

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_  (				UpperCAmelCase_				:  Any						)    ->					str:




	'''simple docstring'''




	if isinstance(snake_case_				,							torch.Tensor						):
		return image
	elif isinstance(snake_case_				,							PIL.Image.Image						):
		_UpperCamelCase :		Union[str, Any]								=    [image]

	if isinstance(image[0]				,							PIL.Image.Image						):
		_UpperCamelCase :		str								=    image[0].size
		_UpperCamelCase :		Any								=    (x - x % 6_4 for x in (w, h))  # resize to integer multiple of 32

		_UpperCamelCase :		Optional[int]								=    [np.array(i.resize((w, h)						)						)[None, :] for i in image]
		_UpperCamelCase :		List[str]								=    np.concatenate(snake_case_				,							axis=0						)
		_UpperCamelCase :		Optional[int]								=    np.array(snake_case_						).astype(np.floataa						) / 2_5_5.0
		_UpperCamelCase :		List[str]								=    image.transpose(0				,							3				,							1				,							2						)
		_UpperCamelCase :		List[Any]								=    2.0 * image - 1.0
		_UpperCamelCase :		int								=    torch.from_numpy(snake_case_						)
	elif isinstance(image[0]				,							torch.Tensor						):
		_UpperCamelCase :		str								=    torch.cat(snake_case_				,							dim=0						)

	return image







class 						lowercase     (					__A				):


		"""simple docstring"""




		def __init__(					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case = 3_50						,							):
			super().__init__(
			    vae=UpperCamelCase_						,							text_encoder=UpperCamelCase_						,							tokenizer=UpperCamelCase_						,							unet=UpperCamelCase_						,							low_res_scheduler=UpperCamelCase_						,							scheduler=UpperCamelCase_						,							safety_checker=UpperCamelCase_						,							feature_extractor=UpperCamelCase_						,							watermarker=UpperCamelCase_						,							max_noise_level=UpperCamelCase_						,							)


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

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

			_UpperCamelCase :		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.
			_UpperCamelCase :		Dict								=    guidance_scale > 1.0

			# 3. Encode input prompt
			_UpperCamelCase :		str								=    self._encode_prompt(
			    UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							prompt_embeds=UpperCamelCase_						,							negative_prompt_embeds=UpperCamelCase_						,							)

			_UpperCamelCase :		Dict								=    ORT_TO_PT_TYPE[str(text_embeddings.dtype)]

			# 4. Preprocess image
			_UpperCamelCase :		str								=    preprocess(UpperCamelCase_)
			_UpperCamelCase :		int								=    image.cpu()

			# 5. set timesteps
			self.scheduler.set_timesteps(UpperCamelCase_						,							device=UpperCamelCase_)
			_UpperCamelCase :		Tuple								=    self.scheduler.timesteps

			# 5. Add noise to image
			_UpperCamelCase :		int								=    torch.tensor([noise_level]						,							dtype=torch.long						,							device=UpperCamelCase_)
			_UpperCamelCase :		List[Any]								=    torch.randn(image.shape						,							generator=UpperCamelCase_						,							device=UpperCamelCase_						,							dtype=UpperCamelCase_)
			_UpperCamelCase :		Tuple								=    self.low_res_scheduler.add_noise(UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_)

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

			# 6. Prepare latent variables
			_UpperCamelCase :		Optional[int]								=    image.shape[2:]
			_UpperCamelCase :		Optional[Any]								=    self.prepare_latents(
			    batch_size * num_images_per_prompt						,							UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							UpperCamelCase_						,							)

			# 7. Check that sizes of image and latents match
			_UpperCamelCase :		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
			_UpperCamelCase :		Dict								=    self.prepare_extra_step_kwargs(UpperCamelCase_						,							UpperCamelCase_)

			_UpperCamelCase :		str								=    next(
			    (input.type for input in self.unet.model.get_inputs() if input.name == 'timestep')						,							'tensor(float)')
			_UpperCamelCase :		List[str]								=    ORT_TO_NP_TYPE[timestep_dtype]

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

					# concat latents, mask, masked_image_latents in the channel dimension
					_UpperCamelCase :		Union[str, Any]								=    self.scheduler.scale_model_input(UpperCamelCase_						,							UpperCamelCase_)
					_UpperCamelCase :		Union[str, Any]								=    np.concatenate([latent_model_input, image]						,							axis=1)

					# timestep to tensor
					_UpperCamelCase :		Dict								=    np.array([t]						,							dtype=UpperCamelCase_)

					# predict the noise residual
					_UpperCamelCase :		Optional[int]								=    self.unet(
					    sample=UpperCamelCase_						,							timestep=UpperCamelCase_						,							encoder_hidden_states=UpperCamelCase_						,							class_labels=noise_level.astype(np.intaa)						,							)[0]

					# perform guidance
					if do_classifier_free_guidance:
						_UpperCamelCase :		Union[str, Any]								=    np.split(UpperCamelCase_						,							2)
					_UpperCamelCase :		int								=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

					# compute the previous noisy sample x_t -> x_t-1
					_UpperCamelCase :		Union[str, Any]								=    self.scheduler.step(
					    torch.from_numpy(UpperCamelCase_)						,							UpperCamelCase_						,							UpperCamelCase_						,							**UpperCamelCase_).prev_sample

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

        # 10. Post-processing
			_UpperCamelCase :		str								=    self.decode_latents(latents.float())

			# 11. Convert to PIL
			if output_type == "pil":
				_UpperCamelCase :		int								=    self.numpy_to_pil(UpperCamelCase_)

			if not return_dict:
				return (image,)

			return ImagePipelineOutput(images=UpperCamelCase_)


		def 		A__       (					self						,							__snake_case):
			_UpperCamelCase :		List[str]								=    1 / 0.0_8_3_3_3 * latents
			_UpperCamelCase :		List[str]								=    self.vae(latent_sample=UpperCamelCase_)[0]
			_UpperCamelCase :		List[Any]								=    np.clip(image / 2 + 0.5						,							0						,							1)
			_UpperCamelCase :		Any								=    image.transpose((0, 2, 3, 1))
			return image






		def 		A__       (					self						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case						,							__snake_case = None						,							__snake_case = None						,							):





			if prompt is not None and isinstance(UpperCamelCase_						,							UpperCamelCase_):
				_UpperCamelCase :		Union[str, Any]								=    1
			elif prompt is not None and isinstance(UpperCamelCase_						,							UpperCamelCase_):
				_UpperCamelCase :		int								=    len(UpperCamelCase_)
			else:
				_UpperCamelCase :		Optional[Any]								=    prompt_embeds.shape[0]

			if prompt_embeds is None:
				_UpperCamelCase :		Tuple								=    self.tokenizer(
				    UpperCamelCase_						,							padding='max_length'						,							max_length=self.tokenizer.model_max_length						,							truncation=UpperCamelCase_						,							return_tensors='pt'						,							)
				_UpperCamelCase :		str								=    text_inputs.input_ids
				_UpperCamelCase :		Any								=    self.tokenizer(UpperCamelCase_						,							padding='longest'						,							return_tensors='pt').input_ids

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

			_UpperCamelCase :		Optional[Any]								=    prompt_embeds.shape
			# duplicate text embeddings for each generation per prompt, using mps friendly method
			_UpperCamelCase :		Optional[Any]								=    prompt_embeds.repeat(1						,							UpperCamelCase_)
			_UpperCamelCase :		List[Any]								=    prompt_embeds.reshape(bs_embed * num_images_per_prompt						,							UpperCamelCase_						,							-1)

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

				_UpperCamelCase :		List[Any]								=    text_input_ids.shape[-1]
				_UpperCamelCase :		Optional[int]								=    self.tokenizer(
				    UpperCamelCase_						,							padding='max_length'						,							max_length=UpperCamelCase_						,							truncation=UpperCamelCase_						,							return_tensors='pt'						,							)

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

				_UpperCamelCase :		Tuple								=    self.text_encoder(
				    input_ids=uncond_input.input_ids.int().to(UpperCamelCase_)						,							)
				_UpperCamelCase :		Union[str, Any]								=    uncond_embeddings[0]

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

			return prompt_embeds









'''simple docstring'''






import warnings

from .generation import FlaxGenerationMixin

class 	_lowerCAmelCase     (    __A       ):






		'''simple docstring'''
		# warning at import time
		warnings.warn(
		    'Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will '
		    'be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead.'     ,		__A     ,		)



