels[0]					,					scale=16)
                  lowerCAmelCase_						=      2 * block_out_channels[0]
            elif time_embedding_type == "positional":
                  lowerCAmelCase_						=      Timesteps(block_out_channels[0]					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)
                  lowerCAmelCase_						=      block_out_channels[0]

            lowerCAmelCase_						=      TimestepEmbedding(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)

            # class embedding
            if class_embed_type is None and num_class_embeds is not None:
                  lowerCAmelCase_						=      nn.Embedding(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)
            elif class_embed_type == "timestep":
                  lowerCAmelCase_						=      TimestepEmbedding(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)
            elif class_embed_type == "identity":
                  lowerCAmelCase_						=      nn.Identity(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)
            else:
                  lowerCAmelCase_						=      None

            lowerCAmelCase_						=      nn.ModuleList([])
            lowerCAmelCase_						=      None
            lowerCAmelCase_						=      nn.ModuleList([])

            # down
            lowerCAmelCase_						=      block_out_channels[0]
            for i, down_block_type in enumerate(__SCREAMING_SNAKE_CASE):
                  lowerCAmelCase_						=      output_channel
                  lowerCAmelCase_						=      block_out_channels[i]
                  lowerCAmelCase_						=      i == len(__SCREAMING_SNAKE_CASE) - 1

                  lowerCAmelCase_						=      get_down_block(
                      __SCREAMING_SNAKE_CASE					,					num_layers=__SCREAMING_SNAKE_CASE					,					in_channels=__SCREAMING_SNAKE_CASE					,					out_channels=__SCREAMING_SNAKE_CASE					,					temb_channels=__SCREAMING_SNAKE_CASE					,					add_downsample=not is_final_block					,					resnet_eps=__SCREAMING_SNAKE_CASE					,					resnet_act_fn=__SCREAMING_SNAKE_CASE					,					resnet_groups=__SCREAMING_SNAKE_CASE					,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,					downsample_padding=__SCREAMING_SNAKE_CASE					,					resnet_time_scale_shift=__SCREAMING_SNAKE_CASE					,					downsample_type=__SCREAMING_SNAKE_CASE					,					)
                  self.down_blocks.append(__SCREAMING_SNAKE_CASE)

            # mid
            lowerCAmelCase_						=      UNetMidBlockaD(
                in_channels=block_out_channels[-1]					,					temb_channels=__SCREAMING_SNAKE_CASE					,					resnet_eps=__SCREAMING_SNAKE_CASE					,					resnet_act_fn=__SCREAMING_SNAKE_CASE					,					output_scale_factor=__SCREAMING_SNAKE_CASE					,					resnet_time_scale_shift=__SCREAMING_SNAKE_CASE					,					attention_head_dim=attention_head_dim if attention_head_dim is not None else block_out_channels[-1]					,					resnet_groups=__SCREAMING_SNAKE_CASE					,					add_attention=__SCREAMING_SNAKE_CASE					,					)

            # up
            lowerCAmelCase_						=      list(reversed(__SCREAMING_SNAKE_CASE))
            lowerCAmelCase_						=      reversed_block_out_channels[0]
            for i, up_block_type in enumerate(__SCREAMING_SNAKE_CASE):
                  lowerCAmelCase_						=      output_channel
                  lowerCAmelCase_						=      reversed_block_out_channels[i]
                  lowerCAmelCase_						=      reversed_block_out_channels[min(i + 1					,					len(__SCREAMING_SNAKE_CASE) - 1)]

                  lowerCAmelCase_						=      i == len(__SCREAMING_SNAKE_CASE) - 1

                  lowerCAmelCase_						=      get_up_block(
                      __SCREAMING_SNAKE_CASE					,					num_layers=layers_per_block + 1					,					in_channels=__SCREAMING_SNAKE_CASE					,					out_channels=__SCREAMING_SNAKE_CASE					,					prev_output_channel=__SCREAMING_SNAKE_CASE					,					temb_channels=__SCREAMING_SNAKE_CASE					,					add_upsample=not is_final_block					,					resnet_eps=__SCREAMING_SNAKE_CASE					,					resnet_act_fn=__SCREAMING_SNAKE_CASE					,					resnet_groups=__SCREAMING_SNAKE_CASE					,					attention_head_dim=attention_head_dim if attention_head_dim is not None else output_channel					,					resnet_time_scale_shift=__SCREAMING_SNAKE_CASE					,					upsample_type=__SCREAMING_SNAKE_CASE					,					)
                  self.up_blocks.append(__SCREAMING_SNAKE_CASE)
                  lowerCAmelCase_						=      output_channel

            # out
            lowerCAmelCase_						=      norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4					,					32)
            lowerCAmelCase_						=      nn.GroupNorm(num_channels=block_out_channels[0]					,					num_groups=__SCREAMING_SNAKE_CASE					,					eps=__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      nn.SiLU()
            lowerCAmelCase_						=      nn.Convad(block_out_channels[0]					,					__SCREAMING_SNAKE_CASE					,					kernel_size=3					,					padding=1)







      def lowercase__		( self					,					_UpperCAmelCase					,					_UpperCAmelCase					,					_UpperCAmelCase = None					,					_UpperCAmelCase = True					,					):







            if self.config.center_input_sample:
                  lowerCAmelCase_						=      2 * sample - 1.0

            # 1. time
            lowerCAmelCase_						=      timestep
            if not torch.is_tensor(__SCREAMING_SNAKE_CASE):
                  lowerCAmelCase_						=      torch.tensor([timesteps]					,					dtype=torch.long					,					device=sample.device)
            elif torch.is_tensor(__SCREAMING_SNAKE_CASE) and len(timesteps.shape) == 0:
                  lowerCAmelCase_						=      timesteps[None].to(sample.device)

            # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
            lowerCAmelCase_						=      timesteps * torch.ones(sample.shape[0]					,					dtype=timesteps.dtype					,					device=timesteps.device)

            lowerCAmelCase_						=      self.time_proj(__SCREAMING_SNAKE_CASE)

            # timesteps does not contain any weights and will always return f32 tensors
            # but time_embedding might actually be running in fp16. so we need to cast here.
            # there might be better ways to encapsulate this.
            lowerCAmelCase_						=      t_emb.to(dtype=self.dtype)
            lowerCAmelCase_						=      self.time_embedding(__SCREAMING_SNAKE_CASE)

            if self.class_embedding is not None:
                  if class_labels is None:
                        raise ValueError('''class_labels should be provided when doing class conditioning''')

                  if self.config.class_embed_type == "timestep":
                        lowerCAmelCase_						=      self.time_proj(__SCREAMING_SNAKE_CASE)

                  lowerCAmelCase_						=      self.class_embedding(__SCREAMING_SNAKE_CASE).to(dtype=self.dtype)
                  lowerCAmelCase_						=      emb + class_emb

            # 2. pre-process
            lowerCAmelCase_						=      sample
            lowerCAmelCase_						=      self.conv_in(__SCREAMING_SNAKE_CASE)

            # 3. down
            lowerCAmelCase_						=      (sample,)
            for downsample_block in self.down_blocks:
                  if hasattr(__SCREAMING_SNAKE_CASE					,					'''skip_conv'''):
                        lowerCAmelCase_						=      downsample_block(
                            hidden_states=__SCREAMING_SNAKE_CASE					,					temb=__SCREAMING_SNAKE_CASE					,					skip_sample=__SCREAMING_SNAKE_CASE)
                  else:
                        lowerCAmelCase_						=      downsample_block(hidden_states=__SCREAMING_SNAKE_CASE					,					temb=__SCREAMING_SNAKE_CASE)

                  down_block_res_samples += res_samples

            # 4. mid
            lowerCAmelCase_						=      self.mid_block(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)

            # 5. up
            lowerCAmelCase_						=      None
            for upsample_block in self.up_blocks:
                  lowerCAmelCase_						=      down_block_res_samples[-len(upsample_block.resnets) :]
                  lowerCAmelCase_						=      down_block_res_samples[: -len(upsample_block.resnets)]

                  if hasattr(__SCREAMING_SNAKE_CASE					,					'''skip_conv'''):
                        lowerCAmelCase_						=      upsample_block(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)
                  else:
                        lowerCAmelCase_						=      upsample_block(__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE					,					__SCREAMING_SNAKE_CASE)

        # 6. post-process
            lowerCAmelCase_						=      self.conv_norm_out(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      self.conv_act(__SCREAMING_SNAKE_CASE)
            lowerCAmelCase_						=      self.conv_out(__SCREAMING_SNAKE_CASE)

            if skip_sample is not None:
                  sample += skip_sample

            if self.config.time_embedding_type == "fourier":
                  lowerCAmelCase_						=      timesteps.reshape((sample.shape[0], *([1] * len(sample.shape[1:]))))
                  lowerCAmelCase_						=      sample / timesteps

            if not return_dict:
                  return (sample,)

            return UNetaDOutput(sample=__SCREAMING_SNAKE_CASE)







from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode
def 	_lowerCamelCase  (		A_  :	List[Any]=1_0      )			->			List[str]:

   '''simple docstring'''






   for _ in tqdm(range(0							,  args.n_examples							,  A_      )      ):
      yield [next(A_      )[args.text_column] for _ in range(A_      )]


# Configuration
__UpperCAmelCase      			=  HfArgumentParser(TokenizerTrainingArguments)
__UpperCAmelCase      			=  parser.parse_args()

# Base tokenizer
__UpperCAmelCase      			=  AutoTokenizer.from_pretrained(args.base_tokenizer)
__UpperCAmelCase      			=  list(bytes_to_unicode().values())

# Load dataset
__UpperCAmelCase      			=  load_dataset(args.dataset_name, split="""train""", streaming=True)
__UpperCAmelCase      			=  iter(dataset)


# Training and saving
__UpperCAmelCase      			=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)
new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)

