



import os
import unicodedata
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 SPIECE_UNDERLINE, logging


lowerCamelCase__			= logging.get_logger(__name__)

lowerCamelCase__			= {"""vocab_file""": """spiece.model"""}

lowerCamelCase__			= {
    """vocab_file""": {
        """TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
    }
}






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):





							def __init__(   self		:		Tuple      ,				__lowercase		:		Dict      ,				__lowercase		:		Optional[int]=False      ,				__lowercase		:		int=True      ,				__lowercase		:		Dict=False      ,				__lowercase		:		Any="<s>"      ,				__lowercase		:		List[Any]="</s>"      ,				__lowercase		:		Tuple="<unk>"      ,				__lowercase		:		Tuple="<sep>"      ,				__lowercase		:		Dict="<pad>"      ,				__lowercase		:		Tuple="<cls>"      ,				__lowercase		:		Any="<mask>"      ,				__lowercase		:		str=["<eop>", "<eod>"]      ,				__lowercase		:		Optional[Dict[str, Any]] = None      ,				**__lowercase		:		List[str]      ,				):

											'''simple docstring'''
											__a       =		AddedToken(__lowercase      ,				lstrip=__lowercase      ,				rstrip=__lowercase	) if isinstance(__lowercase      ,				__lowercase	) else mask_token

											__a       =		{} if sp_model_kwargs is None else sp_model_kwargs

											super().__init__(
											    do_lower_case=__lowercase      ,				remove_space=__lowercase      ,				keep_accents=__lowercase      ,				bos_token=__lowercase      ,				eos_token=__lowercase      ,				unk_token=__lowercase      ,				sep_token=__lowercase      ,				pad_token=__lowercase      ,				cls_token=__lowercase      ,				mask_token=__lowercase      ,				additional_special_tokens=__lowercase      ,				sp_model_kwargs=self.sp_model_kwargs      ,				**__lowercase      ,				)

											__a       =		3

											__a       =		do_lower_case
											__a       =		remove_space
											__a       =		keep_accents
											__a       =		vocab_file

											__a       =		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(__lowercase	)

											try:
															import jieba
											except ModuleNotFoundError as error:
															raise error.__class__(
															    """You need to install jieba to use CpmTokenizer or CpmTokenizerFast. """
															    """See https://pypi.org/project/jieba/ for installation."""	)
											__a       =		jieba
											__a       =		str.maketrans(""" \n"""      ,				"""\u2582\u2583"""	)





							@property
							# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
							def 						UpperCamelCase_					(   self		:		int	):

											'''simple docstring'''
											return len(self.sp_model	)





							def 						UpperCamelCase_					(   self		:		List[str]	):

											'''simple docstring'''
											__a       =		{self.convert_ids_to_tokens(__lowercase	): i for i in range(self.vocab_size	)}
											vocab.update(self.added_tokens_encoder	)
											return vocab





							def __getstate__(   self		:		Optional[Any]	):

											'''simple docstring'''
											__a       =		self.__dict__.copy()
											__a       =		None
											return state





							def __setstate__(   self		:		Tuple      ,				__lowercase		:		Any	):

											'''simple docstring'''
											__a       =		d

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

											__a       =		spm.SentencePieceProcessor(**self.sp_model_kwargs	)
											self.sp_model.Load(self.vocab_file	)





							def 						UpperCamelCase_					(   self		:		Optional[Any]      ,				__lowercase		:		str	):

											'''simple docstring'''
											if self.remove_space:
															__a       =		""" """.join(inputs.strip().split()	)
											else:
															__a       =		inputs
											__a       =		outputs.replace("""``"""      ,				"""\""""	).replace("""''"""      ,				"""\""""	)

											if not self.keep_accents:
															__a       =		unicodedata.normalize("""NFKD"""      ,				__lowercase	)
															__a       =		"""""".join([c for c in outputs if not unicodedata.combining(__lowercase	)]	)
											if self.do_lower_case:
															__a       =		outputs.lower()

											return outputs





							def 						UpperCamelCase_					(   self		:		str      ,				__lowercase		:		str	):

											'''simple docstring'''
											__a       =		self.preprocess_text(__lowercase	)
											__a       =		self.sp_model.encode(__lowercase      ,				out_type=__lowercase	)
											__a       =		[]
											for piece in pieces:
															if len(__lowercase	) > 1 and piece[-1] == str(""","""	) and piece[-2].isdigit():
																			__a       =		self.sp_model.EncodeAsPieces(piece[:-1].replace(__lowercase      ,				""""""	)	)
																			if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
																							if len(cur_pieces[0]	) == 1:
																											__a       =		cur_pieces[1:]
																							else:
																											__a       =		cur_pieces[0][1:]
																			cur_pieces.append(piece[-1]	)
																			new_pieces.extend(__lowercase	)
															else:
																			new_pieces.append(__lowercase	)

											return new_pieces





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		Optional[Any]	):

											'''simple docstring'''
											return self.sp_model.PieceToId(__lowercase	)





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[int]	):

											'''simple docstring'''
											return self.sp_model.IdToPiece(__lowercase	)





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		Dict	):

											'''simple docstring'''
											__a       =		"""""".join(__lowercase	).replace(__lowercase      ,				""" """	).strip()
											return out_string





							def 						UpperCamelCase_					(   self		:		List[str]      ,				__lowercase		:		List[int]      ,				__lowercase		:		Optional[List[int]] = None	):

											'''simple docstring'''
											__a       =		[self.sep_token_id]
											__a       =		[self.cls_token_id]
											if token_ids_a is None:
															return token_ids_a + sep + cls
											return token_ids_a + sep + token_ids_a + sep + cls





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		List[int]      ,				__lowercase		:		Optional[List[int]] = None      ,				__lowercase		:		bool = False	):

											'''simple docstring'''

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

											if token_ids_a is not None:
															return ([0] * len(__lowercase	)) + [1] + ([0] * len(__lowercase	)) + [1, 1]
											return ([0] * len(__lowercase	)) + [1, 1]





							def 						UpperCamelCase_					(   self		:		Tuple      ,				__lowercase		:		List[int]      ,				__lowercase		:		Optional[List[int]] = None	):

											'''simple docstring'''
											__a       =		[self.sep_token_id]
											__a       =		[2]

											if token_ids_a is None:
															return len(token_ids_a + sep	) * [0] + cls_segment_id
											return len(token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1] + cls_segment_id





							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		str      ,				__lowercase		:		Optional[str] = None	):

											'''simple docstring'''
											if not os.path.isdir(__lowercase	):
															logger.error(F"Vocabulary path ({save_directory}) should be a directory"	)
															return
											__a       =		os.path.join(
											    __lowercase      ,				(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

											if os.path.abspath(self.vocab_file	) != os.path.abspath(__lowercase	) and os.path.isfile(self.vocab_file	):
															copyfile(self.vocab_file      ,				__lowercase	)
											elif not os.path.isfile(self.vocab_file	):
															with open(__lowercase      ,				"""wb"""	) as fi:
																			__a       =		self.sp_model.serialized_model_proto()
																			fi.write(__lowercase	)

											return (out_vocab_file,)







							def 						UpperCamelCase_					(   self		:		str      ,				*__lowercase		:		Tuple      ,				**__lowercase		:		Union[str, Any]	):

											'''simple docstring'''
											__a       =		super()._decode(*__lowercase      ,				**__lowercase	)
											__a       =		text.replace(""" """      ,				""""""	).replace("""\u2582"""      ,				""" """	).replace("""\u2583"""      ,				"""\n"""	)
											return text





from dataclasses import dataclass
from typing import Optional

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin






@dataclass
class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__					):
							__lowerCamelCase	:	torch.FloatTensor






class    SCREAMING_SNAKE_CASE			(							lowerCamelCase__			,      lowerCamelCase__					):





							@register_to_config
							def __init__(   self		:		Tuple      ,				__lowercase		:		int = 16      ,				__lowercase		:		int = 88      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		int = 1      ,				__lowercase		:		float = 0.0      ,				__lowercase		:		int = 32      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		bool = False      ,				__lowercase		:		Optional[int] = None      ,				__lowercase		:		str = "geglu"      ,				__lowercase		:		bool = True      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											super().__init__()
											__a       =		num_attention_heads
											__a       =		attention_head_dim
											__a       =		num_attention_heads * attention_head_dim

											__a       =		in_channels

											__a       =		torch.nn.GroupNorm(num_groups=__lowercase      ,				num_channels=__lowercase      ,				eps=1E-6      ,				affine=__lowercase	)
											__a       =		nn.Linear(__lowercase      ,				__lowercase	)

											# 3. Define transformers blocks
											__a       =		nn.ModuleList(
											    [
											        BasicTransformerBlock(
											            __lowercase      ,				__lowercase      ,				__lowercase      ,				dropout=__lowercase      ,				cross_attention_dim=__lowercase      ,				activation_fn=__lowercase      ,				attention_bias=__lowercase      ,				double_self_attention=__lowercase      ,				norm_elementwise_affine=__lowercase      ,				)
											        for d in range(__lowercase	)
											    ]	)

											__a       =		nn.Linear(__lowercase      ,				__lowercase	)







							def 						UpperCamelCase_					(   self		:		Any      ,				__lowercase		:		Optional[Any]      ,				__lowercase		:		str=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		List[Any]=None      ,				__lowercase		:		int=1      ,				__lowercase		:		Union[str, Any]=None      ,				__lowercase		:		bool = True      ,				):

											'''simple docstring'''
											__a     ,					__a     ,					__a     ,					__a       =		hidden_states.shape
											__a       =		batch_frames // num_frames

											__a       =		hidden_states

											__a       =		hidden_states[None, :].reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											__a       =		hidden_states.permute(0      ,				2      ,				1      ,				3      ,				4	)

											__a       =		self.norm(__lowercase	)
											__a       =		hidden_states.permute(0      ,				3      ,				4      ,				2      ,				1	).reshape(batch_size * height * width      ,				__lowercase      ,				__lowercase	)

											__a       =		self.proj_in(__lowercase	)

											# 2. Blocks
											for block in self.transformer_blocks:
															__a       =		block(
															    __lowercase      ,				encoder_hidden_states=__lowercase      ,				timestep=__lowercase      ,				cross_attention_kwargs=__lowercase      ,				class_labels=__lowercase      ,				)

											# 3. Output
											__a       =		self.proj_out(__lowercase	)
											__a       =		(
											    hidden_states[None, None, :]
											    .reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)
											    .permute(0      ,				3      ,				4      ,				1      ,				2	)
											    .contiguous()
											)
											__a       =		hidden_states.reshape(__lowercase      ,				__lowercase      ,				__lowercase      ,				__lowercase	)

											__a       =		hidden_states + residual

											if not return_dict:
															return (output,)

											return TransformerTemporalModelOutput(sample=__lowercase	)
