

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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
				from transformers.pipelines.conversational import Conversation


_a:    List[Any] 							=			logging.get_logger(__name__)

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

_a:    Dict 							=			{
    """tokenizer_file""": {
        """EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json""",
    },
}

_a:    str 							=			{
    """gpt-neox-20b""": 2048,
}

class 						__UpperCamelCase    (					lowercase						):
	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']




	def __init__(		self	:						Tuple		,	lowerCAmelCase	:						Any=None		,	lowerCAmelCase	:						Dict=None		,	lowerCAmelCase	:						str=None		,	lowerCAmelCase	:						int="<|endoftext|>"		,	lowerCAmelCase	:						Optional[Any]="<|endoftext|>"		,	lowerCAmelCase	:						Any="<|endoftext|>"		,	lowerCAmelCase	:						int=False		,	**lowerCAmelCase	:						str		,	):


		'''simple docstring'''







		super().__init__(
		    lowerCAmelCase		,	lowerCAmelCase		,	tokenizer_file=lowerCAmelCase		,	unk_token=lowerCAmelCase		,	bos_token=lowerCAmelCase		,	eos_token=lowerCAmelCase		,	add_prefix_space=lowerCAmelCase		,	**lowerCAmelCase		,	)

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

		UpperCAmelCase_       				=			add_prefix_space




	def       __A					(		self	:						Any		,	lowerCAmelCase	:						str		,	lowerCAmelCase	:						Optional[str] = None							):


		'''simple docstring'''







		UpperCAmelCase_       				=			self._tokenizer.model.save(lowerCAmelCase		,	name=lowerCAmelCase							)
		return tuple(lowerCAmelCase							)




	def       __A					(		self	:						List[Any]		,	lowerCAmelCase	:						"Conversation"							):


		'''simple docstring'''







		UpperCAmelCase_       				=			[]
		for is_user, text in conversation.iter_texts():
			input_ids.extend(self.encode(lowerCAmelCase		,	add_special_tokens=lowerCAmelCase							) + [self.eos_token_id]							)

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


import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_       				=			OmegaConf.load(A    )
	if display:
		print(yaml.dump(OmegaConf.to_container(A    )    )    )
	return config



def       __lowerCAmelCase      (						A       ,     A=None       ,     A=None    ):
	if conf_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.yaml"
	UpperCAmelCase_       				=			load_config(A       ,     display=A    )
	UpperCAmelCase_       				=			VQModel(**config.model.params    )
	if ckpt_path is None:
		UpperCAmelCase_       				=			"./model_checkpoints/vqgan_only.pt"
	UpperCAmelCase_       				=			torch.load(A       ,     map_location=A    )
	if ".ckpt" in ckpt_path:
		UpperCAmelCase_       				=			sd["state_dict"]
	model.load_state_dict(A       ,     strict=A    )
	model.to(A    )
	del sd
	return model



def       __lowerCAmelCase      (						A       ,     A    ):
	UpperCAmelCase_				,   UpperCAmelCase_				,   UpperCAmelCase_       				=			model.encode(A    )
	print(F"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"    )
	UpperCAmelCase_       				=			model.decode(A    )
	return xrec



def       __lowerCAmelCase      (						A       ,     A=False    ):
	UpperCAmelCase_				,   UpperCAmelCase_       				=			string.rsplit("."       ,     1    )
	if reload:
		UpperCAmelCase_       				=			importlib.import_module(A    )
		importlib.reload(A    )
	return getattr(importlib.import_module(A       ,     package=A    )       ,     cls    )



def       __lowerCAmelCase      (						A    ):
	if "target" not in config:
		raise KeyError("Expected key `target` to instantiate."    )
	return get_obj_from_str(config["target"]    )(**config.get("params"       ,     {}    )    )



def       __lowerCAmelCase      (						A       ,     A       ,     A=True       ,     A=True    ):
	UpperCAmelCase_       				=			instantiate_from_config(A    )
	if sd is not None:
		model.load_state_dict(A    )
	if gpu:
		model.cuda()
	if eval_mode:
		model.eval()
	return {"model": model}




def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	# load the specified checkpoint
	if ckpt:
		UpperCAmelCase_       				=			torch.load(A       ,     map_location="cpu"    )
		UpperCAmelCase_       				=			pl_sd["global_step"]
		print(F"loaded model from global step {global_step}."    )
	else:
		UpperCAmelCase_       				=			{"state_dict": None}
		UpperCAmelCase_       				=			None
	UpperCAmelCase_       				=			load_model_from_config(config.model       ,     pl_sd["state_dict"]       ,     gpu=A       ,     eval_mode=A    )["model"]
	return model, global_step