
'''simple docstring'''







import json
import os
import re
import warnings
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


_snake_case					: Dict     			=					logging.get_logger(__name__)

_snake_case					: int     			=					{
    """source_spm""": """source.spm""",
    """target_spm""": """target.spm""",
    """vocab""": """vocab.json""",
    """target_vocab_file""": """target_vocab.json""",
    """tokenizer_config_file""": """tokenizer_config.json""",
}

_snake_case					: List[Any]     			=					{
    """source_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
    },
    """target_spm""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
    },
    """vocab""": {
        """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
    },
    """tokenizer_config_file""": {
        """Helsinki-NLP/opus-mt-en-de""": (
            """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
        )
    },
}

_snake_case					: str     			=					{"""Helsinki-NLP/opus-mt-en-de""": 512}
_snake_case					: str     			=					{}


class       lowerCAmelCase			(					__UpperCAmelCase      ):
		a				:       Optional[int]							=       VOCAB_FILES_NAMES
		a				:       Any							=       PRETRAINED_VOCAB_FILES_MAP
		a				:       Any							=       PRETRAINED_INIT_CONFIGURATION
		a				:       Optional[int]							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		a				:       Any							=       ["""input_ids""", """attention_mask"""]
		a				:       Dict							=       re.compile(""">>.+<<"""      )  # type: re.Pattern




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase=None       ,    UpperCamelCase="<unk>"       ,    UpperCamelCase="</s>"       ,    UpperCamelCase="<pad>"       ,    UpperCamelCase=512       ,    UpperCamelCase = None       ,    UpperCamelCase=False       ,    **UpperCamelCase       ,    ):
								_SCREAMING_SNAKE_CASE					=			{} if sp_model_kwargs is None else sp_model_kwargs

								super().__init__(
								    # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
								    source_lang=UpperCamelCase       ,    target_lang=UpperCamelCase       ,    unk_token=UpperCamelCase       ,    eos_token=UpperCamelCase       ,    pad_token=UpperCamelCase       ,    model_max_length=UpperCamelCase       ,    sp_model_kwargs=self.sp_model_kwargs       ,    target_vocab_file=UpperCamelCase       ,    separate_vocabs=UpperCamelCase       ,    **UpperCamelCase       ,    )
								assert Path(UpperCamelCase   ).exists(), F'cannot find spm source {source_spm}'

								_SCREAMING_SNAKE_CASE					=			separate_vocabs
								_SCREAMING_SNAKE_CASE					=			load_json(UpperCamelCase   )
								if self.unk_token not in self.encoder:
														raise KeyError("<unk> token must be in vocab"   )
								assert self.pad_token in self.encoder

								if separate_vocabs:
														_SCREAMING_SNAKE_CASE					=			load_json(UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			{v: k for k, v in self.target_encoder.items()}
														_SCREAMING_SNAKE_CASE					=			[]
								else:
														_SCREAMING_SNAKE_CASE					=			{v: k for k, v in self.encoder.items()}
														_SCREAMING_SNAKE_CASE					=			[k for k in self.encoder if k.startswith(">>"   ) and k.endswith("<<"   )]

								_SCREAMING_SNAKE_CASE					=			source_lang
								_SCREAMING_SNAKE_CASE					=			target_lang
								_SCREAMING_SNAKE_CASE					=			[source_spm, target_spm]

								# load SentencePiece model for pre-processing
								_SCREAMING_SNAKE_CASE					=			load_spm(UpperCamelCase       ,    self.sp_model_kwargs   )
								_SCREAMING_SNAKE_CASE					=			load_spm(UpperCamelCase       ,    self.sp_model_kwargs   )
								_SCREAMING_SNAKE_CASE					=			self.spm_source
								_SCREAMING_SNAKE_CASE					=			self.encoder

								# Multilingual target side: default to using first supported language code.

								self._setup_normalizer()




		def      lowercase							(					self   ):
								try:
														from sacremoses import MosesPunctNormalizer

														_SCREAMING_SNAKE_CASE					=			MosesPunctNormalizer(self.source_lang   ).normalize
								except (ImportError, FileNotFoundError):
														warnings.warn("Recommended: pip install sacremoses."   )
														_SCREAMING_SNAKE_CASE					=			lambda UpperCamelCase   : x




		def      lowercase							(					self       ,    UpperCamelCase   ):
								return self.punc_normalizer(UpperCamelCase   ) if x else ""




		def      lowercase							(					self       ,    UpperCamelCase   ):
								return self.current_encoder.get(UpperCamelCase       ,    self.current_encoder[self.unk_token]   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.language_code_re.match(UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			[match.group(0   )] if match else []
								return code, self.language_code_re.sub(""       ,    UpperCamelCase   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			self.remove_language_code(UpperCamelCase   )
								_SCREAMING_SNAKE_CASE					=			self.current_spm.encode(UpperCamelCase       ,    out_type=UpperCamelCase   )
								return code + pieces




		def      lowercase							(					self       ,    UpperCamelCase   ):
								return self.decoder.get(UpperCamelCase       ,    self.unk_token   )




		def      lowercase							(					self       ,    UpperCamelCase       ,    **UpperCamelCase   ):
								return super().batch_decode(UpperCamelCase       ,    **UpperCamelCase   )




		def      lowercase							(					self       ,    UpperCamelCase       ,    **UpperCamelCase   ):
								return super().decode(UpperCamelCase       ,    **UpperCamelCase   )




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			self.spm_source if self._decode_use_source_tokenizer else self.spm_target
								_SCREAMING_SNAKE_CASE					=			[]
								_SCREAMING_SNAKE_CASE					=			""
								for token in tokens:
														# make sure that special tokens are not decoded using sentencepiece model
														if token in self.all_special_tokens:
																				out_string += sp_model.decode_pieces(UpperCamelCase   ) + token + " "
																				_SCREAMING_SNAKE_CASE					=			[]
														else:
																				current_sub_tokens.append(UpperCamelCase   )
								out_string += sp_model.decode_pieces(UpperCamelCase   )
								return out_string.strip()




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase=None   ):
								if token_ids_a is None:
														return token_ids_a + [self.eos_token_id]
								# We don't expect to process pairs, but leave the pair logic for API consistency
								return token_ids_a + token_ids_a + [self.eos_token_id]




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.spm_source
								_SCREAMING_SNAKE_CASE					=			self.encoder




		def      lowercase							(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.spm_target
								if self.separate_vocabs:
														_SCREAMING_SNAKE_CASE					=			self.target_encoder




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




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase = None   ):
								if not os.path.isdir(UpperCamelCase   ):
														logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
														return
								_SCREAMING_SNAKE_CASE					=			[]

								if self.separate_vocabs:
														_SCREAMING_SNAKE_CASE					=			os.path.join(
														    UpperCamelCase       ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]       ,    )
														_SCREAMING_SNAKE_CASE					=			os.path.join(
														    UpperCamelCase       ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]       ,    )
														save_json(self.encoder       ,    UpperCamelCase   )
														save_json(self.target_encoder       ,    UpperCamelCase   )
														saved_files.append(UpperCamelCase   )
														saved_files.append(UpperCamelCase   )
								else:
														_SCREAMING_SNAKE_CASE					=			os.path.join(
														    UpperCamelCase       ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]   )
														save_json(self.encoder       ,    UpperCamelCase   )
														saved_files.append(UpperCamelCase   )

								for spm_save_filename, spm_orig_path, spm_model in zip(
								    [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]       ,    self.spm_files       ,    [self.spm_source, self.spm_target]       ,    ):
														_SCREAMING_SNAKE_CASE					=			os.path.join(
														    UpperCamelCase       ,    (filename_prefix + "-" if filename_prefix else "") + spm_save_filename   )
														if os.path.abspath(UpperCamelCase   ) != os.path.abspath(UpperCamelCase   ) and os.path.isfile(UpperCamelCase   ):
																				copyfile(UpperCamelCase       ,    UpperCamelCase   )
																				saved_files.append(UpperCamelCase   )
														elif not os.path.isfile(UpperCamelCase   ):
																				with open(UpperCamelCase       ,    "wb"   ) as fi:
																										_SCREAMING_SNAKE_CASE					=			spm_model.serialized_model_proto()
																										fi.write(UpperCamelCase   )
																				saved_files.append(UpperCamelCase   )

								return tuple(UpperCamelCase   )




		def      lowercase							(					self   ):
								return self.get_src_vocab()




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




		def      lowercase							(					self   ):
								return dict(self.target_encoder       ,    **self.added_tokens_decoder   )




		def __getstate__(					self   ):
								_SCREAMING_SNAKE_CASE					=			self.__dict__.copy()
								state.update(
								    {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}   )
								return state




		def __setstate__(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			d

								# for backward compatibility
								if not hasattr(self       ,    "sp_model_kwargs"   ):
														_SCREAMING_SNAKE_CASE					=			{}

								_SCREAMING_SNAKE_CASE,				_SCREAMING_SNAKE_CASE					=			(load_spm(UpperCamelCase       ,    self.sp_model_kwargs   ) for f in self.spm_files)
								_SCREAMING_SNAKE_CASE					=			self.spm_source
								self._setup_normalizer()




		def      lowercase							(					self       ,    *UpperCamelCase       ,    **UpperCamelCase   ):
								return 1




		def      lowercase							(					self       ,    UpperCamelCase   ):
								_SCREAMING_SNAKE_CASE					=			set(self.all_special_ids   )  # call it once instead of inside list comp
								all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special
								return [1 if x in all_special_ids else 0 for x in seq]




		def      lowercase							(					self       ,    UpperCamelCase       ,    UpperCamelCase = None       ,    UpperCamelCase = False   ):
								if already_has_special_tokens:
														return self._special_token_mask(UpperCamelCase   )
								elif token_ids_a is None:
														return self._special_token_mask(UpperCamelCase   ) + [1]
								else:
														return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]



def       _a		(						_SCREAMING_SNAKE_CASE		: str						,      _SCREAMING_SNAKE_CASE		: Dict[str, Any]	):
						_SCREAMING_SNAKE_CASE					=			sentencepiece.SentencePieceProcessor(**_SCREAMING_SNAKE_CASE	)
						spm.Load(_SCREAMING_SNAKE_CASE	)
						return spm



def       _a		(						_SCREAMING_SNAKE_CASE		: List[Any]						,      _SCREAMING_SNAKE_CASE		: str	):
						with open(_SCREAMING_SNAKE_CASE						,      "w"	) as f:
												json.dump(_SCREAMING_SNAKE_CASE						,      _SCREAMING_SNAKE_CASE						,      indent=2	)




def       _a		(						_SCREAMING_SNAKE_CASE		: str	):
						with open(_SCREAMING_SNAKE_CASE						,      "r"	) as f:
												return json.load(_SCREAMING_SNAKE_CASE	)

'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       lowerCAmelCase			(					__UpperCAmelCase      ):




		def __init__(					self       ,    UpperCamelCase       ,    UpperCamelCase   ):
								super().__init__()
								self.register_modules(unet=UpperCamelCase       ,    scheduler=UpperCamelCase   )




		@torch.no_grad()
		def __call__(					self       ,    UpperCamelCase = 1       ,    UpperCamelCase = None       ,    UpperCamelCase = 1_000       ,    UpperCamelCase = "pil"       ,    UpperCamelCase = True       ,    ):
								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size       ,    UpperCamelCase   ):
														_SCREAMING_SNAKE_CASE					=			(
														    batch_size,
														    self.unet.config.in_channels,
														    self.unet.config.sample_size,
														    self.unet.config.sample_size,
														)
								else:
														_SCREAMING_SNAKE_CASE					=			(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if self.device.type == "mps":
														# randn does not work reproducibly on mps
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase   )
														_SCREAMING_SNAKE_CASE					=			image.to(self.device   )
								else:
														_SCREAMING_SNAKE_CASE					=			randn_tensor(UpperCamelCase       ,    generator=UpperCamelCase       ,    device=self.device   )

								# set step values
								self.scheduler.set_timesteps(UpperCamelCase   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
														# 1. predict noise model_output
														_SCREAMING_SNAKE_CASE					=			self.unet(UpperCamelCase       ,    UpperCamelCase   ).sample

														# 2. compute previous image: x_t -> x_t-1
														_SCREAMING_SNAKE_CASE					=			self.scheduler.step(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    generator=UpperCamelCase   ).prev_sample

								_SCREAMING_SNAKE_CASE					=			(image / 2 + 0.5).clamp(0       ,    1   )
								_SCREAMING_SNAKE_CASE					=			image.cpu().permute(0       ,    2       ,    3       ,    1   ).numpy()
								if output_type == "pil":
														_SCREAMING_SNAKE_CASE					=			self.numpy_to_pil(UpperCamelCase   )

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=UpperCamelCase   )