




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 						__lowercase		(enum.Enum							):




      """simple docstring"""





      _snake_case							     =						0
      _snake_case							     =						1
      _snake_case							     =						2




@add_end_docstrings(UpperCAmelCase__							)
class 						__lowercase		(UpperCAmelCase__							):




      """simple docstring"""





      _snake_case							     =						"\n    In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n    voice of Nicholas's young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n    Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n    and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n    accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n    the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n    begging for his blessing. <eod> </s> <eos>\n    "

      def __init__(      self				,      *A				,      **A	)		->				str:
         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.
            snake_case      :				int				=       None
            if self.model.config.prefix is not None:
               snake_case      :				Any				=       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.
               snake_case      :				Any				=       self.XL_PREFIX
            if prefix is not None:
               # Recalculate some generate_kwargs linked to prefix.
               snake_case      :				Tuple				=       self._sanitize_parameters(prefix=A				,      **self._forward_params	)
               snake_case      :				Optional[int]				=       {**self._preprocess_params, **preprocess_params}
               snake_case      :				Tuple				=       {**self._forward_params, **forward_params}

      def 	UpperCAmelCase      (      self				,      A=None				,      A=None				,      A=None				,      A=None				,      A=None				,      A=None				,      A=None				,      A=None				,      **A				,      )		->				Optional[int]:
         snake_case      :				Optional[int]				=       {}
         if prefix is not None:
            snake_case      :				Union[str, Any]				=       prefix
         if prefix:
            snake_case      :				Union[str, Any]				=       self.tokenizer(
                A				,      padding=A				,      add_special_tokens=A				,      return_tensors=self.framework	)
            snake_case      :				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']"""	)
            snake_case      :				List[Any]				=       handle_long_generation

         preprocess_params.update(A	)
         snake_case      :				Tuple				=       generate_kwargs

         snake_case      :				List[str]				=       {}
         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`"""	)
            snake_case      :				List[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`"""	)
            snake_case      :				Any				=       ReturnType.TENSORS
         if return_type is not None:
            snake_case      :				Union[str, Any]				=       return_type
         if clean_up_tokenization_spaces is not None:
            snake_case      :				Tuple				=       clean_up_tokenization_spaces

         if stop_sequence is not None:
            snake_case      :				str				=       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."""	)
            snake_case      :				Optional[int]				=       stop_sequence_ids[0]

         return preprocess_params, forward_params, postprocess_params

      def 	UpperCAmelCase      (      self				,      *A				,      **A	)		->				Optional[int]:
         # 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	)		->				int:
         return super().__call__(A				,      **A	)

      def 	UpperCAmelCase      (      self				,      A				,      A=""				,      A=None				,      **A	)		->				Any:
         snake_case      :				Union[str, Any]				=       self.tokenizer(
             prefix + prompt_text				,      padding=A				,      add_special_tokens=A				,      return_tensors=self.framework	)
         snake_case      :				Tuple				=       prompt_text

         if handle_long_generation == "hole":
            snake_case      :				Optional[Any]				=       inputs["""input_ids"""].shape[-1]
            if "max_new_tokens" in generate_kwargs:
               snake_case      :				List[str]				=       generate_kwargs["""max_new_tokens"""]
            else:
               snake_case      :				Optional[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:
               snake_case      :				List[Any]				=       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"""	)

               snake_case      :				int				=       inputs["""input_ids"""][:, -keep_length:]
               if "attention_mask" in inputs:
                  snake_case      :				List[str]				=       inputs["""attention_mask"""][:, -keep_length:]

         return inputs

      def 	UpperCAmelCase      (      self				,      A				,      **A	)		->				List[str]:
         snake_case      :				int				=       model_inputs["""input_ids"""]
         snake_case      :				List[str]				=       model_inputs.get("""attention_mask"""				,      A	)
         # Allow empty prompts
         if input_ids.shape[1] == 0:
            snake_case      :				int				=       None
            snake_case      :				Optional[Any]				=       None
            snake_case      :				Any				=       1
         else:
            snake_case      :				Dict				=       input_ids.shape[0]
         snake_case      :				List[str]				=       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.
         snake_case      :				str				=       generate_kwargs.pop("""prefix_length"""				,      0	)
         if prefix_length > 0:
            snake_case      :				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:
               snake_case      :				Union[str, Any]				=       generate_kwargs.get("""max_length"""	) or self.model.config.max_length
               generate_kwargs["max_length"] += prefix_length
            snake_case      :				List[Any]				=       """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
         snake_case      :				Tuple				=       self.model.generate(input_ids=A				,      attention_mask=A				,      **A	)
         snake_case      :				int				=       generated_sequence.shape[0]
         if self.framework == "pt":
            snake_case      :				List[str]				=       generated_sequence.reshape(A				,      out_b // in_b				,      *generated_sequence.shape[1:]	)
         elif self.framework == "tf":
            snake_case      :				List[str]				=       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 	UpperCAmelCase      (      self				,      A				,      A=ReturnType.FULL_TEXT				,      A=True	)		->				Optional[Any]:
         snake_case      :				Optional[int]				=       model_outputs["""generated_sequence"""][0]
         snake_case      :				Tuple				=       model_outputs["""input_ids"""]
         snake_case      :				Tuple				=       model_outputs["""prompt_text"""]
         snake_case      :				List[Any]				=       generated_sequence.numpy().tolist()
         snake_case      :				Dict				=       []
         for sequence in generated_sequence:
            if return_type == ReturnType.TENSORS:
               snake_case      :				Union[str, Any]				=       {"""generated_token_ids""": sequence}
            elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
               # Decode text
               snake_case      :				int				=       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:
                  snake_case      :				Tuple				=       0
               else:
                  snake_case      :				List[Any]				=       len(
                      self.tokenizer.decode(
                          input_ids[0]				,      skip_special_tokens=A				,      clean_up_tokenization_spaces=A				,      )	)

               if return_type == ReturnType.FULL_TEXT:
                  snake_case      :				Dict				=       prompt_text + text[prompt_length:]
               else:
                  snake_case      :				int				=       text[prompt_length:]

               snake_case      :				str				=       {"""generated_text""": all_text}
            records.append(A	)

         return records









'''simple docstring'''







from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer







@dataclass
class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  torch.FloatTensor







class A__       (						UpperCAmelCase__ ,     UpperCAmelCase__      ):






      @register_to_config
      def __init__( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("DownEncoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[str] = ("UpDecoderBlock2D",)     ,						SCREAMING_SNAKE_CASE			:Tuple[int] = (6_4,)     ,						SCREAMING_SNAKE_CASE			:int = 1     ,						SCREAMING_SNAKE_CASE			:str = "silu"     ,						SCREAMING_SNAKE_CASE			:int = 3     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:int = 2_5_6     ,						SCREAMING_SNAKE_CASE			:int = 3_2     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:float = 0.18_215     ,						SCREAMING_SNAKE_CASE			:str = "group"     ,						)       ->					Optional[int]:




             '''simple docstring'''
             super().__init__()

             # pass init params to Encoder
             _a    : Union[str, Any]     					=Encoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						down_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						double_z=SCREAMING_SNAKE_CASE     ,						)

             _a    : Optional[int]     					=vq_embed_dim if vq_embed_dim is not None else latent_channels

             _a    : Optional[int]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)
             _a    : str     					=VectorQuantizer(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						beta=0.25     ,						remap=SCREAMING_SNAKE_CASE     ,						sane_index_shape=SCREAMING_SNAKE_CASE	)
             _a    : List[str]     					=nn.Convad(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						1	)

             # pass init params to Decoder
             _a    : List[str]     					=Decoder(
                 in_channels=SCREAMING_SNAKE_CASE     ,						out_channels=SCREAMING_SNAKE_CASE     ,						up_block_types=SCREAMING_SNAKE_CASE     ,						block_out_channels=SCREAMING_SNAKE_CASE     ,						layers_per_block=SCREAMING_SNAKE_CASE     ,						act_fn=SCREAMING_SNAKE_CASE     ,						norm_num_groups=SCREAMING_SNAKE_CASE     ,						norm_type=SCREAMING_SNAKE_CASE     ,						)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					VQEncoderOutput:




             '''simple docstring'''
             _a    : Optional[int]     					=self.encoder(SCREAMING_SNAKE_CASE	)
             _a    : int     					=self.quant_conv(SCREAMING_SNAKE_CASE	)

             if not return_dict:
                    return (h,)

             return VQEncoderOutput(latents=SCREAMING_SNAKE_CASE	)






      @apply_forward_hook
      def __UpperCAmelCase       ( self			:List[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = False     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             # also go through quantization layer
             if not force_not_quantize:
                    _a					,    _a					,    _a    : Tuple     					=self.quantize(SCREAMING_SNAKE_CASE	)
             else:
                    _a    : str     					=h
             _a    : Dict     					=self.post_quant_conv(SCREAMING_SNAKE_CASE	)
             _a    : Union[str, Any]     					=self.decoder(SCREAMING_SNAKE_CASE     ,						quant if self.config.norm_type == """spatial""" else None	)

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)






      def __UpperCAmelCase       ( self			:Optional[Any]     ,						SCREAMING_SNAKE_CASE			:torch.FloatTensor     ,						SCREAMING_SNAKE_CASE			:bool = True	)       ->					Union[DecoderOutput, torch.FloatTensor]:




             '''simple docstring'''
             _a    : Tuple     					=sample
             _a    : int     					=self.encode(SCREAMING_SNAKE_CASE	).latents
             _a    : List[Any]     					=self.decode(SCREAMING_SNAKE_CASE	).sample

             if not return_dict:
                    return (dec,)

             return DecoderOutput(sample=SCREAMING_SNAKE_CASE	)


