

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


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

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

lowercase			:				Optional[Any]   =     {
    "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",
    },
}

lowercase			:				Optional[Any]   =     {
    "gpt2": 10_24,
    "gpt2-medium": 10_24,
    "gpt2-large": 10_24,
    "gpt2-xl": 10_24,
    "distilgpt2": 10_24,
}







class 					__lowercase  (						lowerCamelCase__				):





							"""simple docstring"""

							UpperCAmelCase_						:		Dict								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		List[str]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_						:		Any								=	['''input_ids''', '''attention_mask''']
							UpperCAmelCase_						:		List[str]								=	GPTaTokenizer




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase="<|endoftext|>"    ,						__UpperCAmelCase="<|endoftext|>"    ,						__UpperCAmelCase="<|endoftext|>"    ,						__UpperCAmelCase=False    ,						**__UpperCAmelCase    ,						)		->       Optional[Any]:
														super().__init__(
														    a__    ,						a__    ,						tokenizer_file=a__    ,						unk_token=a__    ,						bos_token=a__    ,						eos_token=a__    ,						add_prefix_space=a__    ,						**a__    ,						)

														A      :					Dict      		=					kwargs.pop('''add_bos_token'''    ,						a__						)

														A      :					Any      		=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
														if pre_tok_state.get('''add_prefix_space'''    ,						a__						) != add_prefix_space:
																					A      :					List[Any]      		=					getattr(a__    ,						pre_tok_state.pop('''type'''						)						)
																					A      :					Dict      		=					add_prefix_space
																					A      :					str      		=					pre_tok_class(**a__						)

														A      :					int      		=					add_prefix_space




							def       snake_case			(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       Dict:
														A      :					Union[str, Any]      		=					kwargs.get('''is_split_into_words'''    ,						a__						)
														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(*a__    ,						**a__						)




							def       snake_case			(					self    ,						*__UpperCAmelCase    ,						**__UpperCAmelCase						)		->       int:
														A      :					Optional[Any]      		=					kwargs.get('''is_split_into_words'''    ,						a__						)

														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(*a__    ,						**a__						)




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       Optional[int]:
														A      :					Union[str, Any]      		=					self._tokenizer.model.save(a__    ,						name=a__						)
														return tuple(a__						)






							def       snake_case			(					self    ,						__UpperCAmelCase						)		->       List[Any]:
														A      :					int      		=					[]
														for is_user, text in conversation.iter_texts():
																					input_ids.extend(self.encode(a__    ,						add_special_tokens=a__						) + [self.eos_token_id]						)

														if len(a__						) > self.model_max_length:
																					A      :					Dict      		=					input_ids[-self.model_max_length :]
														return input_ids













import inspect
from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				a__   ,				a__   ,				a__):



             """simple docstring"""





             super().__init__()
             self.register_modules(vqvae=a__   ,				unet=a__   ,				scheduler=a__)



      @torch.no_grad()
      def __call__(		self   ,				a__ = 1   ,				a__ = None   ,				a__ = 0.0   ,				a__ = 50   ,				a__ = "pil"   ,				a__ = True   ,				**a__   ,				):



             """simple docstring"""





             _lowerCamelCase				:  Tuple						=		randn_tensor(
                 (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)   ,				generator=a__   ,				)
             _lowerCamelCase				:  Optional[int]						=		latents.to(self.device)

             # scale the initial noise by the standard deviation required by the scheduler
             _lowerCamelCase				:  Tuple						=		latents * self.scheduler.init_noise_sigma

             self.scheduler.set_timesteps(a__)

             # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
             _lowerCamelCase				:  Optional[int]						=		'''eta''' in set(inspect.signature(self.scheduler.step).parameters.keys())

             _lowerCamelCase				:  Union[str, Any]						=		{}
             if accepts_eta:
                    _lowerCamelCase				:  List[Any]						=		eta

             for t in self.progress_bar(self.scheduler.timesteps):
                    _lowerCamelCase				:  List[Any]						=		self.scheduler.scale_model_input(a__   ,				a__)
                    # predict the noise residual
                    _lowerCamelCase				:  Optional[int]						=		self.unet(a__   ,				a__).sample
                    # compute the previous noisy sample x_t -> x_t-1
                    _lowerCamelCase				:  List[str]						=		self.scheduler.step(a__   ,				a__   ,				a__   ,				**a__).prev_sample

             # decode the image latents with the VAE
             _lowerCamelCase				:  Optional[int]						=		self.vqvae.decode(a__).sample

             _lowerCamelCase				:  Dict						=		(image / 2 + 0.5).clamp(0   ,				1)
             _lowerCamelCase				:  List[str]						=		image.cpu().permute(0   ,				2   ,				3   ,				1).numpy()
             if output_type == "pil":
                    _lowerCamelCase				:  List[Any]						=		self.numpy_to_pil(a__)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a__)

