from logging import getLogger
from typing import Any, Callable, List, Optional, Union

import numpy as np
import PIL
import torch

from ...schedulers import DDPMScheduler
from ..onnx_utils import ORT_TO_NP_TYPE, OnnxRuntimeModel
from ..pipeline_utils import ImagePipelineOutput
from . import StableDiffusionUpscalePipeline


lowerCAmelCase__  	=      getLogger(__name__)


lowerCAmelCase__  	=      4
lowerCAmelCase__  	=      7

lowerCAmelCase__  	=      {
    "float16": torch.floataa,
    "float32": torch.floataa,
}

def 	__lowerCamelCase							(				__a    : List[str]		)      ->       Optional[int]:
	if isinstance(__a      ,							torch.Tensor		):
		return image
	elif isinstance(__a      ,							PIL.Image.Image		):
		_lowercase   =[image]

	if isinstance(image[0]      ,							PIL.Image.Image		):
		_lowercase   ,  _lowercase   =image[0].size
		_lowercase   ,  _lowercase   =(x - x % 64 for x in (w, h))  # resize to integer multiple of 32

		_lowercase   =[np.array(i.resize((w, h)		)		)[None, :] for i in image]
		_lowercase   =np.concatenate(__a      ,							axis=0		)
		_lowercase   =np.array(__a		).astype(np.floataa		) / 255.0
		_lowercase   =image.transpose(0      ,							3      ,							1      ,							2		)
		_lowercase   =2.0 * image - 1.0
		_lowercase   =torch.from_numpy(__a		)
	elif isinstance(image[0]      ,							torch.Tensor		):
		_lowercase   =torch.cat(__a      ,							dim=0		)

	return image
class 	_a  (							lowerCamelCase_					):



	"""simple docstring"""



	def __init__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 350       ,  ):
		super().__init__(
		    vae=lowerCAmelCase_       ,  text_encoder=lowerCAmelCase_       ,  tokenizer=lowerCAmelCase_       ,  unet=lowerCAmelCase_       ,  low_res_scheduler=lowerCAmelCase_       ,  scheduler=lowerCAmelCase_       ,  safety_checker=lowerCAmelCase_       ,  feature_extractor=lowerCAmelCase_       ,  watermarker=lowerCAmelCase_       ,  max_noise_level=lowerCAmelCase_       ,  )


	def __call__(   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 75       ,  lowerCAmelCase_ = 9.0       ,  lowerCAmelCase_ = 20       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = 1       ,  lowerCAmelCase_ = 0.0       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = "pil"       ,  lowerCAmelCase_ = True       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = 1       ,  ):
		self.check_inputs(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

		# 2. Define call parameters
		if prompt is not None and isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =1
		elif prompt is not None and isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =len(lowerCAmelCase_       )
		else:
			_lowercase   =prompt_embeds.shape[0]

		_lowercase   =self._execution_device
		# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
		# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
		# corresponds to doing no classifier free guidance.
		_lowercase   =guidance_scale > 1.0

		# 3. Encode input prompt
		_lowercase   =self._encode_prompt(
		    lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  prompt_embeds=lowerCAmelCase_       ,  negative_prompt_embeds=lowerCAmelCase_       ,  )

		_lowercase   =ORT_TO_PT_TYPE[str(text_embeddings.dtype       )]

		# 4. Preprocess image
		_lowercase   =preprocess(lowerCAmelCase_       )
		_lowercase   =image.cpu()

		# 5. set timesteps
		self.scheduler.set_timesteps(lowerCAmelCase_       ,  device=lowerCAmelCase_       )
		_lowercase   =self.scheduler.timesteps

		# 5. Add noise to image
		_lowercase   =torch.tensor([noise_level]       ,  dtype=torch.long       ,  device=lowerCAmelCase_       )
		_lowercase   =torch.randn(image.shape       ,  generator=lowerCAmelCase_       ,  device=lowerCAmelCase_       ,  dtype=lowerCAmelCase_       )
		_lowercase   =self.low_res_scheduler.add_noise(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =2 if do_classifier_free_guidance else 1
		_lowercase   =np.concatenate([image] * batch_multiplier * num_images_per_prompt       )
		_lowercase   =np.concatenate([noise_level] * image.shape[0]       )

		# 6. Prepare latent variables
		_lowercase   ,  _lowercase   =image.shape[2:]
		_lowercase   =self.prepare_latents(
		    batch_size * num_images_per_prompt       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  )

		# 7. Check that sizes of image and latents match
		_lowercase   =image.shape[1]
		if NUM_LATENT_CHANNELS + num_channels_image != NUM_UNET_INPUT_CHANNELS:
			raise ValueError(
			    "Incorrect configuration settings! The config of `pipeline.unet` expects"
			    F''' {NUM_UNET_INPUT_CHANNELS} but received `num_channels_latents`: {NUM_LATENT_CHANNELS} +'''
			    F''' `num_channels_image`: {num_channels_image} '''
			    F''' = {NUM_LATENT_CHANNELS+num_channels_image}. Please verify the config of'''
			    " `pipeline.unet` or your `image` input."       )

		# 8. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline
		_lowercase   =self.prepare_extra_step_kwargs(lowerCAmelCase_       ,  lowerCAmelCase_       )

		_lowercase   =next(
		    (input.type for input in self.unet.model.get_inputs() if input.name == "timestep")       ,  "tensor(float)"       )
		_lowercase   =ORT_TO_NP_TYPE[timestep_dtype]

		# 9. Denoising loop
		_lowercase   =len(lowerCAmelCase_       ) - num_inference_steps * self.scheduler.order
		with self.progress_bar(total=lowerCAmelCase_       ) as progress_bar:
			for i, t in enumerate(lowerCAmelCase_       ):
				# expand the latents if we are doing classifier free guidance
				_lowercase   =np.concatenate([latents] * 2       ) if do_classifier_free_guidance else latents

				# concat latents, mask, masked_image_latents in the channel dimension
				_lowercase   =self.scheduler.scale_model_input(lowerCAmelCase_       ,  lowerCAmelCase_       )
				_lowercase   =np.concatenate([latent_model_input, image]       ,  axis=1       )

				# timestep to tensor
				_lowercase   =np.array([t]       ,  dtype=lowerCAmelCase_       )

				# predict the noise residual
				_lowercase   =self.unet(
				    sample=lowerCAmelCase_       ,  timestep=lowerCAmelCase_       ,  encoder_hidden_states=lowerCAmelCase_       ,  class_labels=noise_level.astype(np.intaa       )       ,  )[0]

				# perform guidance
				if do_classifier_free_guidance:
					_lowercase   ,  _lowercase   =np.split(lowerCAmelCase_       ,  2       )
				_lowercase   =noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

				# compute the previous noisy sample x_t -> x_t-1
				_lowercase   =self.scheduler.step(
				    torch.from_numpy(lowerCAmelCase_       )       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  **lowerCAmelCase_       ).prev_sample

				# call the callback, if provided
				if i == len(lowerCAmelCase_       ) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0):
					progress_bar.update()
					if callback is not None and i % callback_steps == 0:
						callback(lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       )

        # 10. Post-processing
		_lowercase   =self.decode_latents(latents.float()       )

		# 11. Convert to PIL
		if output_type == "pil":
			_lowercase   =self.numpy_to_pil(lowerCAmelCase_       )

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=lowerCAmelCase_       )


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =1 / 0.0_8_3_3_3 * latents
		_lowercase   =self.vae(latent_sample=lowerCAmelCase_       )[0]
		_lowercase   =np.clip(image / 2 + 0.5       ,  0       ,  1       )
		_lowercase   =image.transpose((0, 2, 3, 1)       )
		return image


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = None       ,  lowerCAmelCase_ = None       ,  ):
		if prompt is not None and isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =1
		elif prompt is not None and isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
			_lowercase   =len(lowerCAmelCase_       )
		else:
			_lowercase   =prompt_embeds.shape[0]

		if prompt_embeds is None:
			_lowercase   =self.tokenizer(
			    lowerCAmelCase_       ,  padding="max_length"       ,  max_length=self.tokenizer.model_max_length       ,  truncation=lowerCAmelCase_       ,  return_tensors="pt"       ,  )
			_lowercase   =text_inputs.input_ids
			_lowercase   =self.tokenizer(lowerCAmelCase_       ,  padding="longest"       ,  return_tensors="pt"       ).input_ids

			if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
			    lowerCAmelCase_       ,  lowerCAmelCase_       ):
				_lowercase   =self.tokenizer.batch_decode(
				    untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]       )
				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}'''       )

			# no positional arguments to text_encoder
			_lowercase   =self.text_encoder(
			    input_ids=text_input_ids.int().to(lowerCAmelCase_       )       ,  )
			_lowercase   =prompt_embeds[0]

		_lowercase   ,  _lowercase   ,  _lowercase   =prompt_embeds.shape
		# duplicate text embeddings for each generation per prompt, using mps friendly method
		_lowercase   =prompt_embeds.repeat(1       ,  lowerCAmelCase_       )
		_lowercase   =prompt_embeds.reshape(bs_embed * num_images_per_prompt       ,  lowerCAmelCase_       ,  -1       )

		# get unconditional embeddings for classifier free guidance
		if do_classifier_free_guidance and negative_prompt_embeds is None:
			_lowercase   =42
			if negative_prompt is None:
				_lowercase   =[""] * batch_size
			elif type(lowerCAmelCase_       ) is not type(lowerCAmelCase_       ):
				raise TypeError(
				    F'''`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase_       )} !='''
				    F''' {type(lowerCAmelCase_       )}.'''       )
			elif isinstance(lowerCAmelCase_       ,  lowerCAmelCase_       ):
				_lowercase   =[negative_prompt]
			elif batch_size != len(lowerCAmelCase_       ):
				raise ValueError(
				    F'''`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase_       )}, but `prompt`:'''
				    F''' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'''
				    " the batch size of `prompt`."       )
			else:
				_lowercase   =negative_prompt

			_lowercase   =text_input_ids.shape[-1]
			_lowercase   =self.tokenizer(
			    lowerCAmelCase_       ,  padding="max_length"       ,  max_length=lowerCAmelCase_       ,  truncation=lowerCAmelCase_       ,  return_tensors="pt"       ,  )

			# if hasattr(uncond_input, "attention_mask"):
			#     attention_mask = uncond_input.attention_mask.to(device)
			# else:
			#     attention_mask = None

			_lowercase   =self.text_encoder(
			    input_ids=uncond_input.input_ids.int().to(lowerCAmelCase_       )       ,  )
			_lowercase   =uncond_embeddings[0]

		if do_classifier_free_guidance:
			_lowercase   =uncond_embeddings.shape[1]
			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
			_lowercase   =uncond_embeddings.repeat(1       ,  lowerCAmelCase_       )
			_lowercase   =uncond_embeddings.reshape(batch_size * num_images_per_prompt       ,  lowerCAmelCase_       ,  -1       )

			# 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
			_lowercase   =np.concatenate([uncond_embeddings, prompt_embeds]       )

		return prompt_embeds





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:
						lowerCAmelCase__  	=      None

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

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

# fmt: off
lowerCAmelCase__  	=      "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your\\nanswers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure\\nthat your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not\\ncorrect. If you don't know the answer to a question, please don't share false information."
class 	_a  (							lowerCamelCase_					):



	"""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       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=None       ,  lowerCAmelCase_=False       ,  lowerCAmelCase_="<unk>"       ,  lowerCAmelCase_="<s>"       ,  lowerCAmelCase_="</s>"       ,  lowerCAmelCase_=True       ,  lowerCAmelCase_=False       ,  **lowerCAmelCase_       ,  ):
		super().__init__(
		    vocab_file=lowerCAmelCase_       ,  tokenizer_file=lowerCAmelCase_       ,  clean_up_tokenization_spaces=lowerCAmelCase_       ,  unk_token=lowerCAmelCase_       ,  bos_token=lowerCAmelCase_       ,  eos_token=lowerCAmelCase_       ,  **lowerCAmelCase_       ,  )
		_lowercase   =add_bos_token
		_lowercase   =add_eos_token
		self.update_post_processor()

		_lowercase   =vocab_file
		_lowercase   =False if not self.vocab_file else True


	def __lowerCAmelCase      (   self       ):
		_lowercase   =self.bos_token
		_lowercase   =self.bos_token_id

		_lowercase   =self.eos_token
		_lowercase   =self.eos_token_id

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

		_lowercase   =[]
		if self.add_bos_token:
			special_tokens.append((bos, bos_token_id)       )
		if self.add_eos_token:
			special_tokens.append((eos, eos_token_id)       )
		_lowercase   =processors.TemplateProcessing(
		    single=lowerCAmelCase_       ,  pair=lowerCAmelCase_       ,  special_tokens=lowerCAmelCase_       )


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


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


	@add_eos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	@add_bos_token.setter
	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =value
		self.update_post_processor()


	def __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ,  lowerCAmelCase_ = 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
		_lowercase   =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 __lowerCAmelCase      (   self       ,  lowerCAmelCase_       ):
		_lowercase   =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...)"       )

		_lowercase   =[]
		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]:
				_lowercase   =(
				    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]:
			_lowercase   =(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




