





import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase:	Dict = logging.get_logger(__name__)

__UpperCamelCase:	List[Any] = """▁"""

__UpperCamelCase:	Optional[int] = {"""vocab_file""": """sentencepiece.bpe.model"""}

__UpperCamelCase:	Union[str, Any] = {
    """vocab_file""": {
        """xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model""",
        """xlm-roberta-large-finetuned-conll02-dutch""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll02-spanish""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-english""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model"""
        ),
        """xlm-roberta-large-finetuned-conll03-german""": (
            """https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model"""
        ),
    }
}

__UpperCamelCase:	List[Any] = {
    """xlm-roberta-base""": 5_1_2,
    """xlm-roberta-large""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-dutch""": 5_1_2,
    """xlm-roberta-large-finetuned-conll02-spanish""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-english""": 5_1_2,
    """xlm-roberta-large-finetuned-conll03-german""": 5_1_2,
}


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				VOCAB_FILES_NAMES
	_A													=				PRETRAINED_VOCAB_FILES_MAP
	_A													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A													=				["input_ids", "attention_mask"]

	def __init__(						self:						str,					lowerCamelCase_:						Tuple,					lowerCamelCase_:						Any="<s>",					lowerCamelCase_:						List[str]="</s>",					lowerCamelCase_:						Dict="</s>",					lowerCamelCase_:						List[str]="<s>",					lowerCamelCase_:						Union[str, Any]="<unk>",					lowerCamelCase_:						List[Any]="<pad>",					lowerCamelCase_:						str="<mask>",					lowerCamelCase_:						Optional[Dict[str, Any]] = None,					**lowerCamelCase_:						Union[str, Any],					):
			# Mask token behave like a normal word, i.e. include the space before it
			lowercase__					:      Union[str, Any]      =		AddedToken(lowerCamelCase_,					lstrip=lowerCamelCase_,					rstrip=lowerCamelCase_			) if isinstance(lowerCamelCase_,					lowerCamelCase_			) else mask_token

			lowercase__					:      Tuple      =		{} if sp_model_kwargs is None else sp_model_kwargs

			super().__init__(
			    bos_token=lowerCamelCase_,					eos_token=lowerCamelCase_,					unk_token=lowerCamelCase_,					sep_token=lowerCamelCase_,					cls_token=lowerCamelCase_,					pad_token=lowerCamelCase_,					mask_token=lowerCamelCase_,					sp_model_kwargs=self.sp_model_kwargs,					**lowerCamelCase_,					)

			lowercase__					:      Dict      =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
			self.sp_model.Load(str(lowerCamelCase_			)			)
			lowercase__					:      Dict      =		vocab_file

			# Original fairseq vocab and spm vocab must be "aligned":
			# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
			# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
			# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
			# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

			# Mimic fairseq token-to-id alignment for the first 4 token
			lowercase__					:      Tuple      =		{'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

			# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
			lowercase__					:      int      =		1

			lowercase__					:      Optional[int]      =		len(self.sp_model			) + self.fairseq_offset
			lowercase__					:      Dict      =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}

	def __getstate__(						self:						Optional[int]			):
			lowercase__					:      List[Any]      =		self.__dict__.copy()
			lowercase__					:      List[str]      =		None
			lowercase__					:      List[str]      =		self.sp_model.serialized_model_proto()
			return state

	def __setstate__(						self:						Any,					lowerCamelCase_:						List[Any]			):
			lowercase__					:      int      =		d

			# for backward compatibility
			if not hasattr(self,					'sp_model_kwargs'			):
					lowercase__					:      Tuple      =		{}

			lowercase__					:      List[Any]      =		spm.SentencePieceProcessor(**self.sp_model_kwargs			)
			self.sp_model.LoadFromSerializedProto(self.sp_model_proto			)

	def    snake_case__(						self:						Tuple,					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None			):

			if token_ids_a is None:
					return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
			lowercase__					:      str      =		[self.cls_token_id]
			lowercase__					:      Tuple      =		[self.sep_token_id]
			return cls + token_ids_a + sep + sep + token_ids_a + sep

	def    snake_case__(						self:						str,					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None,					lowerCamelCase_:						bool = False			):

			if already_has_special_tokens:
					return super().get_special_tokens_mask(
					    token_ids_a=lowerCamelCase_,					token_ids_a=lowerCamelCase_,					already_has_special_tokens=lowerCamelCase_			)

			if token_ids_a is None:
					return [1] + ([0] * len(lowerCamelCase_			)) + [1]
			return [1] + ([0] * len(lowerCamelCase_			)) + [1, 1] + ([0] * len(lowerCamelCase_			)) + [1]

	def    snake_case__(						self:						str,					lowerCamelCase_:						List[int],					lowerCamelCase_:						Optional[List[int]] = None			):
			lowercase__					:      List[str]      =		[self.sep_token_id]
			lowercase__					:      Any      =		[self.cls_token_id]

			if token_ids_a is None:
					return len(cls + token_ids_a + sep			) * [0]
			return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]

	@property
	def    snake_case__(						self:						Tuple			):
			return len(self.sp_model			) + self.fairseq_offset + 1  # Add the <mask> token

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      Optional[Any]      =		{self.convert_ids_to_tokens(lowerCamelCase_			): i for i in range(self.vocab_size			)}
			vocab.update(self.added_tokens_encoder			)
			return vocab

	def    snake_case__(						self:						str,					lowerCamelCase_:						str			):
			return self.sp_model.encode(lowerCamelCase_,					out_type=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any],					lowerCamelCase_:						Union[str, Any]			):
			if token in self.fairseq_tokens_to_ids:
					return self.fairseq_tokens_to_ids[token]
			lowercase__					:      str      =		self.sp_model.PieceToId(lowerCamelCase_			)

			# Need to return unknown token if the SP model returned 0
			return spm_id + self.fairseq_offset if spm_id else self.unk_token_id

	def    snake_case__(						self:						Dict,					lowerCamelCase_:						Union[str, Any]			):
			if index in self.fairseq_ids_to_tokens:
					return self.fairseq_ids_to_tokens[index]
			return self.sp_model.IdToPiece(index - self.fairseq_offset			)

	def    snake_case__(						self:						List[Any],					lowerCamelCase_:						int			):
			lowercase__					:      Dict      =		''.join(lowerCamelCase_			).replace(lowerCamelCase_,					' '			).strip()
			return out_string





	def    snake_case__(						self:						Dict,					lowerCamelCase_:						str,					lowerCamelCase_:						Optional[str] = None			):
			if not os.path.isdir(lowerCamelCase_			):
					logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""			)
					return
			lowercase__					:      Dict      =		os.path.join(
			    lowerCamelCase_,					(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

			if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase_			) and os.path.isfile(self.vocab_file			):
					copyfile(self.vocab_file,					lowerCamelCase_			)
			elif not os.path.isfile(self.vocab_file			):
					with open(lowerCamelCase_,					'wb'			) as fi:
							lowercase__					:      List[str]      =		self.sp_model.serialized_model_proto()
							fi.write(lowerCamelCase_			)

			return (out_vocab_file,)









import argparse
import math
import os

import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel


def       SCREAMING_SNAKE_CASE__  (       )   ->							int:




		'''simple docstring'''
		lowercase__					:      str      =		argparse.ArgumentParser()
		parser.add_argument(
		    '-m'   ,    '--pretrained_model_name_or_path'   ,    type=_lowercase   ,    default=_lowercase   ,    required=_lowercase   ,    help='Path to pretrained model or model identifier from huggingface.co/models.'   ,    )
		parser.add_argument(
		    '-c'   ,    '--caption'   ,    type=_lowercase   ,    default='robotic cat with wings'   ,    help='Text used to generate images.'   ,    )
		parser.add_argument(
		    '-n'   ,    '--images_num'   ,    type=_lowercase   ,    default=4   ,    help='How much images to generate.'   ,    )
		parser.add_argument(
		    '-s'   ,    '--seed'   ,    type=_lowercase   ,    default=42   ,    help='Seed for random process.'   ,    )
		parser.add_argument(
		    '-ci'   ,    '--cuda_id'   ,    type=_lowercase   ,    default=0   ,    help='cuda_id.'   ,    )
		lowercase__					:      Any      =		parser.parse_args()
		return args


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Union[str, Any]   ,    _lowercase     : Optional[int]   ,    _lowercase     : Union[str, Any]			)   ->							str:




		'''simple docstring'''
		if not len(_lowercase			) == rows * cols:
				raise ValueError('The specified number of rows and columns are not correct.'			)

		lowercase__   ,				lowercase__					:      Any      =		imgs[0].size
		lowercase__					:      int      =		Image.new('RGB'   ,    size=(cols * w, rows * h)			)
		lowercase__   ,				lowercase__					:      Any      =		grid.size

		for i, img in enumerate(_lowercase			):
				grid.paste(_lowercase   ,    box=(i % cols * w, i // cols * h)			)
		return grid


def       SCREAMING_SNAKE_CASE__  (       _lowercase     : Optional[int]   ,    _lowercase     : Tuple="robotic cat with wings"   ,    _lowercase     : Optional[Any]=7.5   ,    _lowercase     : int=50   ,    _lowercase     : str=1   ,    _lowercase     : Any=42   ,    )   ->							str:




		'''simple docstring'''
		lowercase__					:      int      =		torch.Generator(pipeline.device			).manual_seed(_lowercase			)
		lowercase__					:      Any      =		pipeline(
		    _lowercase   ,    guidance_scale=_lowercase   ,    num_inference_steps=_lowercase   ,    generator=_lowercase   ,    num_images_per_prompt=_lowercase   ,    ).images
		lowercase__					:      Dict      =		int(math.sqrt(_lowercase			)			)
		lowercase__					:      int      =		image_grid(_lowercase   ,    rows=_rows   ,    cols=num_images_per_prompt // _rows			)
		return grid, images


__UpperCamelCase:	int = parse_args()
# Load models and create wrapper for stable diffusion
__UpperCamelCase:	Optional[int] = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder="""tokenizer""")
__UpperCamelCase:	List[str] = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""text_encoder""")
__UpperCamelCase:	int = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="""vae""")
__UpperCamelCase:	int = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder="""unet""")

__UpperCamelCase:	Tuple = StableDiffusionPipeline.from_pretrained(
    args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
__UpperCamelCase:	List[str] = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, """best_model.pt""")):
			__UpperCamelCase:	Dict = load(args.pretrained_model_name_or_path, model=unet)
			unet.eval()
			setattr(pipeline, """unet""", unet)
else:
			__UpperCamelCase:	Dict = unet.to(torch.device("""cuda""", args.cuda_id))
__UpperCamelCase:	Optional[int] = pipeline.to(unet.device)
__UpperCamelCase,	__UpperCamelCase:	Union[str, Any] = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, """{}.png""".format("""_""".join(args.caption.split()))))
__UpperCamelCase:	List[str] = os.path.join(args.pretrained_model_name_or_path, """_""".join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
			image.save(os.path.join(dirname, """{}.png""".format(idx + 1)))


