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

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


__lowerCamelCase :     Any          =  logging.get_logger(__name__)

__lowerCamelCase :     Dict          =  {
    '''src_vocab_file''': '''vocab-src.json''',
    '''tgt_vocab_file''': '''vocab-tgt.json''',
    '''merges_file''': '''merges.txt''',
}

__lowerCamelCase :     Optional[int]          =  {
    '''src_vocab_file''': {
        '''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'''
    },
    '''tgt_vocab_file''': {
        '''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'''
    },
    '''merges_file''': {'''stas/tiny-wmt19-en-de''': '''https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'''},
}

__lowerCamelCase :     Dict          =  {'''stas/tiny-wmt19-en-de''': 10_24}
__lowerCamelCase :     str          =  {
    '''stas/tiny-wmt19-en-de''': {
        '''langs''': ['''en''', '''de'''],
        '''model_max_length''': 10_24,
        '''special_tokens_map_file''': None,
        '''full_tokenizer_file''': None,
    }
}




def 				_snake_case     (       lowerCAmelCase							:       Dict     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Any    				=   set()
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)     )
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   char
		return pairs




def 				_snake_case     (       lowerCAmelCase							:       str     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("，"       ,			","     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   re.sub(R"。\s*"       ,			". "       ,			lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   text.replace("、"       ,			","     )
		SCREAMING_SNAKE_CASE_  :		str    				=   text.replace("”"       ,			"\""     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   text.replace("“"       ,			"\""     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   text.replace("∶"       ,			":"     )
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   text.replace("："       ,			":"     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("？"       ,			"?"     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   text.replace("《"       ,			"\""     )
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   text.replace("》"       ,			"\""     )
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   text.replace("）"       ,			")"     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   text.replace("！"       ,			"!"     )
		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   text.replace("（"       ,			"("     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   text.replace("；"       ,			";"     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   text.replace("１"       ,			"1"     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   text.replace("」"       ,			"\""     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("「"       ,			"\""     )
		SCREAMING_SNAKE_CASE_  :		str    				=   text.replace("０"       ,			"0"     )
		SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   text.replace("３"       ,			"3"     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   text.replace("２"       ,			"2"     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   text.replace("５"       ,			"5"     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("６"       ,			"6"     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("９"       ,			"9"     )
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   text.replace("７"       ,			"7"     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("８"       ,			"8"     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   text.replace("４"       ,			"4"     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   re.sub(R"．\s*"       ,			". "       ,			lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   text.replace("～"       ,			"~"     )
		SCREAMING_SNAKE_CASE_  :		int    				=   text.replace("’"       ,			"'"     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   text.replace("…"       ,			"..."     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   text.replace("━"       ,			"-"     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("〈"       ,			"<"     )
		SCREAMING_SNAKE_CASE_  :		Dict    				=   text.replace("〉"       ,			">"     )
		SCREAMING_SNAKE_CASE_  :		Any    				=   text.replace("【"       ,			"["     )
		SCREAMING_SNAKE_CASE_  :		List[str]    				=   text.replace("】"       ,			"]"     )
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   text.replace("％"       ,			"%"     )
		return text




def 				_snake_case     (       lowerCAmelCase							:       Optional[int]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int    				=   []
		for char in text:
				SCREAMING_SNAKE_CASE_  :		str    				=   unicodedata.category(lowerCAmelCase     )
				if cat.startswith("C"     ):
						continue
				output.append(lowerCAmelCase     )
		return "".join(lowerCAmelCase     )







class 		a__						( A__ ):
					A     	=							VOCAB_FILES_NAMES
					A     	=							PRETRAINED_VOCAB_FILES_MAP
					A     	=							PRETRAINED_INIT_CONFIGURATION
					A     	=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					A     	=							['input_ids', 'attention_mask']

					def __init__(   self				:	List[str],_A				:	Optional[int]=None,_A				:	List[str]=None,_A				:	Any=None,_A				:	Union[str, Any]=None,_A				:	Optional[int]=False,_A				:	Tuple="<unk>",_A				:	List[Any]="<s>",_A				:	Any="</s>",_A				:	Union[str, Any]="<pad>",**_A				:	int,):







							"""simple docstring"""






							super().__init__(
							    langs=_A,src_vocab_file=_A,tgt_vocab_file=_A,merges_file=_A,do_lower_case=_A,unk_token=_A,bos_token=_A,sep_token=_A,pad_token=_A,**_A,)

							try:
									import sacremoses
							except ImportError:
									raise ImportError(
									    "You need to install sacremoses to use XLMTokenizer. "
									    "See https://pypi.org/project/sacremoses/ for installation."   )

							SCREAMING_SNAKE_CASE_  :		int    				=   sacremoses

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   src_vocab_file
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   tgt_vocab_file
							SCREAMING_SNAKE_CASE_  :		Any    				=   merges_file
							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   do_lower_case

							# cache of sm.MosesPunctNormalizer instance
							SCREAMING_SNAKE_CASE_  :		str    				=   {}
							# cache of sm.MosesTokenizer instance
							SCREAMING_SNAKE_CASE_  :		List[Any]    				=   {}
							SCREAMING_SNAKE_CASE_  :		str    				=   {}

							if langs and len(_A   ) == 2:
									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   langs
							else:
									raise ValueError(
									    F'arg `langs` needs to be a list of 2 langs, e.g. [\'en\', \'ru\'], but got {langs}. '
									    "Usually that means that tokenizer can't find a mapping for the given model path "
									    "in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."   )

							with open(_A,encoding="utf-8"   ) as src_vocab_handle:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   json.load(_A   )
							with open(_A,encoding="utf-8"   ) as tgt_vocab_handle:
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   json.load(_A   )
									SCREAMING_SNAKE_CASE_  :		List[str]    				=   {v: k for k, v in tgt_vocab.items()}
							with open(_A,encoding="utf-8"   ) as merges_handle:
									SCREAMING_SNAKE_CASE_  :		Dict    				=   merges_handle.read().split("\n"   )[:-1]
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   [tuple(merge.split()[:2]   ) for merge in merges]
							SCREAMING_SNAKE_CASE_  :		Dict    				=   dict(zip(_A,range(len(_A   )   )   )   )
							SCREAMING_SNAKE_CASE_  :		str    				=   {}

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							return self.get_src_vocab()

					@property
					def     __UpperCamelCase       (   self				:	List[Any]   ):







							"""simple docstring"""






							return self.src_vocab_size

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	str,_A				:	List[Any]   ):







							"""simple docstring"""






							if lang not in self.cache_moses_punct_normalizer:
									SCREAMING_SNAKE_CASE_  :		Any    				=   self.sm.MosesPunctNormalizer(lang=_A   )
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   punct_normalizer
							return self.cache_moses_punct_normalizer[lang].normalize(_A   )

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	int,_A				:	Any   ):







							"""simple docstring"""






							if lang not in self.cache_moses_tokenizer:
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.sm.MosesTokenizer(lang=_A   )
									SCREAMING_SNAKE_CASE_  :		int    				=   moses_tokenizer
							return self.cache_moses_tokenizer[lang].tokenize(
							    _A,aggressive_dash_splits=_A,return_str=_A,escape=_A   )

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	List[str],_A				:	List[str]   ):







							"""simple docstring"""






							if lang not in self.cache_moses_detokenizer:
									SCREAMING_SNAKE_CASE_  :		Any    				=   self.sm.MosesDetokenizer(lang=_A   )
									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   moses_detokenizer
							return self.cache_moses_detokenizer[lang].detokenize(_A   )

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	List[str],_A				:	Tuple   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   replace_unicode_punct(_A   )
							SCREAMING_SNAKE_CASE_  :		str    				=   self.moses_punct_norm(_A,_A   )
							SCREAMING_SNAKE_CASE_  :		Any    				=   remove_non_printing_char(_A   )
							return text

					@property
					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							return len(self.encoder   )

					@property
					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							return len(self.decoder   )

					def     __UpperCamelCase       (   self				:	Dict   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							return dict(self.decoder,**self.added_tokens_decoder   )

					def     __UpperCamelCase       (   self				:	Optional[Any],_A				:	int   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   tuple(token[:-1]   ) + (token[-1] + "</w>",)
							if token in self.cache:
									return self.cache[token]
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   get_pairs(_A   )

							if not pairs:
									return token + "</w>"

							while True:
									SCREAMING_SNAKE_CASE_  :		str    				=   min(_A,key=lambda _A   : self.bpe_ranks.get(_A,float("inf"   )   )   )
									if bigram not in self.bpe_ranks:
											break
									SCREAMING_SNAKE_CASE_				,   SCREAMING_SNAKE_CASE_  :		List[str]    				=   bigram
									SCREAMING_SNAKE_CASE_  :		Any    				=   []
									SCREAMING_SNAKE_CASE_  :		Dict    				=   0
									while i < len(_A   ):
											try:
													SCREAMING_SNAKE_CASE_  :		Dict    				=   word.index(_A,_A   )
											except ValueError:
													new_word.extend(word[i:]   )
													break
											else:
													new_word.extend(word[i:j]   )
													SCREAMING_SNAKE_CASE_  :		Tuple    				=   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
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   tuple(_A   )
									SCREAMING_SNAKE_CASE_  :		int    				=   new_word
									if len(_A   ) == 1:
											break
									else:
											SCREAMING_SNAKE_CASE_  :		str    				=   get_pairs(_A   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   " ".join(_A   )
							if word == "\n  </w>":
									SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   "\n</w>"
							SCREAMING_SNAKE_CASE_  :		Any    				=   word
							return word

					def     __UpperCamelCase       (   self				:	List[Any],_A				:	List[Any],_A				:	str="en",_A				:	Optional[int]=False   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.src_lang

							if self.do_lower_case:
									SCREAMING_SNAKE_CASE_  :		str    				=   text.lower()

							if bypass_tokenizer:
									SCREAMING_SNAKE_CASE_  :		List[Any]    				=   text.split()
							else:
									SCREAMING_SNAKE_CASE_  :		Tuple    				=   self.moses_pipeline(_A,lang=_A   )
									SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   self.moses_tokenize(_A,lang=_A   )

							SCREAMING_SNAKE_CASE_  :		str    				=   []
							for token in text:
									if token:
											split_tokens.extend(list(self.bpe(_A   ).split(" "   )   )   )

							return split_tokens

					def     __UpperCamelCase       (   self				:	Dict,_A				:	Dict   ):







							"""simple docstring"""






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

					def     __UpperCamelCase       (   self				:	Union[str, Any],_A				:	Optional[int]   ):







							"""simple docstring"""






							return self.decoder.get(_A,self.unk_token   )

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	str   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   [t.replace(" ",""   ).replace("</w>"," "   ) for t in tokens]
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   "".join(_A   ).split()
							# detokenize
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   self.moses_detokenize(_A,self.tgt_lang   )
							return text

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	List[int],_A				:	Optional[List[int]] = None   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   [self.sep_token_id]

							# no bos used in fairseq
							if token_ids_a is None:
									return token_ids_a + sep
							return token_ids_a + sep + token_ids_a + sep

					def     __UpperCamelCase       (   self				:	Any,_A				:	List[int],_A				:	Optional[List[int]] = None,_A				:	bool = False   ):







							"""simple docstring"""






							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=_A,token_ids_a=_A,already_has_special_tokens=_A   )
							# no bos used in fairseq
							if token_ids_a is not None:
									return ([0] * len(_A   )) + [1] + ([0] * len(_A   )) + [1]
							return ([0] * len(_A   )) + [1]

					def     __UpperCamelCase       (   self				:	Dict,_A				:	List[int],_A				:	Optional[List[int]] = None   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   [self.sep_token_id]

							# no bos used in fairseq
							if token_ids_a is None:
									return len(token_ids_a + sep   ) * [0]
							return len(token_ids_a + sep   ) * [0] + len(token_ids_a + sep   ) * [1]

					def     __UpperCamelCase       (   self				:	Optional[int],_A				:	str,_A				:	Optional[str] = None   ):







							"""simple docstring"""






							if not os.path.isdir(_A   ):
									logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
									return

							SCREAMING_SNAKE_CASE_  :		Tuple    				=   os.path.join(
							    _A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["src_vocab_file"]   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   os.path.join(
							    _A,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["tgt_vocab_file"]   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   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"   )

							with open(_A,"w",encoding="utf-8"   ) as f:
									SCREAMING_SNAKE_CASE_  :		str    				=   {v: k for k, v in self.decoder.items()}
									f.write(json.dumps(_A,indent=2,sort_keys=_A,ensure_ascii=_A   ) + "\n"   )

							SCREAMING_SNAKE_CASE_  :		int    				=   0
							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!"   )
													SCREAMING_SNAKE_CASE_  :		str    				=   token_index
											writer.write(" ".join(_A   ) + "\n"   )
											index += 1

							return src_vocab_file, tgt_vocab_file, merges_file

					def __getstate__(   self				:	List[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Dict    				=   self.__dict__.copy()
							SCREAMING_SNAKE_CASE_  :		Dict    				=   None
							return state






					def __setstate__(   self				:	List[Any],_A				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Tuple    				=   d

							try:
									import sacremoses
							except ImportError:
									raise ImportError(
									    "You need to install sacremoses to use XLMTokenizer. "
									    "See https://pypi.org/project/sacremoses/ for installation."   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   sacremoses

import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__lowerCamelCase :     List[Any]          =  False






@nightly
@require_torch_gpu
class 		a__						( unittest.TestCase ):

					def     __UpperCamelCase       (   self				:	Optional[int]   ):







							"""simple docstring"""






							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()

					def     __UpperCamelCase       (   self				:	Optional[Any]   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		int    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							# remove text_unet
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )

							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		int    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							with tempfile.TemporaryDirectory() as tmpdirname:
									pipe.save_pretrained(_A   )
									SCREAMING_SNAKE_CASE_  :		Dict    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained(_A   )

							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		List[str]    				=   generator.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		str    				=   pipe(
							    prompt="first prompt",image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=2,output_type="numpy",).images

							assert np.abs(image - new_image   ).sum() < 1E-5, "Models don't have the same forward pass"






					def     __UpperCamelCase       (   self				:	Any   ):







							"""simple docstring"""






							SCREAMING_SNAKE_CASE_  :		Any    				=   VersatileDiffusionDualGuidedPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
							pipe.remove_unused_weights()
							pipe.to(_A   )
							pipe.set_progress_bar_config(disable=_A   )

							SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   "cyberpunk 2077"
							SCREAMING_SNAKE_CASE_  :		Dict    				=   load_image(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
							SCREAMING_SNAKE_CASE_  :		Tuple    				=   torch.manual_seed(0   )
							SCREAMING_SNAKE_CASE_  :		Dict    				=   pipe(
							    prompt=_A,image=_A,text_to_image_strength=0.75,generator=_A,guidance_scale=7.5,num_inference_steps=50,output_type="numpy",).images

							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   image[0, 253:256, 253:256, -1]

							assert image.shape == (1, 512, 512, 3)
							SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   np.array([0.0787, 0.0849, 0.0826, 0.0812, 0.0807, 0.0795, 0.0818, 0.0798, 0.0779]   )

							assert np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
