




import os
from shutil import copyfile
from typing import TYPE_CHECKING, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
from ...utils.versions import require_version


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

require_version("""tokenizers>=0.13.3""")

if is_sentencepiece_available():
							from .tokenization_llama import LlamaTokenizer
else:
							_lowercase		 =      None

_lowercase		 =      logging.get_logger(__name__)
_lowercase		 =      {'''vocab_file''': '''tokenizer.model''', '''tokenizer_file''': '''tokenizer.json'''}

_lowercase		 =      '''[INST]''', '''[/INST]'''
_lowercase		 =      '''<<SYS>>\n''', '''\n<</SYS>>\n\n'''

# fmt: off
_lowercase		 =      '''You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\
answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\
that your responses are socially unbiased and positive in nature.

If a question does not make any sense, or is not factually coherent, explain why instead of answering something not\
correct. If you don\'t know the answer to a question, please don\'t share false information.'''

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				_lowerCAmelCase		     =VOCAB_FILES_NAMES
				_lowerCAmelCase		     =LlamaTokenizer
				_lowerCAmelCase		     ="""left"""
				_lowerCAmelCase		     =["""input_ids""", """attention_mask"""]

				def __init__(				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Any=None ,SCREAMING_SNAKE_CASE_				:       Dict=None ,SCREAMING_SNAKE_CASE_				:       Any=False ,SCREAMING_SNAKE_CASE_				:       int="<unk>" ,SCREAMING_SNAKE_CASE_				:       Union[str, Any]="<s>" ,SCREAMING_SNAKE_CASE_				:       Optional[int]="</s>" ,SCREAMING_SNAKE_CASE_				:       str=True ,SCREAMING_SNAKE_CASE_				:       Optional[Any]=False ,**SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,):
										super().__init__(
										    vocab_file=SCREAMING_SNAKE_CASE_ ,tokenizer_file=SCREAMING_SNAKE_CASE_ ,clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE_ ,unk_token=SCREAMING_SNAKE_CASE_ ,bos_token=SCREAMING_SNAKE_CASE_ ,eos_token=SCREAMING_SNAKE_CASE_ ,**SCREAMING_SNAKE_CASE_ ,)
										snake_case__    :   List[str]							     =   add_bos_token
										snake_case__    :   Union[str, Any]							     =   add_eos_token
										self.update_post_processor()

										snake_case__    :   Optional[int]							     =   vocab_file
										snake_case__    :   Union[str, Any]							     =   False if not self.vocab_file else True

				def  _lowerCamelCase   (				self				:       List[Any] ):
										snake_case__    :   Any							     =   self.bos_token
										snake_case__    :   Dict							     =   self.bos_token_id

										snake_case__    :   Optional[int]							     =   self.eos_token
										snake_case__    :   Optional[Any]							     =   self.eos_token_id

										snake_case__    :   Tuple							     =   F'''{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}'''
										snake_case__    :   int							     =   F'''{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}'''

										snake_case__    :   str							     =   []
										if self.add_bos_token:
																special_tokens.append((bos, bos_token_id) )
										if self.add_eos_token:
																special_tokens.append((eos, eos_token_id) )
										snake_case__    :   List[Any]							     =   processors.TemplateProcessing(
										    single=SCREAMING_SNAKE_CASE_ ,pair=SCREAMING_SNAKE_CASE_ ,special_tokens=SCREAMING_SNAKE_CASE_ )

				@property
				def  _lowerCamelCase   (				self				:       Union[str, Any] ):
										return self._add_eos_token

				@property
				def  _lowerCamelCase   (				self				:       Optional[int] ):
										return self._add_bos_token

				@add_eos_token.setter
				def  _lowerCamelCase   (				self				:       Tuple ,SCREAMING_SNAKE_CASE_				:       Any ):
										snake_case__    :   Tuple							     =   value
										self.update_post_processor()

				@add_bos_token.setter
				def  _lowerCamelCase   (				self				:       Any ,SCREAMING_SNAKE_CASE_				:       str ):
										snake_case__    :   Any							     =   value
										self.update_post_processor()

				def  _lowerCamelCase   (				self				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Dict = None ):


										if not self.can_save_slow_tokenizer:
																raise ValueError(
																    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																    '''tokenizer.''' )

										if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
																logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
																return
										snake_case__    :   Union[str, Any]							     =   os.path.join(
										    SCREAMING_SNAKE_CASE_ ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

										if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
																copyfile(self.vocab_file ,SCREAMING_SNAKE_CASE_ )

										return (out_vocab_file,)





				def  _lowerCamelCase   (				self				:       Dict ,SCREAMING_SNAKE_CASE_				:       str ):
										snake_case__    :   Optional[Any]							     =   list(conversation.iter_texts() )
										if not all([is_user for is_user, msg in dialogue[::2]] ) or not all(
										    [not is_user for is_user, msg in dialogue[1::2]] ):
																raise ValueError(
																    '''The model only supports \'user\' and \'assistant\' roles, starting with user and alternating (u/a/u/a/u...)''' )

										snake_case__    :   int							     =   []
										if len(conversation.past_user_inputs ) > 0:
																if not conversation.past_user_inputs[0].startswith(SCREAMING_SNAKE_CASE_ ) or E_SYS not in conversation.past_user_inputs[0]:
																						snake_case__    :   List[Any]							     =   (
																						    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
																						)
										elif not dialogue[0][1].startswith(SCREAMING_SNAKE_CASE_ ) or E_SYS not in dialogue[0][1]:
																snake_case__    :   Optional[int]							     =   (dialogue[0][0], B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + dialogue[0][1])

										dialog_tokens += sum(
										    [
										        [self.bos_token_id]
										        + self.encode(
										            F'''{B_INST} {(prompt[1]).strip()} {E_INST} {(answer[1]).strip()} ''' ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
										        + [self.eos_token_id]
										        for prompt, answer in zip(dialogue[::2] ,dialogue[1::2] )
										    ] ,[] ,)
										if not (dialogue[-1][0]):
																raise ValueError(F'''Last message must be from user, got {dialogue[-1]['role']}''' )
										dialog_tokens += [self.bos_token_id] + self.encode(
										    F'''{B_INST} {(dialogue[-1][1]).strip()} {E_INST}''' ,add_special_tokens=SCREAMING_SNAKE_CASE_ )
										return dialog_tokens








"""simple docstring"""





import types
from typing import List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer
from transformers.models.clip.modeling_clip import CLIPTextModelOutput

from diffusers.models import PriorTransformer
from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
from diffusers.schedulers import UnCLIPScheduler
from diffusers.utils import logging, randn_tensor


_lowercase		 =      logging.get_logger(__name__)  # pylint: disable=invalid-name

def 						lowerCAmelCase							(	self ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase ,     lowerCamelCase						):
						'''simple docstring'''


						snake_case__    :   Optional[Any]							     =   image.to(device=lowerCamelCase						)
						snake_case__    :   List[Any]							     =   image  # take image as image_embeddings
						snake_case__    :   Tuple							     =   image_embeddings.unsqueeze(1						)

						# duplicate image embeddings for each generation per prompt, using mps friendly method
						snake_case__     ,		snake_case__     ,		snake_case__    :   int							     =   image_embeddings.shape
						snake_case__    :   List[Any]							     =   image_embeddings.repeat(1 ,     lowerCamelCase ,     1						)
						snake_case__    :   int							     =   image_embeddings.view(bs_embed * num_images_per_prompt ,     lowerCamelCase ,     -1						)

						if do_classifier_free_guidance:
												snake_case__    :   Tuple							     =   torch.zeros_like(lowerCamelCase						)

												# For classifier free guidance, we need to do two forward passes.
												# Here we concatenate the unconditional and text embeddings into a single batch
												# to avoid doing two forward passes
												snake_case__    :   Any							     =   torch.cat([uncond_embeddings, image_embeddings]						)

						return image_embeddings

class  __lowercase    (     snake_case_		):


				'''simple docstring'''


				def __init__(				self				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       PriorTransformer ,SCREAMING_SNAKE_CASE_				:       CLIPTokenizer ,SCREAMING_SNAKE_CASE_				:       CLIPTextModelWithProjection ,SCREAMING_SNAKE_CASE_				:       UnCLIPScheduler ,SCREAMING_SNAKE_CASE_				:       Optional[dict] = None ,):
										super().__init__()

										snake_case__    :   Optional[Any]							     =   {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

										snake_case__    :   List[Any]							     =   decoder_pipe_kwargs.get('''torch_dtype''' ,SCREAMING_SNAKE_CASE_ ) or prior.dtype

										snake_case__    :   str							     =   StableDiffusionImageVariationPipeline.from_pretrained(
										    '''lambdalabs/sd-image-variations-diffusers''' ,**SCREAMING_SNAKE_CASE_ )

										# replace `_encode_image` method
										snake_case__    :   Tuple							     =   types.MethodType(_encode_image ,self.decoder_pipe )

										self.register_modules(
										    prior=SCREAMING_SNAKE_CASE_ ,tokenizer=SCREAMING_SNAKE_CASE_ ,text_encoder=SCREAMING_SNAKE_CASE_ ,prior_scheduler=SCREAMING_SNAKE_CASE_ ,)

				def  _lowerCamelCase   (				self				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       int ,SCREAMING_SNAKE_CASE_				:       Dict ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       List[str] ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,):
										if text_model_output is None:
																snake_case__    :   str							     =   len(SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) else 1
																# get prompt text embeddings
																snake_case__    :   Dict							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,return_tensors='''pt''' ,)
																snake_case__    :   List[Any]							     =   text_inputs.input_ids
																snake_case__    :   List[str]							     =   text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )

																if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																						snake_case__    :   Optional[int]							     =   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :] )
																						logger.warning(
																						    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
																						    F''' {self.tokenizer.model_max_length} tokens: {removed_text}''' )
																						snake_case__    :   str							     =   text_input_ids[:, : self.tokenizer.model_max_length]

																snake_case__    :   Tuple							     =   self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Any							     =   text_encoder_output.text_embeds
																snake_case__    :   List[Any]							     =   text_encoder_output.last_hidden_state

										else:
																snake_case__    :   List[Any]							     =   text_model_output[0].shape[0]
																snake_case__     ,		snake_case__    :   List[Any]							     =   text_model_output[0], text_model_output[1]
																snake_case__    :   Tuple							     =   text_attention_mask

										snake_case__    :   Tuple							     =   text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )
										snake_case__    :   Optional[int]							     =   text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )

										if do_classifier_free_guidance:
																snake_case__    :   Optional[Any]							     =   [''''''] * batch_size

																snake_case__    :   str							     =   self.tokenizer(
																    SCREAMING_SNAKE_CASE_ ,padding='''max_length''' ,max_length=self.tokenizer.model_max_length ,truncation=SCREAMING_SNAKE_CASE_ ,return_tensors='''pt''' ,)
																snake_case__    :   Union[str, Any]							     =   uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Tuple							     =   self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_ ) )

																snake_case__    :   Optional[Any]							     =   uncond_embeddings_text_encoder_output.text_embeds
																snake_case__    :   Optional[int]							     =   uncond_embeddings_text_encoder_output.last_hidden_state

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

																snake_case__    :   Tuple							     =   uncond_embeddings.shape[1]
																snake_case__    :   int							     =   uncond_embeddings.repeat(1 ,SCREAMING_SNAKE_CASE_ )
																snake_case__    :   Optional[int]							     =   uncond_embeddings.view(batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ )

																snake_case__    :   Tuple							     =   uncond_text_encoder_hidden_states.shape[1]
																snake_case__    :   int							     =   uncond_text_encoder_hidden_states.repeat(1 ,SCREAMING_SNAKE_CASE_ ,1 )
																snake_case__    :   Optional[Any]							     =   uncond_text_encoder_hidden_states.view(
																    batch_size * num_images_per_prompt ,SCREAMING_SNAKE_CASE_ ,-1 )
																snake_case__    :   int							     =   uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_ ,dim=0 )

																# done duplicates

																# For classifier free guidance, we need to do two forward passes.
																# Here we concatenate the unconditional and text embeddings into a single batch
																# to avoid doing two forward passes
																snake_case__    :   Dict							     =   torch.cat([uncond_embeddings, text_embeddings] )
																snake_case__    :   Dict							     =   torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states] )

																snake_case__    :   Optional[int]							     =   torch.cat([uncond_text_mask, text_mask] )

										return text_embeddings, text_encoder_hidden_states, text_mask

				@property
				def  _lowerCamelCase   (				self				:       Dict ):
										if self.device != torch.device('''meta''' ) or not hasattr(self.prior ,'''_hf_hook''' ):
																return self.device
										for module in self.prior.modules():
																if (
																    hasattr(SCREAMING_SNAKE_CASE_ ,'''_hf_hook''' )
																    and hasattr(module._hf_hook ,'''execution_device''' )
																    and module._hf_hook.execution_device is not None
																):
																						return torch.device(module._hf_hook.execution_device )
										return self.device

				def  _lowerCamelCase   (				self				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Optional[Any] ,SCREAMING_SNAKE_CASE_				:       Union[str, Any] ,SCREAMING_SNAKE_CASE_				:       List[Any] ,SCREAMING_SNAKE_CASE_				:       Optional[int] ,SCREAMING_SNAKE_CASE_				:       Dict ):
										if latents is None:
																snake_case__    :   str							     =   randn_tensor(SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ ,dtype=SCREAMING_SNAKE_CASE_ )
										else:
																if latents.shape != shape:
																						raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
																snake_case__    :   Any							     =   latents.to(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Optional[Any]							     =   latents * scheduler.init_noise_sigma
										return latents

				def  _lowerCamelCase   (				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, torch.device]] = None ):
										self.decoder_pipe.to(SCREAMING_SNAKE_CASE_ )
										super().to(SCREAMING_SNAKE_CASE_ )





				@torch.no_grad()
				def __call__(				self				:       str ,SCREAMING_SNAKE_CASE_				:       Optional[Union[str, List[str]]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       Optional[int] = None ,SCREAMING_SNAKE_CASE_				:       int = 1 ,SCREAMING_SNAKE_CASE_				:       int = 2_5 ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Generator] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.FloatTensor] = None ,SCREAMING_SNAKE_CASE_				:       Optional[Union[CLIPTextModelOutput, Tuple]] = None ,SCREAMING_SNAKE_CASE_				:       Optional[torch.Tensor] = None ,SCREAMING_SNAKE_CASE_				:       float = 4.0 ,SCREAMING_SNAKE_CASE_				:       float = 8.0 ,SCREAMING_SNAKE_CASE_				:       int = 5_0 ,SCREAMING_SNAKE_CASE_				:       Optional[int] = 1 ,SCREAMING_SNAKE_CASE_				:       float = 0.0 ,SCREAMING_SNAKE_CASE_				:       Optional[str] = "pil" ,SCREAMING_SNAKE_CASE_				:       bool = True ,):
										if prompt is not None:
																if isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   Dict							     =   1
																elif isinstance(SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ):
																						snake_case__    :   List[Any]							     =   len(SCREAMING_SNAKE_CASE_ )
																else:
																						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_ )}''' )
										else:
																snake_case__    :   Tuple							     =   text_model_output[0].shape[0]

										snake_case__    :   List[str]							     =   self._execution_device

										snake_case__    :   str							     =   batch_size * num_images_per_prompt

										snake_case__    :   List[str]							     =   prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

										snake_case__     ,		snake_case__     ,		snake_case__    :   Tuple							     =   self._encode_prompt(
										    SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ )

										# prior

										self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_ ,device=SCREAMING_SNAKE_CASE_ )
										snake_case__    :   str							     =   self.prior_scheduler.timesteps

										snake_case__    :   str							     =   self.prior.config.embedding_dim

										snake_case__    :   Optional[int]							     =   self.prepare_latents(
										    (batch_size, embedding_dim) ,text_embeddings.dtype ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,self.prior_scheduler ,)

										for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_ ) ):
																# expand the latents if we are doing classifier free guidance
																snake_case__    :   Tuple							     =   torch.cat([prior_latents] * 2 ) if do_classifier_free_guidance else prior_latents

																snake_case__    :   Optional[int]							     =   self.prior(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,proj_embedding=SCREAMING_SNAKE_CASE_ ,encoder_hidden_states=SCREAMING_SNAKE_CASE_ ,attention_mask=SCREAMING_SNAKE_CASE_ ,).predicted_image_embedding

																if do_classifier_free_guidance:
																						snake_case__     ,		snake_case__    :   Dict							     =   predicted_image_embedding.chunk(2 )
																						snake_case__    :   Any							     =   predicted_image_embedding_uncond + prior_guidance_scale * (
																						    predicted_image_embedding_text - predicted_image_embedding_uncond
																						)

																if i + 1 == prior_timesteps_tensor.shape[0]:
																						snake_case__    :   str							     =   None
																else:
																						snake_case__    :   Tuple							     =   prior_timesteps_tensor[i + 1]

																snake_case__    :   List[Any]							     =   self.prior_scheduler.step(
																    SCREAMING_SNAKE_CASE_ ,timestep=SCREAMING_SNAKE_CASE_ ,sample=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,prev_timestep=SCREAMING_SNAKE_CASE_ ,).prev_sample

										snake_case__    :   Union[str, Any]							     =   self.prior.post_process_latents(SCREAMING_SNAKE_CASE_ )

										snake_case__    :   Any							     =   prior_latents

										snake_case__    :   Dict							     =   self.decoder_pipe(
										    image=SCREAMING_SNAKE_CASE_ ,height=SCREAMING_SNAKE_CASE_ ,width=SCREAMING_SNAKE_CASE_ ,num_inference_steps=SCREAMING_SNAKE_CASE_ ,guidance_scale=SCREAMING_SNAKE_CASE_ ,generator=SCREAMING_SNAKE_CASE_ ,output_type=SCREAMING_SNAKE_CASE_ ,return_dict=SCREAMING_SNAKE_CASE_ ,num_images_per_prompt=SCREAMING_SNAKE_CASE_ ,eta=SCREAMING_SNAKE_CASE_ ,)
										return output


