
"""simple docstring"""
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


lowercase		:						List[Any] =					getLogger(__name__)


lowercase		:						str =					4
lowercase		:						Optional[Any] =					7

lowercase		:						int =					{
    'float16': torch.floataa,
    'float32': torch.floataa,
}


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Any:


					'''simple docstring'''
					if isinstance(lowerCamelCase_	,				torch.Tensor ):
										return image
					elif isinstance(lowerCamelCase_	,				PIL.Image.Image ):
										__a:						List[Any]		    =					[image]

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

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

					return image




class    _snake_case  (						_lowercase       ):






							def __init__(					self						:			Optional[Any]						, snake_case__						:			OnnxRuntimeModel						, snake_case__						:			OnnxRuntimeModel						, snake_case__						:			Any						, snake_case__						:			OnnxRuntimeModel						, snake_case__						:			DDPMScheduler						, snake_case__						:			Any						, snake_case__						:			int = 3_5_0						, ):






												"""simple docstring"""



												super().__init__(
												    vae=snake_case__						, text_encoder=snake_case__						, tokenizer=snake_case__						, unet=snake_case__						, low_res_scheduler=snake_case__						, scheduler=snake_case__						, safety_checker=snake_case__						, feature_extractor=snake_case__						, watermarker=snake_case__						, max_noise_level=snake_case__						, )






							def __call__(					self						:			int						, snake_case__						:			Union[str, List[str]]						, snake_case__						:			Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]						, snake_case__						:			int = 7_5						, snake_case__						:			float = 9.0						, snake_case__						:			int = 2_0						, snake_case__						:			Optional[Union[str, List[str]]] = None						, snake_case__						:			Optional[int] = 1						, snake_case__						:			float = 0.0						, snake_case__						:			Optional[Union[torch.Generator, List[torch.Generator]]] = None						, snake_case__						:			Optional[torch.FloatTensor] = None						, snake_case__						:			Optional[np.ndarray] = None						, snake_case__						:			Optional[np.ndarray] = None						, snake_case__						:			Optional[str] = "pil"						, snake_case__						:			bool = True						, snake_case__						:			Optional[Callable[[int, int, torch.FloatTensor], None]] = None						, snake_case__						:			Optional[int] = 1						, ):






												"""simple docstring"""



												self.check_inputs(snake_case__						, snake_case__						, snake_case__						, snake_case__    )

												# 2. Define call parameters
												if prompt is not None and isinstance(snake_case__						, snake_case__    ):
																	__a:						List[Any]		    =					1
												elif prompt is not None and isinstance(snake_case__						, snake_case__    ):
																	__a:						List[str]		    =					len(snake_case__    )
												else:
																	__a:						List[str]		    =					prompt_embeds.shape[0]

												__a:						Union[str, Any]		    =					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.
												__a:						Any		    =					guidance_scale > 1.0

												# 3. Encode input prompt
												__a:						Optional[Any]		    =					self._encode_prompt(
												    snake_case__						, snake_case__						, snake_case__						, snake_case__						, snake_case__						, prompt_embeds=snake_case__						, negative_prompt_embeds=snake_case__						, )

												__a:						Tuple		    =					ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

												# 4. Preprocess image
												__a:						Any		    =					preprocess(snake_case__    )
												__a:						List[Any]		    =					image.cpu()

												# 5. set timesteps
												self.scheduler.set_timesteps(snake_case__						, device=snake_case__    )
												__a:						Optional[int]		    =					self.scheduler.timesteps

												# 5. Add noise to image
												__a:						List[Any]		    =					torch.tensor([noise_level]						, dtype=torch.long						, device=snake_case__    )
												__a:						Union[str, Any]		    =					torch.randn(image.shape						, generator=snake_case__						, device=snake_case__						, dtype=snake_case__    )
												__a:						List[Any]		    =					self.low_res_scheduler.add_noise(snake_case__						, snake_case__						, snake_case__    )

												__a:						List[Any]		    =					2 if do_classifier_free_guidance else 1
												__a:						List[Any]		    =					np.concatenate([image] * batch_multiplier * num_images_per_prompt    )
												__a:						Union[str, Any]		    =					np.concatenate([noise_level] * image.shape[0]    )

												# 6. Prepare latent variables
												__a    ,				__a:						List[Any]		    =					image.shape[2:]
												__a:						Optional[Any]		    =					self.prepare_latents(
												    batch_size * num_images_per_prompt						, snake_case__						, snake_case__						, snake_case__						, snake_case__						, snake_case__						, snake_case__						, snake_case__						, )

												# 7. Check that sizes of image and latents match
												__a:						Union[str, Any]		    =					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
												__a:						Optional[int]		    =					self.prepare_extra_step_kwargs(snake_case__						, snake_case__    )

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

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

																						# concat latents, mask, masked_image_latents in the channel dimension
																						__a:						int		    =					self.scheduler.scale_model_input(snake_case__						, snake_case__    )
																						__a:						List[Any]		    =					np.concatenate([latent_model_input, image]						, axis=1    )

																						# timestep to tensor
																						__a:						List[Any]		    =					np.array([t]						, dtype=snake_case__    )

																						# predict the noise residual
																						__a:						List[str]		    =					self.unet(
																						    sample=snake_case__						, timestep=snake_case__						, encoder_hidden_states=snake_case__						, class_labels=noise_level.astype(np.intaa    )						, )[0]

																						# perform guidance
																						if do_classifier_free_guidance:
																											__a    ,				__a:						Tuple		    =					np.split(snake_case__						, 2    )
																						__a:						List[str]		    =					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																						# compute the previous noisy sample x_t -> x_t-1
																						__a:						Dict		    =					self.scheduler.step(
																						    torch.from_numpy(snake_case__    )						, snake_case__						, snake_case__						, **snake_case__    ).prev_sample

																						# call the callback, if provided
																						if i == len(snake_case__    ) - 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(snake_case__						, snake_case__						, snake_case__    )

        # 10. Post-processing
												__a:						Tuple		    =					self.decode_latents(latents.float()    )

												# 11. Convert to PIL
												if output_type == "pil":
																	__a:						str		    =					self.numpy_to_pil(snake_case__    )

												if not return_dict:
																	return (image,)

												return ImagePipelineOutput(images=snake_case__    )






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






												"""simple docstring"""



												__a:						int		    =					1 / 0.08_333 * latents
												__a:						Optional[Any]		    =					self.vae(latent_sample=snake_case__    )[0]
												__a:						Dict		    =					np.clip(image / 2 + 0.5						, 0						, 1    )
												__a:						str		    =					image.transpose((0, 2, 3, 1)    )
												return image






							def 							__lowercase  (					self						:			str						, snake_case__						:			Union[str, List[str]]						, snake_case__						:			Tuple						, snake_case__						:			Optional[int]						, snake_case__						:			bool						, snake_case__						:			Optional[str]						, snake_case__						:			Optional[torch.FloatTensor] = None						, snake_case__						:			Optional[torch.FloatTensor] = None						, ):






												"""simple docstring"""



												if prompt is not None and isinstance(snake_case__						, snake_case__    ):
																	__a:						List[str]		    =					1
												elif prompt is not None and isinstance(snake_case__						, snake_case__    ):
																	__a:						Tuple		    =					len(snake_case__    )
												else:
																	__a:						List[str]		    =					prompt_embeds.shape[0]

												if prompt_embeds is None:
																	__a:						Optional[Any]		    =					self.tokenizer(
																	    snake_case__						, padding="""max_length"""						, max_length=self.tokenizer.model_max_length						, truncation=snake_case__						, return_tensors="""pt"""						, )
																	__a:						Optional[int]		    =					text_inputs.input_ids
																	__a:						List[str]		    =					self.tokenizer(snake_case__						, padding="""longest"""						, return_tensors="""pt"""    ).input_ids

																	if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
																	    snake_case__						, snake_case__    ):
																						__a:						List[Any]		    =					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
																	__a:						str		    =					self.text_encoder(
																	    input_ids=text_input_ids.int().to(snake_case__    )						, )
																	__a:						Optional[int]		    =					prompt_embeds[0]

												__a    ,				__a    ,				__a:						Any		    =					prompt_embeds.shape
												# duplicate text embeddings for each generation per prompt, using mps friendly method
												__a:						str		    =					prompt_embeds.repeat(1						, snake_case__    )
												__a:						Dict		    =					prompt_embeds.reshape(bs_embed * num_images_per_prompt						, snake_case__						, -1    )

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

																	__a:						List[Any]		    =					text_input_ids.shape[-1]
																	__a:						Tuple		    =					self.tokenizer(
																	    snake_case__						, padding="""max_length"""						, max_length=snake_case__						, truncation=snake_case__						, return_tensors="""pt"""						, )

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

																	__a:						List[str]		    =					self.text_encoder(
																	    input_ids=uncond_input.input_ids.int().to(snake_case__    )						, )
																	__a:						int		    =					uncond_embeddings[0]

												if do_classifier_free_guidance:
																	__a:						Any		    =					uncond_embeddings.shape[1]
																	# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																	__a:						Optional[Any]		    =					uncond_embeddings.repeat(1						, snake_case__    )
																	__a:						List[str]		    =					uncond_embeddings.reshape(batch_size * num_images_per_prompt						, snake_case__						, -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
																	__a:						Optional[int]		    =					np.concatenate([uncond_embeddings, prompt_embeds]    )

												return prompt_embeds


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
