



import importlib

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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=False		)	->  Dict:



    """simple docstring"""






    A			:	int										=       OmegaConf.load(_lowerCAmelCase		)
    if display:
        print(yaml.dump(OmegaConf.to_container(_lowerCAmelCase		)		)		)
    return config




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=None				,					_lowerCAmelCase=None		)	->  Tuple:



    """simple docstring"""






    if conf_path is None:
        A			:	Union[str, Any]										=       """./model_checkpoints/vqgan_only.yaml"""
    A			:	Tuple										=       load_config(_lowerCAmelCase				,					display=_lowerCAmelCase		)
    A			:	List[str]										=       VQModel(**config.model.params		)
    if ckpt_path is None:
        A			:	Tuple										=       """./model_checkpoints/vqgan_only.pt"""
    A			:	int										=       torch.load(_lowerCAmelCase				,					map_location=_lowerCAmelCase		)
    if ".ckpt" in ckpt_path:
        A			:	List[Any]										=       sd["""state_dict"""]
    model.load_state_dict(_lowerCAmelCase				,					strict=_lowerCAmelCase		)
    model.to(_lowerCAmelCase		)
    del sd
    return model




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase		)	->  List[str]:



    """simple docstring"""






    A					,							A					,							A			:	Optional[Any]										=       model.encode(_lowerCAmelCase		)
    print(f'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}'''		)
    A			:	Optional[Any]										=       model.decode(_lowerCAmelCase		)
    return xrec




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase=False		)	->  str:



    """simple docstring"""






    A					,							A			:	Optional[int]										=       string.rsplit("""."""				,					1		)
    if reload:
        A			:	Union[str, Any]										=       importlib.import_module(_lowerCAmelCase		)
        importlib.reload(_lowerCAmelCase		)
    return getattr(importlib.import_module(_lowerCAmelCase				,					package=_lowerCAmelCase		)				,					cls		)




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  int:



    """simple docstring"""






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




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase=True				,					_lowerCAmelCase=True		)	->  List[str]:



    """simple docstring"""






    A			:	Optional[Any]										=       instantiate_from_config(_lowerCAmelCase		)
    if sd is not None:
        model.load_state_dict(_lowerCAmelCase		)
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}




def      __UpperCamelCase     (  _lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase				,					_lowerCAmelCase		)	->  Optional[int]:



    """simple docstring"""






    if ckpt:
        A			:	Any										=       torch.load(_lowerCAmelCase				,					map_location="""cpu"""		)
        A			:	List[str]										=       pl_sd["""global_step"""]
        print(f'''loaded model from global step {global_step}.'''		)
    else:
        A			:	List[str]										=       {"""state_dict""": None}
        A			:	str										=       None
    A			:	List[Any]										=       load_model_from_config(config.model				,					pl_sd["""state_dict"""]				,					gpu=_lowerCAmelCase				,					eval_mode=_lowerCAmelCase		)["""model"""]
    return model, global_step







import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_:Dict					=						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_:int					=						{
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

SCREAMING_SNAKE_CASE_:Tuple					=						{
    """vocab_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
    },
    """merges_file""": {
        """facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
    },
    """tokenizer_config_file""": {
        """facebook/blenderbot_small-90M""": (
            """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
        )
    },
}

SCREAMING_SNAKE_CASE_:Optional[int]					=						{"""facebook/blenderbot_small-90M""": 512}




def      __UpperCamelCase     (  _lowerCAmelCase		)	->  Dict:



    """simple docstring"""






    A			:	Optional[int]										=       set()
    A			:	Any										=       word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)		)
        A			:	List[Any]										=       char

    A			:	Optional[int]										=       set(_lowerCAmelCase		)
    return pairs






class SCREAMING_SNAKE_CASE__					(		SCREAMING_SNAKE_CASE__   ):




    '''simple docstring'''






    __lowerCamelCase      :			Union[str, Any] =				VOCAB_FILES_NAMES
    __lowerCamelCase      :			Any =				PRETRAINED_VOCAB_FILES_MAP
    __lowerCamelCase      :			Optional[Any] =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __lowerCamelCase      :			int =				["input_ids", "attention_mask"]






    def __init__(	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__="__start__",		lowerCamelCase__="__end__",		lowerCamelCase__="__unk__",		lowerCamelCase__="__null__",		**lowerCamelCase__,		):
        super().__init__(unk_token=lowerCamelCase__,		bos_token=lowerCamelCase__,		eos_token=lowerCamelCase__,		pad_token=lowerCamelCase__,		**lowerCamelCase__		)

        with open(lowerCamelCase__,		encoding="""utf-8"""		) as vocab_handle:
            A			:	Tuple										=       json.load(lowerCamelCase__		)
        A			:	Optional[Any]										=       {v: k for k, v in self.encoder.items()}
        with open(lowerCamelCase__,		encoding="""utf-8"""		) as merges_handle:
            A			:	str										=       merges_handle.read().split("""\n"""		)[1:-1]
        A			:	List[str]										=       [tuple(merge.split()		) for merge in merges]
        A			:	int										=       dict(zip(lowerCamelCase__,		range(len(lowerCamelCase__		)		)		)		)
        A			:	Optional[int]										=       {}






    @property
    def       _lowerCAmelCase (	self		):
        return len(self.encoder		)






    def       _lowerCAmelCase (	self		):
        return dict(self.encoder,		**self.added_tokens_encoder		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        if token in self.cache:
            return self.cache[token]
        A			:	Optional[int]										=       re.sub("""([.,!?()])""",		R""" \1""",		lowerCamelCase__		)
        A			:	List[Any]										=       re.sub("""(')""",		R""" \1 """,		lowerCamelCase__		)
        A			:	int										=       re.sub(R"""\s{2,}""",		""" """,		lowerCamelCase__		)
        if "\n" in token:
            A			:	Dict										=       token.replace("""\n""",		""" __newln__"""		)

        A			:	Tuple										=       token.split(""" """		)
        A			:	Union[str, Any]										=       []
        for token in tokens:
            if not len(lowerCamelCase__		):
                continue

            A			:	Optional[int]										=       token.lower()
            A			:	Optional[Any]										=       tuple(lowerCamelCase__		)
            A			:	int										=       tuple(list(word[:-1]		) + [word[-1] + """</w>"""]		)
            A			:	Tuple										=       get_pairs(lowerCamelCase__		)

            if not pairs:
                words.append(lowerCamelCase__		)
                continue

            while True:
                A			:	Any										=       min(lowerCamelCase__,		key=lambda lowerCamelCase__		: self.bpe_ranks.get(lowerCamelCase__,		float("""inf"""		)		)		)
                if bigram not in self.bpe_ranks:
                    break
                A					,							A			:	Any										=       bigram
                A			:	Optional[Any]										=       []
                A			:	Any										=       0

                while i < len(lowerCamelCase__		):
                    try:
                        A			:	List[str]										=       word.index(lowerCamelCase__,		lowerCamelCase__		)
                        new_word.extend(word[i:j]		)
                        A			:	Dict										=       j
                    except ValueError:
                        new_word.extend(word[i:]		)
                        break

                    if word[i] == first and i < len(lowerCamelCase__		) - 1 and word[i + 1] == second:
                        new_word.append(first + second		)
                        i += 2
                    else:
                        new_word.append(word[i]		)
                        i += 1
                A			:	Tuple										=       tuple(lowerCamelCase__		)
                A			:	int										=       new_word
                if len(lowerCamelCase__		) == 1:
                    break
                else:
                    A			:	Dict										=       get_pairs(lowerCamelCase__		)
            A			:	Any										=       """@@ """.join(lowerCamelCase__		)
            A			:	Dict										=       word[:-4]

            A			:	Union[str, Any]										=       word
            words.append(lowerCamelCase__		)
        return " ".join(lowerCamelCase__		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	int										=       []

        A			:	Optional[Any]										=       re.findall(R"""\S+\n?""",		lowerCamelCase__		)

        for token in words:
            split_tokens.extend(list(self.bpe(lowerCamelCase__		).split(""" """		)		)		)
        return split_tokens






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	List[Any]										=       token.lower()
        return self.encoder.get(lowerCamelCase__,		self.encoder.get(self.unk_token		)		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        return self.decoder.get(lowerCamelCase__,		self.unk_token		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Dict										=       """ """.join(lowerCamelCase__		).replace("""@@ """,		""""""		).strip()
        return out_string






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__ = None		):
        if not os.path.isdir(lowerCamelCase__		):
            logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''		)
            return
        A			:	str										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]		)
        A			:	int										=       os.path.join(
            lowerCamelCase__,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]		)

        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as f:
            f.write(json.dumps(self.encoder,		indent=2,		sort_keys=lowerCamelCase__,		ensure_ascii=lowerCamelCase__		) + """\n"""		)

        A			:	str										=       0
        with open(lowerCamelCase__,		"""w""",		encoding="""utf-8"""		) as writer:
            writer.write("""#version: 0.2\n"""		)
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(),		key=lambda lowerCamelCase__		: kv[1]		):
                if index != token_index:
                    logger.warning(
                        f'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                        """ Please check that the tokenizer is not corrupted!"""		)
                    A			:	List[Any]										=       token_index
                writer.write(""" """.join(lowerCamelCase__		) + """\n"""		)
                index += 1

        return vocab_file, merge_file


