

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:
						_SCREAMING_SNAKE_CASE          = None

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

_SCREAMING_SNAKE_CASE          = """[INST]""", """[/INST]"""
_SCREAMING_SNAKE_CASE          = """<<SYS>>\n""", """\n<</SYS>>\n\n"""

# fmt: off
_SCREAMING_SNAKE_CASE          = """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     lowerCamelCase_   ( UpperCAmelCase__):
	_lowerCAmelCase       		=  VOCAB_FILES_NAMES
	_lowerCAmelCase       		=  LlamaTokenizer
	_lowerCAmelCase       		=  'left'
	_lowerCAmelCase       		=  ['input_ids', 'attention_mask']




	def __init__( self     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=None     ,     _UpperCAmelCase=False     ,     _UpperCAmelCase="<unk>"     ,     _UpperCAmelCase="<s>"     ,     _UpperCAmelCase="</s>"     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=False     ,     **_UpperCAmelCase     ,     ):
			super().__init__(
			    vocab_file=lowerCamelCase__     ,     tokenizer_file=lowerCamelCase__     ,     clean_up_tokenization_spaces=lowerCamelCase__     ,     unk_token=lowerCamelCase__     ,     bos_token=lowerCamelCase__     ,     eos_token=lowerCamelCase__     ,     **lowerCamelCase__     ,     )
			__UpperCAmelCase  :     Dict		=    add_bos_token
			__UpperCAmelCase  :     Tuple		=    add_eos_token
			self.update_post_processor()

			__UpperCAmelCase  :     List[Any]		=    vocab_file
			__UpperCAmelCase  :     Tuple		=    False if not self.vocab_file else True




	def 						a					( self       ):
			__UpperCAmelCase  :     Optional[int]		=    self.bos_token
			__UpperCAmelCase  :     List[str]		=    self.bos_token_id

			__UpperCAmelCase  :     Union[str, Any]		=    self.eos_token
			__UpperCAmelCase  :     str		=    self.eos_token_id

			__UpperCAmelCase  :     int		=    F"{(bos+':0 ') * self.add_bos_token}$A:0{(' '+eos+':0') * self.add_eos_token}"
			__UpperCAmelCase  :     List[str]		=    F"{single}{(' '+bos+':1') * self.add_bos_token} $B:1{(' '+eos+':1') * self.add_eos_token}"

			__UpperCAmelCase  :     Optional[Any]		=    []
			if self.add_bos_token:
					special_tokens.append((bos, bos_token_id)       )
			if self.add_eos_token:
					special_tokens.append((eos, eos_token_id)       )
			__UpperCAmelCase  :     Any		=    processors.TemplateProcessing(
			    single=lowerCamelCase__     ,     pair=lowerCamelCase__     ,     special_tokens=lowerCamelCase__       )




	@property
	def 						a					( self       ):
			return self._add_eos_token




	@property
	def 						a					( self       ):
			return self._add_bos_token




	@add_eos_token.setter
	def 						a					( self     ,     _UpperCAmelCase       ):
			__UpperCAmelCase  :     int		=    value
			self.update_post_processor()




	@add_bos_token.setter
	def 						a					( self     ,     _UpperCAmelCase       ):
			__UpperCAmelCase  :     int		=    value
			self.update_post_processor()




	def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase = 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(lowerCamelCase__       ):
					logger.error(F"Vocabulary path ({save_directory}) should be a directory"       )
					return
			__UpperCAmelCase  :     Any		=    os.path.join(
			    lowerCamelCase__     ,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

			return (out_vocab_file,)




	def 						a					( self     ,     _UpperCAmelCase       ):
			__UpperCAmelCase  :     List[str]		=    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...)'       )

			__UpperCAmelCase  :     Tuple		=    []
			if len(conversation.past_user_inputs       ) > 0:
					if not conversation.past_user_inputs[0].startswith(lowerCamelCase__       ) or E_SYS not in conversation.past_user_inputs[0]:
							__UpperCAmelCase  :     int		=    (
							    B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS + conversation.past_user_inputs[0]
							)
			elif not dialogue[0][1].startswith(lowerCamelCase__       ) or E_SYS not in dialogue[0][1]:
					__UpperCAmelCase  :     Any		=    (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=lowerCamelCase__       )
			        + [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=lowerCamelCase__       )
			return dialog_tokens








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


_SCREAMING_SNAKE_CASE          = False


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




 def 						a					( self       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Any		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     Union[str, Any]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_UpperCAmelCase       )

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

   __UpperCAmelCase  :     List[str]		=    generator.manual_seed(0       )
   __UpperCAmelCase  :     Optional[int]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     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 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    'cyberpunk 2077'
   __UpperCAmelCase  :     Dict		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     List[Any]		=    pipe(
       prompt=_UpperCAmelCase     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=50     ,     output_type='numpy'     ,     ).images

   __UpperCAmelCase  :     Tuple		=    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   __UpperCAmelCase  :     Tuple		=    np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]       )

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





