





"""simple docstring"""






import importlib

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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False							)		->       List[str]:






       """simple docstring"""




       _UpperCAmelCase     =     OmegaConf.load(__snake_case							)
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case							)							)							)
       return config

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=None,SCREAMING_SNAKE_CASE=None							)		->       Optional[int]:






       """simple docstring"""




       if conf_path is None:
              _UpperCAmelCase     =     "./model_checkpoints/vqgan_only.yaml"
       _UpperCAmelCase     =     load_config(__snake_case,display=__snake_case							)
       _UpperCAmelCase     =     VQModel(**config.model.params							)
       if ckpt_path is None:
              _UpperCAmelCase     =     "./model_checkpoints/vqgan_only.pt"
       _UpperCAmelCase     =     torch.load(__snake_case,map_location=__snake_case							)
       if ".ckpt" in ckpt_path:
              _UpperCAmelCase     =     sd["state_dict"]
       model.load_state_dict(__snake_case,strict=__snake_case							)
       model.to(__snake_case							)
       del sd
       return model

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       List[str]:






       """simple docstring"""




       _UpperCAmelCase     =     model.encode(__snake_case							)
       print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""							)
       _UpperCAmelCase     =     model.decode(__snake_case							)
       return xrec

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=False							)		->       Tuple:






       """simple docstring"""




       _UpperCAmelCase     =     string.rsplit('.',1							)
       if reload:
              _UpperCAmelCase     =     importlib.import_module(__snake_case							)
              importlib.reload(__snake_case							)
       return getattr(importlib.import_module(__snake_case,package=__snake_case							),cls							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       Tuple:






       """simple docstring"""




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

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE=True,SCREAMING_SNAKE_CASE=True							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     instantiate_from_config(__snake_case							)
       if sd is not None:
              model.load_state_dict(__snake_case							)
       if gpu:
              model.cuda()
       if eval_mode:
              model.eval()
       return {"model": model}


def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Tuple:






       """simple docstring"""




       if ckpt:
              _UpperCAmelCase     =     torch.load(__snake_case,map_location='cpu'							)
              _UpperCAmelCase     =     pl_sd["global_step"]
              print(F"""loaded model from global step {global_step}."""							)
       else:
              _UpperCAmelCase     =     {"state_dict": None}
              _UpperCAmelCase     =     None
       _UpperCAmelCase     =     load_model_from_config(config.model,pl_sd['state_dict'],gpu=__snake_case,eval_mode=__snake_case							)["model"]
       return model, global_step













"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    '''microsoft/trocr-base-handwritten''': (
        '''https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'''
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """trocr"""
       lowerCAmelCase__						 =  ["""past_key_values"""]
       lowerCAmelCase__						 =  {
           """num_attention_heads""": """decoder_attention_heads""",
           """hidden_size""": """d_model""",
           """num_hidden_layers""": """decoder_layers""",
       }






       def __init__(   self    ,  a__=5_02_65    ,  a__=10_24    ,  a__=12    ,  a__=16    ,  a__=40_96    ,  a__="gelu"    ,  a__=5_12    ,  a__=0.1    ,  a__=0.0    ,  a__=0.0    ,  a__=2    ,  a__=0.02    ,  a__=0.0    ,  a__=True    ,  a__=False    ,  a__=True    ,  a__=True    ,  a__=1    ,  a__=0    ,  a__=2    ,  **a__    ,  ):
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     d_model
              _UpperCAmelCase     =     decoder_layers
              _UpperCAmelCase     =     decoder_attention_heads
              _UpperCAmelCase     =     decoder_ffn_dim
              _UpperCAmelCase     =     activation_function
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     dropout
              _UpperCAmelCase     =     attention_dropout
              _UpperCAmelCase     =     activation_dropout
              _UpperCAmelCase     =     init_std
              _UpperCAmelCase     =     decoder_layerdrop
              _UpperCAmelCase     =     use_cache
              _UpperCAmelCase     =     scale_embedding
              _UpperCAmelCase     =     use_learned_position_embeddings
              _UpperCAmelCase     =     layernorm_embedding

              super().__init__(
                  pad_token_id=a__    ,  bos_token_id=a__    ,  eos_token_id=a__    ,  decoder_start_token_id=a__    ,  **a__    ,  )






