




from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase_		:					Optional[Any]          =  logging.get_logger(__name__)  # pylint: disable=invalid-name




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):

						@register_to_config
						def __init__(		self :List[str]     ,			__snake_case :bool     ,			__snake_case :Optional[int] = None     ,			__snake_case :Optional[int] = None			):
										'''simple docstring'''

										super().__init__()

										__magic_name__						:						int										=learnable

										if self.learnable:
														assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
														assert length is not None, "learnable=True requires `length` to be set"

														__magic_name__						:						Dict										=torch.zeros(__snake_case     ,			__snake_case			)
										else:
														__magic_name__						:						Tuple										=None

										__magic_name__						:						int										=torch.nn.Parameter(__snake_case			)




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       42
						UpperCamelCase      =       42
						UpperCamelCase      =       42
						UpperCamelCase      =       42
						UpperCamelCase      =       42
						UpperCamelCase      =       42

						def __init__(		self :Dict     ,			__snake_case :VQModel     ,			__snake_case :CLIPTextModel     ,			__snake_case :CLIPTokenizer     ,			__snake_case :TransformeraDModel     ,			__snake_case :VQDiffusionScheduler     ,			__snake_case :LearnedClassifierFreeSamplingEmbeddings     ,			):
										'''simple docstring'''

										super().__init__()

										self.register_modules(
										    vqvae=__snake_case     ,			transformer=__snake_case     ,			text_encoder=__snake_case     ,			tokenizer=__snake_case     ,			scheduler=__snake_case     ,			learned_classifier_free_sampling_embeddings=__snake_case     ,			)

						def      A__    (		self :Optional[int]     ,			__snake_case :Optional[int]     ,			__snake_case :Optional[Any]     ,			__snake_case :Dict			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=len(__snake_case			) if isinstance(__snake_case     ,			__snake_case			) else 1

										# get prompt text embeddings
										__magic_name__						:						str										=self.tokenizer(
										    __snake_case     ,			padding="""max_length"""     ,			max_length=self.tokenizer.model_max_length     ,			return_tensors="""pt"""     ,			)
										__magic_name__						:						str										=text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														__magic_name__						:						int										=self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]			)
														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}"			)
														__magic_name__						:						List[Any]										=text_input_ids[:, : self.tokenizer.model_max_length]
										__magic_name__						:						Union[str, Any]										=self.text_encoder(text_input_ids.to(self.device			)			)[0]

										# NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
										# While CLIP does normalize the pooled output of the text transformer when combining
										# the image and text embeddings, CLIP does not directly normalize the last hidden state.
										#
										# CLIP normalizing the pooled output.
										# https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
										__magic_name__						:						Optional[Any]										=prompt_embeds / prompt_embeds.norm(dim=-1     ,			keepdim=__snake_case			)

										# duplicate text embeddings for each generation per prompt
										__magic_name__						:						Any										=prompt_embeds.repeat_interleave(__snake_case     ,			dim=0			)

										if do_classifier_free_guidance:
														if self.learned_classifier_free_sampling_embeddings.learnable:
																		__magic_name__						:						Union[str, Any]										=self.learned_classifier_free_sampling_embeddings.embeddings
																		__magic_name__						:						Optional[Any]										=negative_prompt_embeds.unsqueeze(0			).repeat(__snake_case     ,			1     ,			1			)
														else:
																		__magic_name__						:						Any										=[""""""] * batch_size

																		__magic_name__						:						List[str]										=text_input_ids.shape[-1]
																		__magic_name__						:						Tuple										=self.tokenizer(
																		    __snake_case     ,			padding="""max_length"""     ,			max_length=__snake_case     ,			truncation=__snake_case     ,			return_tensors="""pt"""     ,			)
																		__magic_name__						:						List[Any]										=self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]
																		# See comment for normalizing text embeddings
																		__magic_name__						:						Any										=negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1     ,			keepdim=__snake_case			)

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														__magic_name__						:						Any										=negative_prompt_embeds.shape[1]
														__magic_name__						:						str										=negative_prompt_embeds.repeat(1     ,			__snake_case     ,			1			)
														__magic_name__						:						Any										=negative_prompt_embeds.view(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
														__magic_name__						:						Optional[int]										=torch.cat([negative_prompt_embeds, prompt_embeds]			)

										return prompt_embeds

						@torch.no_grad()
						def __call__(		self :Optional[Any]     ,			__snake_case :Union[str, List[str]]     ,			__snake_case :int = 1_00     ,			__snake_case :float = 5.0     ,			__snake_case :float = 1.0     ,			__snake_case :int = 1     ,			__snake_case :Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,			__snake_case :Optional[torch.FloatTensor] = None     ,			__snake_case :Optional[str] = "pil"     ,			__snake_case :bool = True     ,			__snake_case :Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,			__snake_case :int = 1     ,			):
										'''simple docstring'''

										if isinstance(__snake_case     ,			__snake_case			):
														__magic_name__						:						str										=1
										elif isinstance(__snake_case     ,			__snake_case			):
														__magic_name__						:						List[str]										=len(__snake_case			)
										else:
														raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(__snake_case			)}"			)

										__magic_name__						:						List[str]										=batch_size * num_images_per_prompt

										__magic_name__						:						Dict										=guidance_scale > 1.0

										__magic_name__						:						Union[str, Any]										=self._encode_prompt(__snake_case     ,			__snake_case     ,			__snake_case			)

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(__snake_case     ,			__snake_case			) or callback_steps <= 0)
										):
														raise ValueError(
														    f"`callback_steps` has to be a positive integer but is {callback_steps} of type"
														    f" {type(__snake_case			)}."			)

										# get the initial completely masked latents unless the user supplied it

										__magic_name__						:						List[Any]										=(batch_size, self.transformer.num_latent_pixels)
										if latents is None:
														__magic_name__						:						List[Any]										=self.transformer.num_vector_embeds - 1
														__magic_name__						:						Union[str, Any]										=torch.full(__snake_case     ,			__snake_case			).to(self.device			)
										else:
														if latents.shape != latents_shape:
																		raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"			)
														if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
																		raise ValueError(
																		    """Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"""
																		    f" {self.transformer.num_vector_embeds - 1} (inclusive)."			)
														__magic_name__						:						Dict										=latents.to(self.device			)

										# set timesteps
										self.scheduler.set_timesteps(__snake_case     ,			device=self.device			)

										__magic_name__						:						Optional[Any]										=self.scheduler.timesteps.to(self.device			)

										__magic_name__						:						List[Any]										=latents

										for i, t in enumerate(self.progress_bar(__snake_case			)			):
														# expand the sample if we are doing classifier free guidance
														__magic_name__						:						Dict										=torch.cat([sample] * 2			) if do_classifier_free_guidance else sample

														# predict the un-noised image
														# model_output == `log_p_x_0`
														__magic_name__						:						List[str]										=self.transformer(__snake_case     ,			encoder_hidden_states=__snake_case     ,			timestep=__snake_case			).sample

														if do_classifier_free_guidance:
																		__magic_name__					,			__magic_name__						:						List[str]										=model_output.chunk(2			)
																		__magic_name__						:						List[Any]										=model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																		model_output -= torch.logsumexp(__snake_case     ,			dim=1     ,			keepdim=__snake_case			)

														__magic_name__						:						Any										=self.truncate(__snake_case     ,			__snake_case			)

														# remove `log(0)`'s (`-inf`s)
														__magic_name__						:						Union[str, Any]										=model_output.clamp(-70			)

														# compute the previous noisy sample x_t -> x_t-1
														__magic_name__						:						Union[str, Any]										=self.scheduler.step(__snake_case     ,			timestep=__snake_case     ,			sample=__snake_case     ,			generator=__snake_case			).prev_sample

														# call the callback, if provided
														if callback is not None and i % callback_steps == 0:
																		callback(__snake_case     ,			__snake_case     ,			__snake_case			)

										__magic_name__						:						Tuple										=self.vqvae.config.vq_embed_dim
										__magic_name__						:						str										=(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
										__magic_name__						:						Optional[int]										=self.vqvae.quantize.get_codebook_entry(__snake_case     ,			shape=__snake_case			)
										__magic_name__						:						Any										=self.vqvae.decode(__snake_case     ,			force_not_quantize=__snake_case			).sample

										__magic_name__						:						Any										=(image / 2 + 0.5).clamp(0     ,			1			)
										__magic_name__						:						Optional[Any]										=image.cpu().permute(0     ,			2     ,			3     ,			1			).numpy()

										if output_type == "pil":
														__magic_name__						:						Union[str, Any]										=self.numpy_to_pil(__snake_case			)

										if not return_dict:
														return (image,)

										return ImagePipelineOutput(images=__snake_case			)


						def      A__    (		self :List[str]     ,			__snake_case :torch.FloatTensor     ,			__snake_case :float			):
										'''simple docstring'''

										__magic_name__					,			__magic_name__						:						int										=torch.sort(__snake_case     ,			1     ,			descending=__snake_case			)
										__magic_name__						:						Any										=torch.exp(__snake_case			)
										__magic_name__						:						Dict										=sorted_p_x_0.cumsum(dim=1			) < truncation_rate

										# Ensure that at least the largest probability is not zeroed out
										__magic_name__						:						Dict										=torch.full_like(keep_mask[:, 0:1, :]     ,			__snake_case			)
										__magic_name__						:						str										=torch.cat((all_true, keep_mask)     ,			dim=1			)
										__magic_name__						:						Tuple										=keep_mask[:, :-1, :]

										__magic_name__						:						Optional[int]										=keep_mask.gather(1     ,			indices.argsort(1			)			)

										__magic_name__						:						Optional[int]										=log_p_x_0.clone()

										__magic_name__						:						Union[str, Any]										=-torch.inf  # -inf = log(0)

										return rv










import math

import tensorflow as tf
from packaging import version




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						List[str]										=0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 )						,  x.dtype ) ))

				return x * cdf




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						str										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Optional[Any]										=tf.cast(math.pi						,  x.dtype )
				__magic_name__						:						int										=tf.cast(0.0_4_4_7_1_5						,  x.dtype )
				__magic_name__						:						Tuple										=0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(lowerCamelCase						,  3 )) ))

				return x * cdf




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Any										=tf.convert_to_tensor(lowerCamelCase )

				return x * tf.tanh(tf.math.softplus(lowerCamelCase ) )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						Optional[Any]										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Union[str, Any]										=tf.cast(0.0_4_4_7_1_5						,  x.dtype )
				__magic_name__						:						Tuple										=tf.cast(0.7_9_7_8_8_4_5_6_0_8						,  x.dtype )

				return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))




def 		lowerCAmelCase_ (						lowerCamelCase ):
				__magic_name__						:						List[str]										=tf.convert_to_tensor(lowerCamelCase )
				__magic_name__						:						Dict										=tf.cast(1.7_0_2						,  x.dtype )
				return x * tf.math.sigmoid(coeff * x )




def 		lowerCAmelCase_ (						lowerCamelCase ):
				return tf.clip_by_value(_gelu(lowerCamelCase )						,  -10						,  10 )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=-1 ):
				__magic_name__					,			__magic_name__						:						List[Any]										=tf.split(lowerCamelCase						,  2						,  axis=lowerCamelCase )
				return a * tf.math.sigmoid(lowerCamelCase )


if version.parse(tf.version.VERSION) >= version.parse("2.4"):




							def 		lowerCAmelCase_ (						lowerCamelCase ):
											return tf.keras.activations.gelu(lowerCamelCase						,  approximate=lowerCamelCase )

							UpperCAmelCase_		:					List[str]          =  tf.keras.activations.gelu



							UpperCAmelCase_		:					Dict          =  approximate_gelu_wrap
else:
							UpperCAmelCase_		:					Dict          =  _gelu
							UpperCAmelCase_		:					str          =  _gelu_new


UpperCAmelCase_		:					Any          =  {
    "gelu": gelu,
    "gelu_10": gelu_aa,
    "gelu_fast": gelu_fast,
    "gelu_new": gelu_new,
    "glu": glu,
    "mish": mish,
    "quick_gelu": quick_gelu,
    "relu": tf.keras.activations.relu,
    "sigmoid": tf.keras.activations.sigmoid,
    "silu": tf.keras.activations.swish,
    "swish": tf.keras.activations.swish,
    "tanh": tf.keras.activations.tanh,
}




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}" )




