
"""simple docstring"""




import enum
import warnings

from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_tf_available():
   import tensorflow as tf

class 		a__						(      enum.Enum						):
   __lowerCAmelCase            =				0
   __lowerCAmelCase            =				1
   __lowerCAmelCase            =				2


@add_end_docstrings(a_						)
class 		a__						(      a_						):
   __lowerCAmelCase            =				"""
    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The
    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western
    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision
    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an
    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of
    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,
    begging for his blessing. <eod> </s> <eos>
    """
   def __init__(			self	,    *_a	,    **_a				):
          super().__init__(*_a	,    **_a				)
          self.check_model_type(
              TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING				)
          if "prefix" not in self._preprocess_params:
                 # This is very specific. The logic is quite complex and needs to be done
                 # as a "default".
                 # It also defines both some preprocess_kwargs and generate_kwargs
                 # which is why we cannot put them in their respective methods.
                 lowercase    :				Dict       		=							None
                 if self.model.config.prefix is not None:
                        lowercase    :				Optional[int]       		=							self.model.config.prefix
                 if prefix is None and self.model.__class__.__name__ in [
                     "XLNetLMHeadModel",
                     "TransfoXLLMHeadModel",
                     "TFXLNetLMHeadModel",
                     "TFTransfoXLLMHeadModel",
                 ]:
                        # For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
                        lowercase    :				int       		=							self.XL_PREFIX
                 if prefix is not None:
                        # Recalculate some generate_kwargs linked to prefix.
                        lowercase		,    lowercase		,    lowercase    :				Dict       		=							self._sanitize_parameters(prefix=_a	,    **self._forward_params				)
                        lowercase    :				str       		=							{**self._preprocess_params, **preprocess_params}
                        lowercase    :				Any       		=							{**self._forward_params, **forward_params}
   def 			__magic_name__			(			self	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    _a=None	,    **_a	,    ):
          lowercase    :				str       		=							{}
          if prefix is not None:
                 lowercase    :				Tuple       		=							prefix
          if prefix:
                 lowercase    :				Dict       		=							self.tokenizer(
                     _a	,    padding=_a	,    add_special_tokens=_a	,    return_tensors=self.framework				)
                 lowercase    :				List[Any]       		=							prefix_inputs["input_ids"].shape[-1]

          if handle_long_generation is not None:
                 if handle_long_generation not in {"hole"}:
                        raise ValueError(
                            f"""{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected"""
                            " [None, 'hole']"				)
                 lowercase    :				List[Any]       		=							handle_long_generation

          preprocess_params.update(_a				)
          lowercase    :				Dict       		=							generate_kwargs

          lowercase    :				int       		=							{}
          if return_full_text is not None and return_type is None:
                 if return_text is not None:
                        raise ValueError("`return_text` is mutually exclusive with `return_full_text`"				)
                 if return_tensors is not None:
                        raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`"				)
                 lowercase    :				Any       		=							ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
          if return_tensors is not None and return_type is None:
                 if return_text is not None:
                        raise ValueError("`return_text` is mutually exclusive with `return_tensors`"				)
                 lowercase    :				Tuple       		=							ReturnType.TENSORS
          if return_type is not None:
                 lowercase    :				Any       		=							return_type
          if clean_up_tokenization_spaces is not None:
                 lowercase    :				int       		=							clean_up_tokenization_spaces

          if stop_sequence is not None:
                 lowercase    :				Tuple       		=							self.tokenizer.encode(_a	,    add_special_tokens=_a				)
                 if len(_a				) > 1:
                        warnings.warn(
                            "Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
                            " the stop sequence will be used as the stop sequence string in the interim."				)
                 lowercase    :				int       		=							stop_sequence_ids[0]

          return preprocess_params, forward_params, postprocess_params
   def 			__magic_name__			(			self	,    *_a	,    **_a				):
          # Parse arguments
          if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
                 kwargs.update({"add_space_before_punct_symbol": True}				)

          return super()._parse_and_tokenize(*_a	,    **_a				)
   def __call__(			self	,    _a	,    **_a				):
          return super().__call__(_a	,    **_a				)
   def 			__magic_name__			(			self	,    _a	,    _a=""	,    _a=None	,    **_a				):
          lowercase    :				int       		=							self.tokenizer(
              prefix + prompt_text	,    padding=_a	,    add_special_tokens=_a	,    return_tensors=self.framework				)
          lowercase    :				List[str]       		=							prompt_text

          if handle_long_generation == "hole":
                 lowercase    :				Any       		=							inputs["input_ids"].shape[-1]
                 if "max_new_tokens" in generate_kwargs:
                        lowercase    :				List[Any]       		=							generate_kwargs["max_new_tokens"]
                 else:
                        lowercase    :				Any       		=							generate_kwargs.get("max_length"	,    self.model.config.max_length				) - cur_len
                        if new_tokens < 0:
                               raise ValueError("We cannot infer how many new tokens are expected"				)
                 if cur_len + new_tokens > self.tokenizer.model_max_length:
                        lowercase    :				List[str]       		=							self.tokenizer.model_max_length - new_tokens
                        if keep_length <= 0:
                               raise ValueError(
                                   "We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
                                   " models max length"				)

                        lowercase    :				Optional[int]       		=							inputs["input_ids"][:, -keep_length:]
                        if "attention_mask" in inputs:
                               lowercase    :				Tuple       		=							inputs["attention_mask"][:, -keep_length:]

          return inputs
   def 			__magic_name__			(			self	,    _a	,    **_a				):
          lowercase    :				Any       		=							model_inputs["input_ids"]
          lowercase    :				List[Any]       		=							model_inputs.get("attention_mask"	,    _a				)
          # Allow empty prompts
          if input_ids.shape[1] == 0:
                 lowercase    :				Dict       		=							None
                 lowercase    :				Dict       		=							None
                 lowercase    :				Union[str, Any]       		=							1
          else:
                 lowercase    :				Dict       		=							input_ids.shape[0]
          lowercase    :				Union[str, Any]       		=							model_inputs.pop("prompt_text"				)

          # If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
          # generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
          lowercase    :				str       		=							generate_kwargs.pop("prefix_length"	,    0				)
          if prefix_length > 0:
                 lowercase    :				Optional[int]       		=							"max_new_tokens" in generate_kwargs or (
                     "generation_config" in generate_kwargs
                     and generate_kwargs["generation_config"].max_new_tokens is not None
                 )
                 if not has_max_new_tokens:
                        lowercase    :				List[str]       		=							generate_kwargs.get("max_length"				) or self.model.config.max_length
                        generate_kwargs["max_length"] += prefix_length
                 lowercase    :				str       		=							"min_new_tokens" in generate_kwargs or (
                     "generation_config" in generate_kwargs
                     and generate_kwargs["generation_config"].min_new_tokens is not None
                 )
                 if not has_min_new_tokens and "min_length" in generate_kwargs:
                        generate_kwargs["min_length"] += prefix_length

        # BS x SL
          lowercase    :				Tuple       		=							self.model.generate(input_ids=_a	,    attention_mask=_a	,    **_a				)
          lowercase    :				List[str]       		=							generated_sequence.shape[0]
          if self.framework == "pt":
                 lowercase    :				List[str]       		=							generated_sequence.reshape(_a	,    out_b // in_b	,    *generated_sequence.shape[1:]				)
          elif self.framework == "tf":
                 lowercase    :				Dict       		=							tf.reshape(_a	,    (in_b, out_b // in_b, *generated_sequence.shape[1:])				)
          return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}

   def 			__magic_name__			(			self	,    _a	,    _a=ReturnType.FULL_TEXT	,    _a=True				):
          lowercase    :				List[str]       		=							model_outputs["generated_sequence"][0]
          lowercase    :				List[str]       		=							model_outputs["input_ids"]
          lowercase    :				Tuple       		=							model_outputs["prompt_text"]
          lowercase    :				Optional[Any]       		=							generated_sequence.numpy().tolist()
          lowercase    :				int       		=							[]
          for sequence in generated_sequence:
                 if return_type == ReturnType.TENSORS:
                        lowercase    :				int       		=							{"generated_token_ids": sequence}
                 elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
                        # Decode text
                        lowercase    :				Dict       		=							self.tokenizer.decode(
                            _a	,    skip_special_tokens=_a	,    clean_up_tokenization_spaces=_a	,    )

                        # Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
                        if input_ids is None:
                               lowercase    :				List[Any]       		=							0
                        else:
                               lowercase    :				Optional[int]       		=							len(
                                   self.tokenizer.decode(
                                       input_ids[0]	,    skip_special_tokens=_a	,    clean_up_tokenization_spaces=_a	,    )				)

                        if return_type == ReturnType.FULL_TEXT:
                               lowercase    :				Union[str, Any]       		=							prompt_text + text[prompt_length:]
                        else:
                               lowercase    :				Tuple       		=							text[prompt_length:]

                        lowercase    :				Dict       		=							{"generated_text": all_text}
                 records.append(_a				)

          return records




"""simple docstring"""




import argparse

import OmegaConf
import torch

from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel


def   __magic_name__	(      __snake_case				:       List[str]     , __snake_case				:       Tuple     , __snake_case				:       Tuple  ) ->				int:
       lowercase    :				Union[str, Any]       		=							OmegaConf.load(__snake_case  )
       lowercase    :				int       		=							torch.load(__snake_case     , map_location="cpu"  )["model"]
       lowercase    :				Optional[Any]       		=							list(state_dict.keys()  )

       # extract state_dict for VQVAE
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				Union[str, Any]       		=							"first_stage_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				List[Any]       		=							state_dict[key]

    # extract state_dict for UNetLDM
       lowercase    :				Optional[int]       		=							{}
       lowercase    :				List[Any]       		=							"model.diffusion_model."
       for key in keys:
              if key.startswith(__snake_case  ):
                     lowercase    :				Optional[Any]       		=							state_dict[key]

       lowercase    :				Dict       		=							config.model.params.first_stage_config.params
       lowercase    :				List[str]       		=							config.model.params.unet_config.params

       lowercase    :				Union[str, Any]       		=							VQModel(**__snake_case  ).eval()
       vqvae.load_state_dict(__snake_case  )

       lowercase    :				List[Any]       		=							UNetLDMModel(**__snake_case  ).eval()
       unet.load_state_dict(__snake_case  )

       lowercase    :				Dict       		=							DDIMScheduler(
           timesteps=config.model.params.timesteps     , beta_schedule="scaled_linear"     , beta_start=config.model.params.linear_start     , beta_end=config.model.params.linear_end     , clip_sample=__snake_case     , )

       lowercase    :				Optional[Any]       		=							LDMPipeline(__snake_case     , __snake_case     , __snake_case  )
       pipeline.save_pretrained(__snake_case  )


if __name__ == "__main__":
   _A			:       Optional[Any]   		=       argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", type=str, required=True)
   parser.add_argument("""--config_path""", type=str, required=True)
   parser.add_argument("""--output_path""", type=str, required=True)
   _A			:       Dict   		=       parser.parse_args()

   convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)


