

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


SCREAMING_SNAKE_CASE						:int      =			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE						:Union[str, Any]      =			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
SCREAMING_SNAKE_CASE						:Tuple      =			{
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
}

SCREAMING_SNAKE_CASE						:Tuple      =			{
    """RUCAIBox/mvp""": 1024,
}




@lru_cache()
def        lowerCAmelCase(	)->		Any:



    """simple docstring"""






    UpperCamelCase_          =		(
        list(range(ord("!"       )       ,		ord("~"       ) + 1       )       ) + list(range(ord("¡"       )       ,		ord("¬"       ) + 1       )       ) + list(range(ord("®"       )       ,		ord("ÿ"       ) + 1       )       )
    )
    UpperCamelCase_          =		bs[:]
    UpperCamelCase_          =		0
    for b in range(2**8       ):
        if b not in bs:
            bs.append(_lowercase       )
            cs.append(2**8 + n       )
            n += 1
    UpperCamelCase_          =		[chr(_lowercase       ) for n in cs]
    return dict(zip(_lowercase       ,		_lowercase       )       )




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






    UpperCamelCase_          =		set()
    UpperCamelCase_          =		word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)       )
        UpperCamelCase_          =		char
    return pairs






class 		__magic_name__ (				snake_case							):
     UpperCamelCase_     :Union[str, Any]									=	VOCAB_FILES_NAMES
     UpperCamelCase_     :Dict									=	PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase_     :str									=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase_     :List[Any]									=	["""input_ids""", """attention_mask"""]






     def __init__(							self						,		_lowercase						,		_lowercase						,		_lowercase="replace"						,		_lowercase="<s>"						,		_lowercase="</s>"						,		_lowercase="</s>"						,		_lowercase="<s>"						,		_lowercase="<unk>"						,		_lowercase="<pad>"						,		_lowercase="<mask>"						,		_lowercase=False						,		**_lowercase						,		)->			Optional[Any]:
         UpperCamelCase_          =		AddedToken(__A						,		lstrip=__A						,		rstrip=__A     ) if isinstance(__A						,		__A     ) else bos_token
         UpperCamelCase_          =		AddedToken(__A						,		lstrip=__A						,		rstrip=__A     ) if isinstance(__A						,		__A     ) else eos_token
         UpperCamelCase_          =		AddedToken(__A						,		lstrip=__A						,		rstrip=__A     ) if isinstance(__A						,		__A     ) else sep_token
         UpperCamelCase_          =		AddedToken(__A						,		lstrip=__A						,		rstrip=__A     ) if isinstance(__A						,		__A     ) else cls_token
         UpperCamelCase_          =		AddedToken(__A						,		lstrip=__A						,		rstrip=__A     ) if isinstance(__A						,		__A     ) else unk_token
         UpperCamelCase_          =		AddedToken(__A						,		lstrip=__A						,		rstrip=__A     ) if isinstance(__A						,		__A     ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase_          =		AddedToken(__A						,		lstrip=__A						,		rstrip=__A     ) if isinstance(__A						,		__A     ) else mask_token

         super().__init__(
             errors=__A						,		bos_token=__A						,		eos_token=__A						,		unk_token=__A						,		sep_token=__A						,		cls_token=__A						,		pad_token=__A						,		mask_token=__A						,		add_prefix_space=__A						,		**__A						,		)
         with open(__A						,		encoding="utf-8"     ) as vocab_handle:
             UpperCamelCase_          =		json.load(__A     )
         UpperCamelCase_          =		{v: k for k, v in self.encoder.items()}
         UpperCamelCase_          =		errors  # how to handle errors in decoding
         UpperCamelCase_          =		bytes_to_unicode()
         UpperCamelCase_          =		{v: k for k, v in self.byte_encoder.items()}
         with open(__A						,		encoding="utf-8"     ) as merges_handle:
             UpperCamelCase_          =		merges_handle.read().split("\n"     )[1:-1]
         UpperCamelCase_          =		[tuple(merge.split()     ) for merge in bpe_merges]
         UpperCamelCase_          =		dict(zip(__A						,		range(len(__A     )     )     )     )
         UpperCamelCase_          =		{}
         UpperCamelCase_          =		add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         UpperCamelCase_          =		re.compile(r"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"     )






     @property
     def      UpperCAmelCase_							(							self     )->			Dict:
         return len(self.encoder     )






     def      UpperCAmelCase_							(							self     )->			int:
         return dict(self.encoder						,		**self.added_tokens_encoder     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			Dict:
         if token in self.cache:
             return self.cache[token]
         UpperCamelCase_          =		tuple(__A     )
         UpperCamelCase_          =		get_pairs(__A     )

         if not pairs:
             return token

         while True:
             UpperCamelCase_          =		min(__A						,		key=lambda _lowercase     : self.bpe_ranks.get(__A						,		float("inf"     )     )     )
             if bigram not in self.bpe_ranks:
                 break
             UpperCamelCase_   ,    UpperCamelCase_          =		bigram
             UpperCamelCase_          =		[]
             UpperCamelCase_          =		0
             while i < len(__A     ):
                 try:
                     UpperCamelCase_          =		word.index(__A						,		__A     )
                 except ValueError:
                     new_word.extend(word[i:]     )
                     break
                 else:
                     new_word.extend(word[i:j]     )
                     UpperCamelCase_          =		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_          =		tuple(__A     )
             UpperCamelCase_          =		new_word
             if len(__A     ) == 1:
                 break
             else:
                 UpperCamelCase_          =		get_pairs(__A     )
         UpperCamelCase_          =		" ".join(__A     )
         UpperCamelCase_          =		word
         return word






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			Tuple:
         UpperCamelCase_          =		[]
         for token in re.findall(self.pat						,		__A     ):
             UpperCamelCase_          =		"".join(
                 self.byte_encoder[b] for b in token.encode("utf-8"     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
             bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__A     ).split(" "     )     )
         return bpe_tokens






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			int:
         return self.encoder.get(__A						,		self.encoder.get(self.unk_token     )     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			List[str]:
         return self.decoder.get(__A     )






     def      UpperCAmelCase_							(							self						,		_lowercase     )->			List[Any]:
         UpperCamelCase_          =		"".join(__A     )
         UpperCamelCase_          =		bytearray([self.byte_decoder[c] for c in text]     ).decode("utf-8"						,		errors=self.errors     )
         return text






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = None     )->			Tuple[str]:
         if not os.path.isdir(__A     ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"     )
             return
         UpperCamelCase_          =		os.path.join(
             __A						,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
         UpperCamelCase_          =		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_          =		0
         with open(__A						,		"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 _lowercase     : 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!"     )
                     UpperCamelCase_          =		token_index
                 writer.write(" ".join(__A     ) + "\n"     )
                 index += 1

         return vocab_file, merge_file






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = None     )->			List[int]:
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         UpperCamelCase_          =		[self.cls_token_id]
         UpperCamelCase_          =		[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = None						,		_lowercase = False     )->			List[int]:
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__A						,		token_ids_a=__A						,		already_has_special_tokens=__A     )

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






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase = None     )->			List[int]:
         UpperCamelCase_          =		[self.sep_token_id]
         UpperCamelCase_          =		[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]






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase=False						,		**_lowercase     )->			Optional[int]:
         UpperCamelCase_          =		kwargs.pop("add_prefix_space"						,		self.add_prefix_space     )
         if (is_split_into_words or add_prefix_space) and (len(__A     ) > 0 and not text[0].isspace()):
             UpperCamelCase_          =		" " + text
         return (text, kwargs)








import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							A_       =    "hf-internal-testing/tiny-stable-diffusion-pipe"
							A_       =    1
							A_       =    77
							A_       =    32
							A_       =    4
							A_       =    A_       =    64
							A_       =    torch.manual_seed(0    )

							def 					__A       (     self:      List[Any]						,   __A:      List[Any]=0.9_999      )			-> Dict:
												_A									=       UNetaDConditionModel.from_pretrained(self.model_id						,   subfolder='''unet'''      )
												_A									=       unet.to(__A      )
												_A									=       EMAModel(unet.parameters()						,   decay=__A						,   model_cls=__A						,   model_config=unet.config      )
												return unet, ema_unet

							def 					__A       (     self:      Tuple      )			-> Union[str, Any]:
												_A									=       torch.randn(
												    self.batch_size						,   self.num_in_channels						,   self.latent_height						,   self.latent_width						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randint(0						,   10_00						,   size=(self.batch_size,)						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randn(
												    self.batch_size						,   self.prompt_length						,   self.text_encoder_hidden_dim						,   generator=self.generator      ).to(__A      )
												return noisy_latents, timesteps, encoder_hidden_states

							def 					__A       (     self:      Tuple						,   __A:      Optional[int]      )			-> Optional[Any]:
												_A									=       {}
												for k, param in unet.state_dict().items():
																	_A									=       torch.randn_like(__A      ) + (param * torch.randn_like(__A      ))
																	updated_state_dict.update({k: updated_param}      )
												unet.load_state_dict(__A      )
												return unet

							def 					__A       (     self:      Any      )			-> Any:
												_A							,_A									=       self.get_models()
												# Take the first (hypothetical) EMA step.
												ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 1

												# Take two more.
												for _ in range(2      ):
																	ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 3

							def 					__A       (     self:      Union[str, Any]      )			-> str:
												_A							,_A									=       self.get_models()
												# Since the `unet` is not being updated (i.e., backprop'd)
												# there won't be any difference between the `params` of `unet`
												# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
												ema_unet.step(unet.parameters()      )
												_A									=       list(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

												# The above holds true even if we call `ema.step()` multiple times since
												# `unet` params are still not being updated.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Tuple      )			-> List[str]:
												_A							,_A									=       self.get_models()
												# Here we simulate the parameter updates for `unet`. Since there might
												# be some parameters which are initialized to zero we take extra care to
												# initialize their values to something non-zero before the multiplication.
												_A									=       self.simulate_backprop(__A      )

												# Take the EMA step.
												ema_unet.step(unet_pseudo_updated_step_one.parameters()      )

												# Now the EMA'd parameters won't be equal to the original model parameters.
												_A									=       list(unet_pseudo_updated_step_one.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

												# Ensure this is the case when we take multiple EMA steps.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Optional[int]      )			-> Optional[int]:
												# If we call EMA step after a backpropagation consecutively for two times,
												# the shadow params from those two steps should be different.
												_A							,_A									=       self.get_models()

												# First backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												# Second backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      int      )			-> Any:
												# If there's no decay even if there are backprops, EMA steps
												# won't take any effect i.e., the shadow params would remain the
												# same.
												_A							,_A									=       self.get_models(decay=0.0      )
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert torch.allclose(__A						,   __A      )



							@skip_mps
							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A							,_A									=       self.get_models()
												_A							,_A							,_A									=       self.get_dummy_inputs()

												with tempfile.TemporaryDirectory() as tmpdir:
																	ema_unet.save_pretrained(__A      )
																	_A									=       UNetaDConditionModel.from_pretrained(__A						,   model_cls=__A      )
																	_A									=       loaded_unet.to(unet.device      )

												# Since no EMA step has been performed the outputs should match.
												_A									=       unet(__A						,   __A						,   __A      ).sample
												_A									=       loaded_unet(__A						,   __A						,   __A      ).sample

												assert torch.allclose(__A						,   __A						,   atol=1e-4      )



