
"""simple docstring"""




import copy
import os
from typing import Union

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


_A			:       Optional[Any]   		=       logging.get_logger(__name__)

_A			:       int   		=       {
    """google/pix2struct-textcaps-base""": (
        """https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
    ),
}

class 		a__						(      a_						):
   __lowerCAmelCase            =				"""pix2struct_text_model"""
   __lowerCAmelCase            =				["""past_key_values"""]
   __lowerCAmelCase            =				{
       """hidden_size""": """hidden_size""",
       """num_attention_heads""": """num_heads""",
       """num_hidden_layers""": """num_layers""",
   }
   def __init__(			self	,    _a=50_244	,    _a=768	,    _a=64	,    _a=2_048	,    _a=12	,    _a=12	,    _a=32	,    _a=128	,    _a=0.1	,    _a=1E-6	,    _a=1.0	,    _a="gelu_new"	,    _a=0	,    _a=False	,    _a=0	,    _a=1	,    _a=False	,    _a=True	,    **_a	,    ):
          lowercase    :				Optional[int]       		=							vocab_size
          lowercase    :				Any       		=							hidden_size
          lowercase    :				Optional[Any]       		=							d_kv
          lowercase    :				Any       		=							d_ff
          lowercase    :				Union[str, Any]       		=							num_layers
          lowercase    :				Union[str, Any]       		=							num_heads
          lowercase    :				Any       		=							relative_attention_num_buckets
          lowercase    :				Tuple       		=							relative_attention_max_distance
          lowercase    :				int       		=							dropout_rate
          lowercase    :				Dict       		=							layer_norm_epsilon
          lowercase    :				List[str]       		=							initializer_factor
          lowercase    :				int       		=							use_cache

          lowercase    :				Optional[int]       		=							eos_token_id
          lowercase    :				List[Any]       		=							decoder_start_token_id

          # for backwards compatibility
          lowercase    :				Dict       		=							dense_act_fn

          super().__init__(
              pad_token_id=_a	,    eos_token_id=_a	,    decoder_start_token_id=_a	,    tie_word_embeddings=_a	,    is_decoder=_a	,    **_a	,    )

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          cls._set_token_in_kwargs(_a				)

          lowercase		,    lowercase    :				List[str]       		=							cls.get_config_dict(_a	,    **_a				)

          # get the text config dict if we are loading from Pix2StructConfig
          if config_dict.get("model_type"				) == "pix2struct":
                 lowercase    :				int       		=							config_dict["text_config"]

          if "model_type" in config_dict and hasattr(cls	,    "model_type"				) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                     f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

          return cls.from_dict(_a	,    **_a				)


class 		a__						(      a_						):
   __lowerCAmelCase            =				"""pix2struct_vision_model"""
   def __init__(			self	,    _a=768	,    _a=768	,    _a=2_048	,    _a=64	,    _a=12	,    _a=12	,    _a="gelu_new"	,    _a=1E-6	,    _a=0.0	,    _a=0.0	,    _a=1E-10	,    _a=1.0	,    _a=4_096	,    _a=32	,    _a=128	,    **_a	,    ):
          super().__init__(**_a				)

          lowercase    :				Any       		=							hidden_size
          lowercase    :				Tuple       		=							patch_embed_hidden_size
          lowercase    :				Optional[Any]       		=							d_ff
          lowercase    :				Any       		=							dropout_rate
          lowercase    :				Dict       		=							num_hidden_layers
          lowercase    :				str       		=							num_attention_heads
          lowercase    :				Union[str, Any]       		=							initializer_range
          lowercase    :				List[str]       		=							initializer_factor
          lowercase    :				int       		=							attention_dropout
          lowercase    :				Optional[Any]       		=							layer_norm_eps
          lowercase    :				List[str]       		=							dense_act_fn
          lowercase    :				Union[str, Any]       		=							seq_len
          lowercase    :				Union[str, Any]       		=							relative_attention_num_buckets
          lowercase    :				Union[str, Any]       		=							relative_attention_max_distance
          lowercase    :				Optional[int]       		=							d_kv

   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    **_a				):
          cls._set_token_in_kwargs(_a				)

          lowercase		,    lowercase    :				Tuple       		=							cls.get_config_dict(_a	,    **_a				)

          # get the vision config dict if we are loading from Pix2StructConfig
          if config_dict.get("model_type"				) == "pix2struct":
                 lowercase    :				Union[str, Any]       		=							config_dict["vision_config"]

          if "model_type" in config_dict and hasattr(cls	,    "model_type"				) and config_dict["model_type"] != cls.model_type:
                 logger.warning(
                     f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
                     f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""				)

          return cls.from_dict(_a	,    **_a				)


class 		a__						(      a_						):
   __lowerCAmelCase            =				"""pix2struct"""
   __lowerCAmelCase            =				True
   def __init__(			self	,    _a=None	,    _a=None	,    _a=1.0	,    _a=0.0_2	,    _a=False	,    _a=False	,    _a=True	,    **_a	,    ):
          super().__init__(tie_word_embeddings=_a	,    is_encoder_decoder=_a	,    **_a				)

          if text_config is None:
                 lowercase    :				int       		=							{}
                 logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."				)

          if vision_config is None:
                 lowercase    :				List[Any]       		=							{}
                 logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."				)

          lowercase    :				int       		=							PixaStructTextConfig(**_a				)
          lowercase    :				Optional[int]       		=							PixaStructVisionConfig(**_a				)

          lowercase    :				List[str]       		=							self.text_config.decoder_start_token_id
          lowercase    :				List[Any]       		=							self.text_config.pad_token_id
          lowercase    :				Tuple       		=							self.text_config.eos_token_id

          lowercase    :				str       		=							initializer_factor
          lowercase    :				str       		=							initializer_range

          lowercase    :				Dict       		=							self.initializer_range
          lowercase    :				Dict       		=							self.initializer_range

          lowercase    :				Union[str, Any]       		=							is_vqa
   @classmethod
   def 			__magic_name__			(			cls	,    _a	,    _a	,    **_a				):
          return cls(text_config=text_config.to_dict()	,    vision_config=vision_config.to_dict()	,    **_a				)

   def 			__magic_name__			(			self				):
          lowercase    :				Dict       		=							copy.deepcopy(self.__dict__				)
          lowercase    :				List[str]       		=							self.text_config.to_dict()
          lowercase    :				Tuple       		=							self.vision_config.to_dict()
          lowercase    :				Optional[Any]       		=							self.__class__.model_type
          return output




"""simple docstring"""




import importlib

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


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=False  ) ->				Tuple:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       if display:
              print(yaml.dump(OmegaConf.to_container(__snake_case  )  )  )
       return config


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[Any]=None     , __snake_case				:       Union[str, Any]=None  ) ->				Tuple:
       if conf_path is None:
              lowercase    :				List[Any]       		=							"./model_checkpoints/vqgan_only.yaml"
       lowercase    :				Tuple       		=							load_config(__snake_case     , display=__snake_case  )
       lowercase    :				List[Any]       		=							VQModel(**config.model.params  )
       if ckpt_path is None:
              lowercase    :				List[str]       		=							"./model_checkpoints/vqgan_only.pt"
       lowercase    :				Optional[int]       		=							torch.load(__snake_case     , map_location=__snake_case  )
       if ".ckpt" in ckpt_path:
              lowercase    :				str       		=							sd["state_dict"]
       model.load_state_dict(__snake_case     , strict=__snake_case  )
       model.to(__snake_case  )
       del sd
       return model


def   __magic_name__	(      __snake_case				:       Tuple     , __snake_case				:       Union[str, Any]  ) ->				int:
       lowercase		,    lowercase		,    lowercase    :				List[Any]       		=							model.encode(__snake_case  )
       print(f"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""  )
       lowercase    :				str       		=							model.decode(__snake_case  )
       return xrec


def   __magic_name__	(      __snake_case				:       Dict     , __snake_case				:       Optional[int]=False  ) ->				int:
       lowercase		,    lowercase    :				Union[str, Any]       		=							string.rsplit("."     , 1  )
       if reload:
              lowercase    :				Any       		=							importlib.import_module(__snake_case  )
              importlib.reload(__snake_case  )
       return getattr(importlib.import_module(__snake_case     , package=__snake_case  )     , cls  )


def   __magic_name__	(      __snake_case				:       str  ) ->				List[str]:
       if "target" not in config:
              raise KeyError("Expected key `target` to instantiate."  )
       return get_obj_from_str(config["target"]  )(**config.get("params"     , {}  )  )


def   __magic_name__	(      __snake_case				:       Any     , __snake_case				:       int     , __snake_case				:       List[Any]=True     , __snake_case				:       Dict=True  ) ->				str:
       lowercase    :				Optional[int]       		=							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   __magic_name__	(      __snake_case				:       Optional[int]     , __snake_case				:       Any     , __snake_case				:       Optional[int]     , __snake_case				:       List[str]  ) ->				Any:
       # load the specified checkpoint
       if ckpt:
              lowercase    :				Dict       		=							torch.load(__snake_case     , map_location="cpu"  )
              lowercase    :				List[Any]       		=							pl_sd["global_step"]
              print(f"""loaded model from global step {global_step}."""  )
       else:
              lowercase    :				int       		=							{"state_dict": None}
              lowercase    :				Optional[Any]       		=							None
       lowercase    :				List[Any]       		=							load_model_from_config(config.model     , pl_sd["state_dict"]     , gpu=__snake_case     , eval_mode=__snake_case  )["model"]
       return model, global_step


