



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:
						UpperCAmelCase_			      =  None

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

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

# fmt: off
UpperCAmelCase_			      =  '''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 		__SCREAMING_SNAKE_CASE      (				lowercase__      ):






				"""simple docstring"""

				SCREAMING_SNAKE_CASE_ 				=  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_ 				=  LlamaTokenizer
				SCREAMING_SNAKE_CASE_ 				=  'left'
				SCREAMING_SNAKE_CASE_ 				=  ['input_ids', 'attention_mask']







				def __init__(				self						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=None						,	SCREAMING_SNAKE_CASE__=False						,	SCREAMING_SNAKE_CASE__="<unk>"						,	SCREAMING_SNAKE_CASE__="<s>"						,	SCREAMING_SNAKE_CASE__="</s>"						,	SCREAMING_SNAKE_CASE__=True						,	SCREAMING_SNAKE_CASE__=False						,	**SCREAMING_SNAKE_CASE__						,	):






					"""simple docstring"""

					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					:							Optional[int]					     =   add_bos_token
					_snake_case					:							List[Any]					     =   add_eos_token
					self.update_post_processor()

					_snake_case					:							str					     =   vocab_file
					_snake_case					:							Dict					     =   False if not self.vocab_file else True







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   self.bos_token
					_snake_case					:							int					     =   self.bos_token_id

					_snake_case					:							Dict					     =   self.eos_token
					_snake_case					:							List[str]					     =   self.eos_token_id

					_snake_case					:							Optional[Any]					     =   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					:							List[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)      )
					_snake_case					:							Optional[Any]					     =   processors.TemplateProcessing(
					    single=SCREAMING_SNAKE_CASE__						,	pair=SCREAMING_SNAKE_CASE__						,	special_tokens=SCREAMING_SNAKE_CASE__      )







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return self._add_eos_token







				@property
				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					return self._add_bos_token







				@add_eos_token.setter
				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Union[str, Any]					     =   value
					self.update_post_processor()







				@add_bos_token.setter
				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Dict					     =   value
					self.update_post_processor()







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__ = None      ):






					"""simple docstring"""

					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					:							Dict					     =   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						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Union[str, 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					:							Optional[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[str]					     =   (
							    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					:							str					     =   (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










import tempfile
import unittest

import numpy as np
import torch

from diffusers import DiffusionPipeline
from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__ = False      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   10

					if only_cross_attention:
						_snake_case					:							Tuple					     =   12
					else:
						# when only cross attention is not set, the cross attention dim must be the same as the query dim
						_snake_case					:							Any					     =   query_dim

					return {
					    "query_dim": query_dim,
					    "cross_attention_dim": cross_attention_dim,
					    "heads": 2,
					    "dim_head": 4,
					    "added_kv_proj_dim": 6,
					    "norm_num_groups": 1,
					    "only_cross_attention": only_cross_attention,
					    "processor": AttnAddedKVProcessor(),
					}







				def 		__lowerCamelCase(				self						,	SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__      ):






					"""simple docstring"""

					_snake_case					:							Any					     =   2

					_snake_case					:							List[str]					     =   torch.rand(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	3						,	2      )
					_snake_case					:							List[str]					     =   torch.rand(SCREAMING_SNAKE_CASE__						,	4						,	SCREAMING_SNAKE_CASE__      )
					_snake_case					:							Optional[int]					     =   None

					return {
					    "hidden_states": hidden_states,
					    "encoder_hidden_states": encoder_hidden_states,
					    "attention_mask": attention_mask,
					}







				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					torch.manual_seed(0      )

					_snake_case					:							Optional[int]					     =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   Attention(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue(attn.to_k is not None      )
					self.assertTrue(attn.to_v is not None      )

					_snake_case					:							Dict					     =   self.get_forward_arguments(
					    query_dim=constructor_args["""query_dim"""]						,	added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

					_snake_case					:							Any					     =   attn(**SCREAMING_SNAKE_CASE__      )

					# only self attention

					torch.manual_seed(0      )

					_snake_case					:							Any					     =   self.get_constructor_arguments(only_cross_attention=SCREAMING_SNAKE_CASE__      )
					_snake_case					:							List[str]					     =   Attention(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue(attn.to_k is None      )
					self.assertTrue(attn.to_v is None      )

					_snake_case					:							str					     =   self.get_forward_arguments(
					    query_dim=constructor_args["""query_dim"""]						,	added_kv_proj_dim=constructor_args["""added_kv_proj_dim"""]      )

					_snake_case					:							Any					     =   attn(**SCREAMING_SNAKE_CASE__      )

					self.assertTrue((only_cross_attn_out != self_and_cross_attn_out).all()      )





class 		__SCREAMING_SNAKE_CASE      (				unittest.TestCase      ):






				"""simple docstring"""

				def 		__lowerCamelCase(				self      ):






					"""simple docstring"""

					_snake_case					:							str					     =   DiffusionPipeline.from_pretrained("""hf-internal-testing/tiny-stable-diffusion-pipe"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Tuple					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					# the initial conversion succeeds
					_snake_case					:							List[str]					     =   DiffusionPipeline.from_pretrained(
					    """hf-internal-testing/tiny-stable-diffusion-pipe"""						,	device_map="""sequential"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							Union[str, Any]					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					with tempfile.TemporaryDirectory() as tmpdir:
						# save the converted model
						pipe.save_pretrained(SCREAMING_SNAKE_CASE__      )

						# can also load the converted weights
						_snake_case					:							str					     =   DiffusionPipeline.from_pretrained(SCREAMING_SNAKE_CASE__						,	device_map="""sequential"""						,	safety_checker=SCREAMING_SNAKE_CASE__      )

					_snake_case					:							int					     =   pipe(
					    """foo"""						,	num_inference_steps=2						,	generator=torch.Generator("""cpu"""      ).manual_seed(0      )						,	output_type="""np"""						,	).images

					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-5      )      )
					self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__						,	SCREAMING_SNAKE_CASE__						,	atol=1e-5      )      )





