




import warnings
from dataclasses import dataclass, field
from typing import Optional, Tuple

from .training_args import TrainingArguments
from .utils import cached_property, is_tf_available, logging, requires_backends


SCREAMING_SNAKE_CASE_						:					Optional[Any]			   =       logging.get_logger(__name__)

if is_tf_available():
 import tensorflow as tf







@dataclass
class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     __UpperCAmelCase	: int						=    """tf"""
     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Zone of TPU"""}       ,	)

     __UpperCAmelCase	: Optional[str]						=    field(
         default=lowercase__       ,	metadata={"""help""": """Name of Cloud TPU-enabled project"""}       ,	)

     __UpperCAmelCase	: float						=    field(
         default=1.0       ,	metadata={"""help""": """Power for the Polynomial decay LR scheduler."""}       ,	)

     __UpperCAmelCase	: bool						=    field(default=lowercase__       ,	metadata={"""help""": """Whether to activate the XLA compilation or not"""} )

     @cached_property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			Tuple["tf.distribute.Strategy", int]:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         logger.info("""Tensorflow: setting up strategy"""    )

         a__      =       tf.config.list_physical_devices("""GPU"""    )

         # Set to float16 at first
         if self.fpaa:
             tf.keras.mixed_precision.set_global_policy("""mixed_float16"""    )

         if self.no_cuda:
             a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
         else:
             try:
                 if self.tpu_name:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver(
                         self.tpu_name						,				zone=self.tpu_zone						,				project=self.gcp_project    )
                 else:
                     a__      =       tf.distribute.cluster_resolver.TPUClusterResolver()
             except ValueError:
                 if self.tpu_name:
                     raise RuntimeError(f"""Couldn't connect to TPU {self.tpu_name}!"""    )
                 else:
                     a__      =       None

             if tpu:
                 # Set to bfloat16 in case of TPU
                 if self.fpaa:
                     tf.keras.mixed_precision.set_global_policy("""mixed_bfloat16"""    )

                 tf.config.experimental_connect_to_cluster(A    )
                 tf.tpu.experimental.initialize_tpu_system(A    )

                 a__      =       tf.distribute.TPUStrategy(A    )

             elif len(A    ) == 0:
                 a__      =       tf.distribute.OneDeviceStrategy(device="""/cpu:0"""    )
             elif len(A    ) == 1:
                 a__      =       tf.distribute.OneDeviceStrategy(device="""/gpu:0"""    )
             elif len(A    ) > 1:
                 # If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
                 a__      =       tf.distribute.MirroredStrategy()
             else:
                 raise ValueError("""Cannot find the proper strategy, please check your environment properties."""    )

         return strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Tuple    )			->			"tf.distribute.Strategy":






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         return self._setup_strategy.num_replicas_in_sync

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Dict    )			->			List[Any]:






         """simple docstring"""
         return False  # TF Logging is handled by Keras not the Trainer

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       Optional[Any]    )			->			int:






         """simple docstring"""
         if self.per_gpu_train_batch_size:
             logger.warning(
                 """Using deprecated `--per_gpu_train_batch_size` argument which will be removed in a future """
                 """version. Using `--per_device_train_batch_size` is preferred."""    )
         a__      =       self.per_gpu_train_batch_size or self.per_device_train_batch_size
         return per_device_batch_size * self.n_replicas

     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]    )			->			int:






         """simple docstring"""
         if self.per_gpu_eval_batch_size:
             logger.warning(
                 """Using deprecated `--per_gpu_eval_batch_size` argument which will be removed in a future """
                 """version. Using `--per_device_eval_batch_size` is preferred."""    )
         a__      =       self.per_gpu_eval_batch_size or self.per_device_eval_batch_size
         return per_device_batch_size * self.n_replicas






     @property
     def 				__SCREAMING_SNAKE_CASE		(      self :       str    )			->			int:






         """simple docstring"""
         requires_backends(self						,				["""tf"""]    )
         warnings.warn(
             """The n_gpu argument is deprecated and will be removed in a future version, use n_replicas instead."""						,				A						,				)
         return self._setup_strategy.num_replicas_in_sync












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


SCREAMING_SNAKE_CASE_						:					Optional[int]			   =       getLogger(__name__)


SCREAMING_SNAKE_CASE_						:					Union[str, Any]			   =       4
SCREAMING_SNAKE_CASE_						:					List[str]			   =       7

SCREAMING_SNAKE_CASE_						:					List[str]			   =       {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}
def        __lowerCAmelCase (UpperCAmelCase_		):
    if isinstance(UpperCAmelCase_   ,    torch.Tensor		):
        return image
    elif isinstance(UpperCAmelCase_   ,    PIL.Image.Image		):
        a__      =       [image]

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

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

    return image







class 		__UpperCAmelCase(		lowercase__ ):





     """simple docstring"""



     def __init__(      self :       int						,				A :       OnnxRuntimeModel						,				A :       OnnxRuntimeModel						,				A :       Any						,				A :       OnnxRuntimeModel						,				A :       DDPMScheduler						,				A :       Any						,				A :       int = 3_50						,				)			->			List[Any]:






         """simple docstring"""
         super().__init__(
             vae=A						,				text_encoder=A						,				tokenizer=A						,				unet=A						,				low_res_scheduler=A						,				scheduler=A						,				safety_checker=A						,				feature_extractor=A						,				watermarker=A						,				max_noise_level=A						,				)

     def __call__(      self :       Optional[int]						,				A :       Union[str, List[str]]						,				A :       Union[torch.FloatTensor, PIL.Image.Image, List[PIL.Image.Image]]						,				A :       int = 75						,				A :       float = 9.0						,				A :       int = 20						,				A :       Optional[Union[str, List[str]]] = None						,				A :       Optional[int] = 1						,				A :       float = 0.0						,				A :       Optional[Union[torch.Generator, List[torch.Generator]]] = None						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[np.ndarray] = None						,				A :       Optional[str] = "pil"						,				A :       bool = True						,				A :       Optional[Callable[[int, int, torch.FloatTensor], None]] = None						,				A :       Optional[int] = 1						,				)			->			Tuple:






         """simple docstring"""
         self.check_inputs(A						,				A						,				A						,				A    )

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

         a__      =       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__      =       guidance_scale > 1.0

         # 3. Encode input prompt
         a__      =       self._encode_prompt(
             A						,				A						,				A						,				A						,				A						,				prompt_embeds=A						,				negative_prompt_embeds=A						,				)

         a__      =       ORT_TO_PT_TYPE[str(text_embeddings.dtype    )]

         # 4. Preprocess image
         a__      =       preprocess(A    )
         a__      =       image.cpu()

         # 5. set timesteps
         self.scheduler.set_timesteps(A						,				device=A    )
         a__      =       self.scheduler.timesteps

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

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

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

         # 7. Check that sizes of image and latents match
         a__      =       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__      =       self.prepare_extra_step_kwargs(A						,				A    )

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

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

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

                 # timestep to tensor
                 a__      =       np.array([t]						,				dtype=A    )

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

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

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

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

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

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

         if not return_dict:
             return (image,)

         return ImagePipelineOutput(images=A    )

     def 				__SCREAMING_SNAKE_CASE		(      self :       List[Any]						,				A :       Dict    )			->			List[str]:






         """simple docstring"""
         a__      =       1 / 0.0_8_3_3_3 * latents
         a__      =       self.vae(latent_sample=A    )[0]
         a__      =       np.clip(image / 2 + 0.5						,				0						,				1    )
         a__      =       image.transpose((0, 2, 3, 1)    )
         return image






     def 				__SCREAMING_SNAKE_CASE		(      self :       List[str]						,				A :       Union[str, List[str]]						,				A :       str						,				A :       Optional[int]						,				A :       bool						,				A :       Optional[str]						,				A :       Optional[torch.FloatTensor] = None						,				A :       Optional[torch.FloatTensor] = None						,				)			->			Tuple:






         """simple docstring"""
         if prompt is not None and isinstance(A						,				A    ):
             a__      =       1
         elif prompt is not None and isinstance(A						,				A    ):
             a__      =       len(A    )
         else:
             a__      =       prompt_embeds.shape[0]

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

             if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(
                 A						,				A    ):
                 a__      =       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__      =       self.text_encoder(
                 input_ids=text_input_ids.int().to(A    )						,				)
             a__      =       prompt_embeds[0]

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

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

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

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

             a__      =       self.text_encoder(
                 input_ids=uncond_input.input_ids.int().to(A    )						,				)
             a__      =       uncond_embeddings[0]

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

         return prompt_embeds






