


'''simple docstring'''






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 get_down_block, get_mid_block, get_out_block, get_up_block




@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42





class 	a       (       _SCREAMING_SNAKE_CASE					,			_SCREAMING_SNAKE_CASE    ):
 @register_to_config
 def __init__(	self ,			__magic_name__ = 6_55_36 ,			__magic_name__ = None ,			__magic_name__ = 2 ,			__magic_name__ = 2 ,			__magic_name__ = 0 ,			__magic_name__ = "fourier" ,			__magic_name__ = True ,			__magic_name__ = False ,			__magic_name__ = 0.0 ,			__magic_name__ = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,			__magic_name__ = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,			__magic_name__ = "UNetMidBlock1D" ,			__magic_name__ = None ,			__magic_name__ = (32, 32, 64) ,			__magic_name__ = None ,			__magic_name__ = 8 ,			__magic_name__ = 1 ,			__magic_name__ = False ,			)   ->							Tuple:
      super().__init__()
      _a      =							sample_size

      # time
      if time_embedding_type == "fourier":
           _a      =							GaussianFourierProjection(
               embedding_size=8 ,			set_W_to_weight=__magic_name__ ,			log=__magic_name__ ,			flip_sin_to_cos=__magic_name__  )
           _a      =							2 * block_out_channels[0]
      elif time_embedding_type == "positional":
           _a      =							Timesteps(
               block_out_channels[0] ,			flip_sin_to_cos=__magic_name__ ,			downscale_freq_shift=__magic_name__  )
           _a      =							block_out_channels[0]

      if use_timestep_embedding:
           _a      =							block_out_channels[0] * 4
           _a      =							TimestepEmbedding(
               in_channels=__magic_name__ ,			time_embed_dim=__magic_name__ ,			act_fn=__magic_name__ ,			out_dim=block_out_channels[0] ,			)

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

      # down
      _a      =							in_channels
      for i, down_block_type in enumerate(__magic_name__  ):
           _a      =							output_channel
           _a      =							block_out_channels[i]

           if i == 0:
                input_channel += extra_in_channels

           _a      =							i == len(__magic_name__  ) - 1

           _a      =							get_down_block(
               __magic_name__ ,			num_layers=__magic_name__ ,			in_channels=__magic_name__ ,			out_channels=__magic_name__ ,			temb_channels=block_out_channels[0] ,			add_downsample=not is_final_block or downsample_each_block ,			)
           self.down_blocks.append(__magic_name__  )

      # mid
      _a      =							get_mid_block(
          __magic_name__ ,			in_channels=block_out_channels[-1] ,			mid_channels=block_out_channels[-1] ,			out_channels=block_out_channels[-1] ,			embed_dim=block_out_channels[0] ,			num_layers=__magic_name__ ,			add_downsample=__magic_name__ ,			)

      # up
      _a      =							list(reversed(__magic_name__  )  )
      _a      =							reversed_block_out_channels[0]
      if out_block_type is None:
           _a      =							out_channels
      else:
           _a      =							block_out_channels[0]

      for i, up_block_type in enumerate(__magic_name__  ):
           _a      =							output_channel
           _a      =							(
               reversed_block_out_channels[i + 1] if i < len(__magic_name__  ) - 1 else final_upsample_channels
           )

           _a      =							i == len(__magic_name__  ) - 1

           _a      =							get_up_block(
               __magic_name__ ,			num_layers=__magic_name__ ,			in_channels=__magic_name__ ,			out_channels=__magic_name__ ,			temb_channels=block_out_channels[0] ,			add_upsample=not is_final_block ,			)
           self.up_blocks.append(__magic_name__  )
           _a      =							output_channel

      # out
      _a      =							norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,			32  )
      _a      =							get_out_block(
          out_block_type=__magic_name__ ,			num_groups_out=__magic_name__ ,			embed_dim=block_out_channels[0] ,			out_channels=__magic_name__ ,			act_fn=__magic_name__ ,			fc_dim=block_out_channels[-1] // 4 ,			)




 def 							__UpperCAmelCase						(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = True ,			)   ->							Union[UNetaDOutput, Tuple]:
      _a      =							timestep
      if not torch.is_tensor(__magic_name__  ):
           _a      =							torch.tensor([timesteps] ,			dtype=torch.long ,			device=sample.device  )
      elif torch.is_tensor(__magic_name__  ) and len(timesteps.shape  ) == 0:
           _a      =							timesteps[None].to(sample.device  )

      _a      =							self.time_proj(__magic_name__  )
      if self.config.use_timestep_embedding:
           _a      =							self.time_mlp(__magic_name__  )
      else:
           _a      =							timestep_embed[..., None]
           _a      =							timestep_embed.repeat([1, 1, sample.shape[2]]  ).to(sample.dtype  )
           _a      =							timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:])  )

      # 2. down
      _a      =							()
      for downsample_block in self.down_blocks:
           _a ,				_a      =							downsample_block(hidden_states=__magic_name__ ,			temb=__magic_name__  )
           down_block_res_samples += res_samples

      # 3. mid
      if self.mid_block:
           _a      =							self.mid_block(__magic_name__ ,			__magic_name__  )

      # 4. up
      for i, upsample_block in enumerate(self.up_blocks  ):
           _a      =							down_block_res_samples[-1:]
           _a      =							down_block_res_samples[:-1]
           _a      =							upsample_block(__magic_name__ ,			res_hidden_states_tuple=__magic_name__ ,			temb=__magic_name__  )

      # 5. post-process
      if self.out_block:
           _a      =							self.out_block(__magic_name__ ,			__magic_name__  )

      if not return_dict:
           return (sample,)

      return UNetaDOutput(sample=__magic_name__  )





'''simple docstring'''






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

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

     for i, tensor in enumerate(lowerCAmelCase__						):
          if padding_side == "right":
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]

     return out_tensor.tolist()






def 		_A			(lowerCAmelCase__					:Any						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"pt"
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      import torch

      _a      =							'label' if 'label' in features[0].keys() else 'labels'
      _a      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =							self.tokenizer.pad(
          __magic_name__ ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors='pt' if labels is None else None ,			)

      if labels is None:
           return batch

      _a      =							torch.tensor(batch['entity_ids']  ).shape[1]
      _a      =							self.tokenizer.padding_side
      if padding_side == "right":
           _a      =							[
               list(__magic_name__  ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) for label in labels
           ]
      else:
           _a      =							[
               [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) + list(__magic_name__  ) for label in labels
           ]

      _a      =							[feature['ner_tags'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			-1 ,			__magic_name__ ,			__magic_name__  )
      _a      =							[feature['original_entity_spans'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			(-1, -1) ,			__magic_name__ ,			__magic_name__  )
      _a      =							{k: torch.tensor(__magic_name__ ,			dtype=torch.intaa  ) for k, v in batch.items()}

      return batch

