
"""simple docstring"""




from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


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

class 		a__						(      a_						):
   def 			__magic_name__			(			self	,    _a				):
          if isinstance(_a	,    _a				):
                 lowercase    :				int       		=							[label.strip() for label in labels.split(","				) if label.strip()]
          return labels

   def __call__(			self	,    _a	,    _a	,    _a				):
          if len(_a				) == 0 or len(_a				) == 0:
                 raise ValueError("You must include at least one label and at least one sequence."				)
          if hypothesis_template.format(labels[0]				) == hypothesis_template:
                 raise ValueError(
                     (
                         "The provided hypothesis_template \"{}\" was not able to be formatted with the target labels. "
                         "Make sure the passed template includes formatting syntax such as {{}} where the label should go."
                     ).format(_a				)				)

          if isinstance(_a	,    _a				):
                 lowercase    :				int       		=							[sequences]

          lowercase    :				Union[str, Any]       		=							[]
          for sequence in sequences:
                 sequence_pairs.extend([[sequence, hypothesis_template.format(_a				)] for label in labels]				)

          return sequence_pairs, sequences


@add_end_docstrings(a_						)
class 		a__						(      a_						):
   def __init__(			self	,    _a=ZeroShotClassificationArgumentHandler()	,    *_a	,    **_a				):
          lowercase    :				List[str]       		=							args_parser
          super().__init__(*_a	,    **_a				)
          if self.entailment_id == -1:
                 logger.warning(
                     "Failed to determine 'entailment' label id from the label2id mapping in the model config. Setting to "
                     "-1. Define a descriptive label2id mapping in the model config to ensure correct outputs."				)
   @property
   def 			__magic_name__			(			self				):
          for label, ind in self.model.config.labelaid.items():
                 if label.lower().startswith("entail"				):
                        return ind
          return -1
   def 			__magic_name__			(			self	,    _a	,    _a=True	,    _a=True	,    _a=TruncationStrategy.ONLY_FIRST	,    **_a				):
          lowercase    :				Optional[Any]       		=							self.framework
          if self.tokenizer.pad_token is None:
                 # Override for tokenizers not supporting padding
                 logger.error(
                     "Tokenizer was not supporting padding necessary for zero-shot, attempting to use "
                     " `pad_token=eos_token`"				)
                 lowercase    :				Dict       		=							self.tokenizer.eos_token
          try:
                 lowercase    :				int       		=							self.tokenizer(
                     _a	,    add_special_tokens=_a	,    return_tensors=_a	,    padding=_a	,    truncation=_a	,    )
          except Exception as e:
                 if "too short" in str(_a				):
                        # tokenizers might yell that we want to truncate
                        # to a value that is not even reached by the input.
                        # In that case we don't want to truncate.
                        # It seems there's not a really better way to catch that
                        # exception.

                        lowercase    :				int       		=							self.tokenizer(
                            _a	,    add_special_tokens=_a	,    return_tensors=_a	,    padding=_a	,    truncation=TruncationStrategy.DO_NOT_TRUNCATE	,    )
                 else:
                        raise e

          return inputs
   def 			__magic_name__			(			self	,    **_a				):
          if kwargs.get("multi_class"	,    _a				) is not None:
                 lowercase    :				Union[str, Any]       		=							kwargs["multi_class"]
                 logger.warning(
                     "The `multi_class` argument has been deprecated and renamed to `multi_label`. "
                     "`multi_class` will be removed in a future version of Transformers."				)
          lowercase    :				Any       		=							{}
          if "candidate_labels" in kwargs:
                 lowercase    :				Optional[int]       		=							self._args_parser._parse_labels(kwargs["candidate_labels"]				)
          if "hypothesis_template" in kwargs:
                 lowercase    :				List[str]       		=							kwargs["hypothesis_template"]

          lowercase    :				int       		=							{}
          if "multi_label" in kwargs:
                 lowercase    :				Union[str, Any]       		=							kwargs["multi_label"]
          return preprocess_params, {}, postprocess_params
   def __call__(			self	,    _a	,    *_a	,    **_a	,    ):
          if len(_a				) == 0:
                 pass
          elif len(_a				) == 1 and "candidate_labels" not in kwargs:
                 lowercase    :				List[Any]       		=							args[0]
          else:
                 raise ValueError(f"""Unable to understand extra arguments {args}"""				)

          return super().__call__(_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a=None	,    _a="This example is {}."				):
          lowercase		,    lowercase    :				Dict       		=							self._args_parser(_a	,    _a	,    _a				)

          for i, (candidate_label, sequence_pair) in enumerate(zip(_a	,    _a				)				):
                 lowercase    :				List[Any]       		=							self._parse_and_tokenize([sequence_pair]				)

                 yield {
                     "candidate_label": candidate_label,
                     "sequence": sequences[0],
                     "is_last": i == len(_a				) - 1,
                     **model_input,
                 }
   def 			__magic_name__			(			self	,    _a				):
          lowercase    :				List[Any]       		=							inputs["candidate_label"]
          lowercase    :				Optional[Any]       		=							inputs["sequence"]
          lowercase    :				Optional[int]       		=							{k: inputs[k] for k in self.tokenizer.model_input_names}
          lowercase    :				str       		=							self.model(**_a				)

          lowercase    :				int       		=							{
              "candidate_label": candidate_label,
              "sequence": sequence,
              "is_last": inputs["is_last"],
              **outputs,
          }
          return model_outputs

   def 			__magic_name__			(			self	,    _a	,    _a=False				):
          lowercase    :				Tuple       		=							[outputs["candidate_label"] for outputs in model_outputs]
          lowercase    :				Optional[int]       		=							[outputs["sequence"] for outputs in model_outputs]
          lowercase    :				Optional[int]       		=							np.concatenate([output["logits"].numpy() for output in model_outputs]				)
          lowercase    :				Optional[Any]       		=							logits.shape[0]
          lowercase    :				Optional[Any]       		=							len(_a				)
          lowercase    :				Tuple       		=							N // n
          lowercase    :				Tuple       		=							logits.reshape((num_sequences, n, -1)				)

          if multi_label or len(_a				) == 1:
                 # softmax over the entailment vs. contradiction dim for each label independently
                 lowercase    :				Tuple       		=							self.entailment_id
                 lowercase    :				Optional[Any]       		=							-1 if entailment_id == 0 else 0
                 lowercase    :				Any       		=							reshaped_outputs[..., [contradiction_id, entailment_id]]
                 lowercase    :				Dict       		=							np.exp(_a				) / np.exp(_a				).sum(-1	,    keepdims=_a				)
                 lowercase    :				List[str]       		=							scores[..., 1]
          else:
                 # softmax the "entailment" logits over all candidate labels
                 lowercase    :				Optional[int]       		=							reshaped_outputs[..., self.entailment_id]
                 lowercase    :				List[Any]       		=							np.exp(_a				) / np.exp(_a				).sum(-1	,    keepdims=_a				)

          lowercase    :				Tuple       		=							list(reversed(scores[0].argsort()				)				)
          return {
              "sequence": sequences[0],
              "labels": [candidate_labels[i] for i in top_inds],
              "scores": scores[0, top_inds].tolist(),
          }




"""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


