
'''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							:				Union[str, Any]					=       getLogger(__name__)


__lowercase							:				Dict					=       4
__lowercase							:				Optional[Any]					=       7

__lowercase							:				Optional[int]					=       {
    '''float16''': torch.floataa,
    '''float32''': torch.floataa,
}




def lowercase_		(				_lowercase			)       ->							int:
					'''simple docstring'''

					if isinstance(_lowercase     ,    torch.Tensor			):
										return image
					elif isinstance(_lowercase     ,    PIL.Image.Image			):
										lowerCamelCase_   : Dict       =						[image]

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

										lowerCamelCase_   : Dict       =						[np.array(i.resize((w, h)			)			)[None, :] for i in image]
										lowerCamelCase_   : List[Any]       =						np.concatenate(_lowercase     ,    axis=0			)
										lowerCamelCase_   : Any       =						np.array(_lowercase			).astype(np.floataa			) / 2_55.0
										lowerCamelCase_   : str       =						image.transpose(0     ,    3     ,    1     ,    2			)
										lowerCamelCase_   : List[Any]       =						2.0 * image - 1.0
										lowerCamelCase_   : Optional[Any]       =						torch.from_numpy(_lowercase			)
					elif isinstance(image[0]     ,    torch.Tensor			):
										lowerCamelCase_   : List[str]       =						torch.cat(_lowercase     ,    dim=0			)

					return image






class 		__lowercase			(					_lowercase      ):

			def __init__(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A = 3_5_0     ,				):
								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     ,				A     ,				A     ,				A = 7_5     ,				A = 9.0     ,				A = 2_0     ,				A = None     ,				A = 1     ,				A = 0.0     ,				A = None     ,				A = None     ,				A = None     ,				A = None     ,				A = "pil"     ,				A = True     ,				A = None     ,				A = 1     ,				):
								self.check_inputs(A     ,				A     ,				A     ,				A )

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

								lowerCamelCase_   : int       =						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.
								lowerCamelCase_   : List[Any]       =						guidance_scale > 1.0

								# 3. Encode input prompt
								lowerCamelCase_   : Dict       =						self._encode_prompt(
								    A     ,				A     ,				A     ,				A     ,				A     ,				prompt_embeds=A     ,				negative_prompt_embeds=A     ,				)

								lowerCamelCase_   : List[Any]       =						ORT_TO_PT_TYPE[str(text_embeddings.dtype )]

								# 4. Preprocess image
								lowerCamelCase_   : Dict       =						preprocess(A )
								lowerCamelCase_   : Any       =						image.cpu()

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

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

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

								# 6. Prepare latent variables
								lowerCamelCase_, lowerCamelCase_   : List[str]       =						image.shape[2:]
								lowerCamelCase_   : Optional[Any]       =						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
								lowerCamelCase_   : List[str]       =						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
								lowerCamelCase_   : Any       =						self.prepare_extra_step_kwargs(A     ,				A )

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

								# 9. Denoising loop
								lowerCamelCase_   : Union[str, Any]       =						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
																		lowerCamelCase_   : Optional[Any]       =						np.concatenate([latents] * 2 ) if do_classifier_free_guidance else latents

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

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

																		# predict the noise residual
																		lowerCamelCase_   : int       =						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:
																							lowerCamelCase_, lowerCamelCase_   : Tuple       =						np.split(A     ,				2 )
																		lowerCamelCase_   : str       =						noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																		# compute the previous noisy sample x_t -> x_t-1
																		lowerCamelCase_   : Dict       =						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
								lowerCamelCase_   : List[Any]       =						self.decode_latents(latents.float() )

								# 11. Convert to PIL
								if output_type == "pil":
													lowerCamelCase_   : List[str]       =						self.numpy_to_pil(A )

								if not return_dict:
													return (image,)

								return ImagePipelineOutput(images=A )

			def     UpperCAmelCase__	(self     ,				A ):
								lowerCamelCase_   : Union[str, Any]       =						1 / 0.0_83_33 * latents
								lowerCamelCase_   : Optional[Any]       =						self.vae(latent_sample=A )[0]
								lowerCamelCase_   : Dict       =						np.clip(image / 2 + 0.5     ,				0     ,				1 )
								lowerCamelCase_   : Union[str, Any]       =						image.transpose((0, 2, 3, 1) )
								return image






			def     UpperCAmelCase__	(self     ,				A     ,				A     ,				A     ,				A     ,				A     ,				A = None     ,				A = None     ,				):
								if prompt is not None and isinstance(A     ,				A ):
													lowerCamelCase_   : Optional[Any]       =						1
								elif prompt is not None and isinstance(A     ,				A ):
													lowerCamelCase_   : Tuple       =						len(A )
								else:
													lowerCamelCase_   : str       =						prompt_embeds.shape[0]

								if prompt_embeds is None:
													lowerCamelCase_   : int       =						self.tokenizer(
													    A     ,				padding='''max_length'''     ,				max_length=self.tokenizer.model_max_length     ,				truncation=A     ,				return_tensors='''pt'''     ,				)
													lowerCamelCase_   : Optional[Any]       =						text_inputs.input_ids
													lowerCamelCase_   : str       =						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 ):
																		lowerCamelCase_   : 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
													lowerCamelCase_   : int       =						self.text_encoder(
													    input_ids=text_input_ids.int().to(A )     ,				)
													lowerCamelCase_   : int       =						prompt_embeds[0]

								lowerCamelCase_, lowerCamelCase_, lowerCamelCase_   : Any       =						prompt_embeds.shape
								# duplicate text embeddings for each generation per prompt, using mps friendly method
								lowerCamelCase_   : Optional[int]       =						prompt_embeds.repeat(1     ,				A )
								lowerCamelCase_   : int       =						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:
													lowerCamelCase_   : List[str]
													if negative_prompt is None:
																		lowerCamelCase_   : Dict       =						[''''''] * 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 ):
																		lowerCamelCase_   : List[Any]       =						[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:
																		lowerCamelCase_   : str       =						negative_prompt

													lowerCamelCase_   : Optional[Any]       =						text_input_ids.shape[-1]
													lowerCamelCase_   : Optional[Any]       =						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

													lowerCamelCase_   : Any       =						self.text_encoder(
													    input_ids=uncond_input.input_ids.int().to(A )     ,				)
													lowerCamelCase_   : List[str]       =						uncond_embeddings[0]

								if do_classifier_free_guidance:
													lowerCamelCase_   : Any       =						uncond_embeddings.shape[1]
													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
													lowerCamelCase_   : Dict       =						uncond_embeddings.repeat(1     ,				A )
													lowerCamelCase_   : Union[str, Any]       =						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
													lowerCamelCase_   : str       =						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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


