





"""simple docstring"""





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation


SCREAMING_SNAKE_CASE__					=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					=   {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__					=   {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/tokenizer.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/tokenizer.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__					=   {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

class 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE					=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE					=      ['input_ids', 'attention_mask']
      _SCREAMING_SNAKE_CASE					=      GPTaTokenizer





      def __init__(       self      ,						lowercase=None      ,						lowercase=None      ,						lowercase=None      ,						lowercase="<|endoftext|>"      ,						lowercase="<|endoftext|>"      ,						lowercase="<|endoftext|>"      ,						lowercase=False      ,						**lowercase      ,						)		->     Dict:
            super().__init__(
                lowercase      ,						lowercase      ,						tokenizer_file=lowercase      ,						unk_token=lowercase      ,						bos_token=lowercase      ,						eos_token=lowercase      ,						add_prefix_space=lowercase      ,						**lowercase      ,						)

            lowerCAmelCase  						=       kwargs.pop("""add_bos_token"""      ,						lowercase )

            lowerCAmelCase  						=       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
            if pre_tok_state.get("""add_prefix_space"""      ,						lowercase ) != add_prefix_space:
                  lowerCAmelCase  						=       getattr(lowercase      ,						pre_tok_state.pop("""type""" ) )
                  lowerCAmelCase  						=       add_prefix_space
                  lowerCAmelCase  						=       pre_tok_class(**lowercase )

            lowerCAmelCase  						=       add_prefix_space





      def     _snake_case			(       self      ,						*lowercase      ,						**lowercase )		->     BatchEncoding:
            lowerCAmelCase  						=       kwargs.get("""is_split_into_words"""      ,						lowercase )
            assert self.add_prefix_space or not is_split_into_words, (
                f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						*lowercase      ,						**lowercase )		->     BatchEncoding:
            lowerCAmelCase  						=       kwargs.get("""is_split_into_words"""      ,						lowercase )

            assert self.add_prefix_space or not is_split_into_words, (
                f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*lowercase      ,						**lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = None )		->     Tuple[str]:
            lowerCAmelCase  						=       self._tokenizer.model.save(lowercase      ,						name=lowercase )
            return tuple(lowercase )





      def     _snake_case			(       self      ,						lowercase )		->     List[int]:
            lowerCAmelCase  						=       []
            for is_user, text in conversation.iter_texts():
                  input_ids.extend(self.encode(lowercase      ,						add_special_tokens=lowercase ) + [self.eos_token_id] )

            if len(lowercase ) > self.model_max_length:
                  lowerCAmelCase  						=       input_ids[-self.model_max_length :]
            return input_ids













"""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 		lowercase					(					_UpperCAmelCase					):
      _SCREAMING_SNAKE_CASE					=      42







class 		lowercase					(					_UpperCAmelCase     ,  _UpperCAmelCase					):





      @register_to_config
      def __init__(       self      ,						lowercase = 3      ,						lowercase = 3      ,						lowercase = ("DownEncoderBlock2D",)      ,						lowercase = ("UpDecoderBlock2D",)      ,						lowercase = (64,)      ,						lowercase = 1      ,						lowercase = "silu"      ,						lowercase = 3      ,						lowercase = 32      ,						lowercase = 256      ,						lowercase = 32      ,						lowercase = None      ,						lowercase = 0.18_215      ,						lowercase = "group"      ,						)		->     Union[str, Any]:
            super().__init__()

            # pass init params to Encoder
            lowerCAmelCase  						=       Encoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						down_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						double_z=lowercase      ,						)

            lowerCAmelCase  						=       vq_embed_dim if vq_embed_dim is not None else latent_channels

            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )
            lowerCAmelCase  						=       VectorQuantizer(lowercase      ,						lowercase      ,						beta=0.25      ,						remap=lowercase      ,						sane_index_shape=lowercase )
            lowerCAmelCase  						=       nn.Convad(lowercase      ,						lowercase      ,						1 )

            # pass init params to Decoder
            lowerCAmelCase  						=       Decoder(
                in_channels=lowercase      ,						out_channels=lowercase      ,						up_block_types=lowercase      ,						block_out_channels=lowercase      ,						layers_per_block=lowercase      ,						act_fn=lowercase      ,						norm_num_groups=lowercase      ,						norm_type=lowercase      ,						)





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     VQEncoderOutput:
            lowerCAmelCase  						=       self.encoder(lowercase )
            lowerCAmelCase  						=       self.quant_conv(lowercase )

            if not return_dict:
                  return (h,)

            return VQEncoderOutput(latents=lowercase )





      @apply_forward_hook
      def     _snake_case			(       self      ,						lowercase      ,						lowercase = False      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            # also go through quantization layer
            if not force_not_quantize:
                  lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  						=       self.quantize(lowercase )
            else:
                  lowerCAmelCase  						=       h
            lowerCAmelCase  						=       self.post_quant_conv(lowercase )
            lowerCAmelCase  						=       self.decoder(lowercase      ,						quant if self.config.norm_type == """spatial""" else None )

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )





      def     _snake_case			(       self      ,						lowercase      ,						lowercase = True )		->     Union[DecoderOutput, torch.FloatTensor]:
            lowerCAmelCase  						=       sample
            lowerCAmelCase  						=       self.encode(lowercase ).latents
            lowerCAmelCase  						=       self.decode(lowercase ).sample

            if not return_dict:
                  return (dec,)

            return DecoderOutput(sample=lowercase )






