

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

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


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)


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

SCREAMING_SNAKE_CASE_            = {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

SCREAMING_SNAKE_CASE_            = '</w>'
SCREAMING_SNAKE_CASE_            = '@@ '

def 							__SCREAMING_SNAKE_CASE							(			lowerCAmelCase:					List[str]	)				->    int:
				_UpperCAmelCase    :						Dict							=      set()
				_UpperCAmelCase    :						Any							=      word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)	)
								_UpperCAmelCase    :						Union[str, Any]							=      char
				return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE_            = {'facebook/s2t-wav2vec2-large-en-de': 1024}





class 			a					(		UpperCAmelCase			):
						_lowercase        =     VOCAB_FILES_NAMES
						_lowercase        =     PRETRAINED_VOCAB_FILES_MAP
						_lowercase        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowercase        =     ["input_ids", "attention_mask"]


						def __init__(    self  ,   A_  ,   A_="<s>"  ,   A_="<pad>"  ,   A_="</s>"  ,   A_="<unk>"  ,   A_=False  ,   A_=None  ,   **A_  ,   ):







										'''simple docstring'''


										super().__init__(
										    unk_token=A_  ,   bos_token=A_  ,   eos_token=A_  ,   pad_token=A_  ,   do_lower_case=A_  ,   **A_  ,   )

										_UpperCAmelCase    :						Dict							=      do_lower_case

										with open(A_  ,   encoding="utf-8"		) as vocab_handle:
														_UpperCAmelCase    :						List[Any]							=      json.load(A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      {v: k for k, v in self.encoder.items()}

										if merges_file is None:
														logger.info(f'No merges files provided. {self.__class__.__name__} can only be used for decoding.'		)

														_UpperCAmelCase    :						Union[str, Any]							=      None
														_UpperCAmelCase    :						Union[str, Any]							=      None
										else:
														with open(A_  ,   encoding="utf-8"		) as merges_handle:
																		_UpperCAmelCase    :						List[Any]							=      merges_handle.read().split("\n"		)[:-1]

														_UpperCAmelCase    :						str							=      [tuple(merge.split()[:2]		) for merge in merges]
														_UpperCAmelCase    :						Any							=      dict(zip(A_  ,   range(len(A_		)		)		)		)
														_UpperCAmelCase    :						Any							=      {}


						@property
						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return len(self.decoder		)


						def     _UpperCAmelCase			(    self		):







										'''simple docstring'''


										return dict(self.encoder  ,   **self.added_tokens_encoder		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      tuple(token[:-1]		) + (token[-1] + BPE_TOKEN_MERGES,)
										if token in self.cache:
														return self.cache[token]
										_UpperCAmelCase    :						int							=      get_pairs(A_		)

										if not pairs:
														return token

										while True:
														_UpperCAmelCase    :						Dict							=      min(A_  ,   key=lambda A_		: self.bpe_ranks.get(A_  ,   float("inf"		)		)		)
														if bigram not in self.bpe_ranks:
																		break
														_UpperCAmelCase						,							_UpperCAmelCase    :						List[Any]							=      bigram
														_UpperCAmelCase    :						Any							=      []
														_UpperCAmelCase    :						str							=      0
														while i < len(A_		):
																		try:
																						_UpperCAmelCase    :						str							=      word.index(A_  ,   A_		)
																		except ValueError:
																						new_word.extend(word[i:]		)
																						break
																		else:
																						new_word.extend(word[i:j]		)
																						_UpperCAmelCase    :						List[Any]							=      j

																		if word[i] == first and i < len(A_		) - 1 and word[i + 1] == second:
																						new_word.append(first + second		)
																						i += 2
																		else:
																						new_word.append(word[i]		)
																						i += 1
														_UpperCAmelCase    :						int							=      tuple(A_		)
														_UpperCAmelCase    :						Dict							=      new_word
														if len(A_		) == 1:
																		break
														else:
																		_UpperCAmelCase    :						Optional[Any]							=      get_pairs(A_		)
										_UpperCAmelCase    :						List[Any]							=      " ".join(A_		)
										if word == "\n  " + BPE_TOKEN_MERGES:
														_UpperCAmelCase    :						Tuple							=      "\n" + BPE_TOKEN_MERGES

										if word.endswith(A_		):
														_UpperCAmelCase    :						Any							=      word.replace(A_  ,   ""		)

										_UpperCAmelCase    :						Tuple							=      word.replace(" "  ,   A_		)
										_UpperCAmelCase    :						List[Any]							=      word
										return word


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										if self.bpe_ranks is None:
														raise ValueError(
														    "This tokenizer was instantiated without a `merges.txt` file, so"
														    " that it can only be used for decoding, not for encoding."
														    "Make sure to provide `merges.txt` file at instantiation to enable "
														    "encoding."		)

										if self.do_lower_case:
														_UpperCAmelCase    :						Dict							=      text.lower()

										_UpperCAmelCase    :						List[Any]							=      text.split()

										_UpperCAmelCase    :						List[Any]							=      []
										for token in text:
														if token:
																		split_tokens.extend(list(self.bpe(A_		).split(" "		)		)		)

										return split_tokens


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										return self.encoder.get(A_  ,   self.encoder.get(self.unk_token		)		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						str							=      self.decoder.get(A_  ,   self.unk_token		)
										return result


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Dict							=      " ".join(A_		)

										# make sure @@ tokens are concatenated
										_UpperCAmelCase    :						Tuple							=      "".join(string.split(A_		)		)

										return string





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None		):







										'''simple docstring'''


										if not os.path.isdir(A_		):
														logger.error(f'Vocabulary path ({save_directory}) should be a directory'		)
														return
										_UpperCAmelCase    :						Dict							=      os.path.join(
										    A_  ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)
										_UpperCAmelCase    :						int							=      os.path.join(
										    A_  ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]		)

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

										_UpperCAmelCase    :						int							=      0
										if self.bpe_ranks is None:
														return (vocab_file,)

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

										return (vocab_file, merges_file)





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_            = argparse.ArgumentParser()

					parser.add_argument(
					    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    '--original_config_file',
					    default=None,
					    type=str,
					    help='The YAML config file corresponding to the original architecture.',
					)
					parser.add_argument(
					    '--num_in_channels',
					    default=None,
					    type=int,
					    help='The number of input channels. If `None` number of input channels will be automatically inferred.',
					)
					parser.add_argument(
					    '--scheduler_type',
					    default='pndm',
					    type=str,
					    help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
					)
					parser.add_argument(
					    '--pipeline_type',
					    default=None,
					    type=str,
					    help=(
					        'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
					        '. If `None` pipeline will be automatically inferred.'
					    ),
					)
					parser.add_argument(
					    '--image_size',
					    default=None,
					    type=int,
					    help=(
					        'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
					        ' Base. Use 768 for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--prediction_type',
					    default=None,
					    type=str,
					    help=(
					        'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
					        ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
					    ),
					)
					parser.add_argument(
					    '--extract_ema',
					    action='store_true',
					    help=(
					        'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
					        ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
					        ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
					    ),
					)
					parser.add_argument(
					    '--upcast_attention',
					    action='store_true',
					    help=(
					        'Whether the attention computation should always be upcasted. This is necessary when running stable'
					        ' diffusion 2.1.'
					    ),
					)
					parser.add_argument(
					    '--from_safetensors',
					    action='store_true',
					    help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
					)
					parser.add_argument(
					    '--to_safetensors',
					    action='store_true',
					    help='Whether to store pipeline in safetensors format or not.',
					)
					parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
					parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
					parser.add_argument(
					    '--stable_unclip',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
					)
					parser.add_argument(
					    '--stable_unclip_prior',
					    type=str,
					    default=None,
					    required=False,
					    help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
					)
					parser.add_argument(
					    '--clip_stats_path',
					    type=str,
					    help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
					    required=False,
					)
					parser.add_argument(
					    '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
					)
					parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
					parser.add_argument(
					    '--vae_path',
					    type=str,
					    default=None,
					    required=False,
					    help='Set to a path, hub id to an already converted vae to not convert it again.',
					)
					SCREAMING_SNAKE_CASE_            = parser.parse_args()

					SCREAMING_SNAKE_CASE_            = download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)


