

import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel







def 		_lowercase (     lowercase__   ,	lowercase__=False						):
      __lowerCAmelCase					:						Union[str, Any]					=					OmegaConf.load(lowercase__						)
      if display:
            print(yaml.dump(OmegaConf.to_container(lowercase__						)						)						)
      return config







def 		_lowercase (     lowercase__   ,	lowercase__=None   ,	lowercase__=None						):
      if conf_path is None:
            __lowerCAmelCase					:						Tuple					=					'''./model_checkpoints/vqgan_only.yaml'''
      __lowerCAmelCase					:						Tuple					=					load_config(lowercase__   ,	display=lowercase__						)
      __lowerCAmelCase					:						Optional[Any]					=					VQModel(**config.model.params						)
      if ckpt_path is None:
            __lowerCAmelCase					:						List[str]					=					'''./model_checkpoints/vqgan_only.pt'''
      __lowerCAmelCase					:						List[str]					=					torch.load(lowercase__   ,	map_location=lowercase__						)
      if ".ckpt" in ckpt_path:
            __lowerCAmelCase					:						Tuple					=					sd['''state_dict''']
      model.load_state_dict(lowercase__   ,	strict=lowercase__						)
      model.to(lowercase__						)
      del sd
      return model







def 		_lowercase (     lowercase__   ,	lowercase__						):
      __lowerCAmelCase,							__lowerCAmelCase,							__lowerCAmelCase					:						str					=					model.encode(lowercase__						)
      print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""						)
      __lowerCAmelCase					:						Optional[int]					=					model.decode(lowercase__						)
      return xrec







def 		_lowercase (     lowercase__   ,	lowercase__=False						):
      __lowerCAmelCase,							__lowerCAmelCase					:						str					=					string.rsplit('''.'''   ,	1						)
      if reload:
            __lowerCAmelCase					:						Any					=					importlib.import_module(lowercase__						)
            importlib.reload(lowercase__						)
      return getattr(importlib.import_module(lowercase__   ,	package=lowercase__						)   ,	cls						)







def 		_lowercase (     lowercase__						):
      if "target" not in config:
            raise KeyError('''Expected key `target` to instantiate.'''						)
      return get_obj_from_str(config['''target''']						)(**config.get('''params'''   ,	{}						)						)







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__=True   ,	lowercase__=True						):
      __lowerCAmelCase					:						Tuple					=					instantiate_from_config(lowercase__						)
      if sd is not None:
            model.load_state_dict(lowercase__						)
      if gpu:
            model.cuda()
      if eval_mode:
            model.eval()
      return {"model": model}







def 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      # load the specified checkpoint
      if ckpt:
            __lowerCAmelCase					:						List[Any]					=					torch.load(lowercase__   ,	map_location='''cpu'''						)
            __lowerCAmelCase					:						Dict					=					pl_sd['''global_step''']
            print(f"""loaded model from global step {global_step}."""						)
      else:
            __lowerCAmelCase					:						Any					=					{'''state_dict''': None}
            __lowerCAmelCase					:						Union[str, Any]					=					None
      __lowerCAmelCase					:						Optional[Any]					=					load_model_from_config(config.model   ,	pl_sd['''state_dict''']   ,	gpu=lowercase__   ,	eval_mode=lowercase__						)['''model''']
      return model, global_step





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 		_lowercase (     lowercase__   ,	lowercase__   ,	lowercase__   ,	lowercase__						):
      if isinstance(lowercase__   ,	lowercase__						):
            __lowerCAmelCase					:						Dict					=					np.full((len(lowercase__						), sequence_length, 2)   ,	lowercase__						)
      else:
            __lowerCAmelCase					:						Optional[int]					=					np.full((len(lowercase__						), sequence_length)   ,	lowercase__						)

      for i, tensor in enumerate(lowercase__						):
            if padding_side == "right":
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						int					=					tensor[:sequence_length]
            else:
                  if isinstance(lowercase__   ,	lowercase__						):
                        __lowerCAmelCase					:						Union[str, Any]					=					tensor[:sequence_length]
                  else:
                        __lowerCAmelCase					:						Optional[Any]					=					tensor[:sequence_length]

      return out_tensor.tolist()







def 		_lowercase (     lowercase__						):
      __lowerCAmelCase					:						Union[str, Any]					=					ord(lowercase__						)
      if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
            return True
      __lowerCAmelCase					:						int					=					unicodedata.category(lowercase__						)
      if cat.startswith('''P'''						):
            return True
      return False







@dataclass
class   __lowercase      (_UpperCAmelCase ):
 _UpperCamelCase			  =  42
 _UpperCamelCase			  =  True
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  None
 _UpperCamelCase			  =  -100
 _UpperCamelCase			  =  "pt"
 def 	UpperCamelCase__  (							self      ,							A_					)					->Optional[int]:



       '''simple docstring'''





       import torch

       __lowerCAmelCase					:						List[str]					=					'''label''' if '''label''' in features[0].keys() else '''labels'''
       __lowerCAmelCase					:						Union[str, Any]					=					[feature[label_name] for feature in features] if label_name in features[0].keys() else None
       __lowerCAmelCase					:						List[Any]					=					self.tokenizer.pad(
           A_      ,							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

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

       __lowerCAmelCase					:						Tuple					=					[feature['''ner_tags'''] for feature in features]
       __lowerCAmelCase					:						List[Any]					=					padding_tensor(A_      ,							-1      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[int]					=					[feature['''original_entity_spans'''] for feature in features]
       __lowerCAmelCase					:						Any					=					padding_tensor(A_      ,							(-1, -1)      ,							A_      ,							A_					)
       __lowerCAmelCase					:						Optional[Any]					=					{k: torch.tensor(A_      ,							dtype=torch.intaa					) for k, v in batch.items()}

       return batch


