'''simple docstring'''

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


lowercase_     		= logging.get_logger(__name__)  # pylint: disable=invalid-name
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase):







		"""simple docstring"""






		@register_to_config
		def __init__(   self		,						__A		,						__A = None		,						__A = None				) ->     Tuple:
								super().__init__()

								_lowerCAmelCase										=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"

														_lowerCAmelCase										=torch.zeros(__A		,						__A				)
								else:
														_lowerCAmelCase										=None

								_lowerCAmelCase										=torch.nn.Parameter(__A				)

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      VQModel
		lowercase   :      CLIPTextModel
		lowercase   :      CLIPTokenizer
		lowercase   :      TransformeraDModel
		lowercase   :      LearnedClassifierFreeSamplingEmbeddings
		lowercase   :      VQDiffusionScheduler






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A		,						) ->     int:
								super().__init__()

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






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Tuple:
								_lowerCAmelCase										=len(__A				) if isinstance(__A		,						__A				) else 1

								# get prompt text embeddings
								_lowerCAmelCase										=self.tokenizer(
								    __A		,						padding='max_length'		,						max_length=self.tokenizer.model_max_length		,						return_tensors='pt'		,						)
								_lowerCAmelCase										=text_inputs.input_ids

								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														_lowerCAmelCase										=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}'''				)
														_lowerCAmelCase										=text_input_ids[:, : self.tokenizer.model_max_length]
								_lowerCAmelCase										=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
								_lowerCAmelCase										=prompt_embeds / prompt_embeds.norm(dim=-1		,						keepdim=__A				)

								# duplicate text embeddings for each generation per prompt
								_lowerCAmelCase										=prompt_embeds.repeat_interleave(__A		,						dim=0				)

								if do_classifier_free_guidance:
														if self.learned_classifier_free_sampling_embeddings.learnable:
																				_lowerCAmelCase										=self.learned_classifier_free_sampling_embeddings.embeddings
																				_lowerCAmelCase										=negative_prompt_embeds.unsqueeze(0				).repeat(__A		,						1		,						1				)
														else:
																				_lowerCAmelCase										=[''] * batch_size

																				_lowerCAmelCase										=text_input_ids.shape[-1]
																				_lowerCAmelCase										=self.tokenizer(
																				    __A		,						padding='max_length'		,						max_length=__A		,						truncation=__A		,						return_tensors='pt'		,						)
																				_lowerCAmelCase										=self.text_encoder(uncond_input.input_ids.to(self.device				)				)[0]
																				# See comment for normalizing text embeddings
																				_lowerCAmelCase										=negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1		,						keepdim=__A				)

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														_lowerCAmelCase										=negative_prompt_embeds.shape[1]
														_lowerCAmelCase										=negative_prompt_embeds.repeat(1		,						__A		,						1				)
														_lowerCAmelCase										=negative_prompt_embeds.view(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										=torch.cat([negative_prompt_embeds, prompt_embeds]				)

								return prompt_embeds






		@torch.no_grad()
		def __call__(   self		,						__A		,						__A = 100		,						__A = 5.0		,						__A = 1.0		,						__A = 1		,						__A = None		,						__A = None		,						__A = "pil"		,						__A = True		,						__A = None		,						__A = 1		,						) ->     Union[ImagePipelineOutput, Tuple]:
								if isinstance(__A		,						__A				):
														_lowerCAmelCase										=1
								elif isinstance(__A		,						__A				):
														_lowerCAmelCase										=len(__A				)
								else:
														raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(__A				)}'''				)

								_lowerCAmelCase										=batch_size * num_images_per_prompt

								_lowerCAmelCase										=guidance_scale > 1.0

								_lowerCAmelCase										=self._encode_prompt(__A		,						__A		,						__A				)

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

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

								_lowerCAmelCase										=(batch_size, self.transformer.num_latent_pixels)
								if latents is None:
														_lowerCAmelCase										=self.transformer.num_vector_embeds - 1
														_lowerCAmelCase										=torch.full(__A		,						__A				).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).'''				)
														_lowerCAmelCase										=latents.to(self.device				)

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

								_lowerCAmelCase										=self.scheduler.timesteps.to(self.device				)

								_lowerCAmelCase										=latents

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

														# predict the un-noised image
														# model_output == `log_p_x_0`
														_lowerCAmelCase										=self.transformer(__A		,						encoder_hidden_states=__A		,						timestep=__A				).sample

														if do_classifier_free_guidance:
																				_lowerCAmelCase						,				_lowerCAmelCase										=model_output.chunk(2				)
																				_lowerCAmelCase										=model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
																				model_output -= torch.logsumexp(__A		,						dim=1		,						keepdim=__A				)

														_lowerCAmelCase										=self.truncate(__A		,						__A				)

														# remove `log(0)`'s (`-inf`s)
														_lowerCAmelCase										=model_output.clamp(-70				)

														# compute the previous noisy sample x_t -> x_t-1
														_lowerCAmelCase										=self.scheduler.step(__A		,						timestep=__A		,						sample=__A		,						generator=__A				).prev_sample

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

								_lowerCAmelCase										=self.vqvae.config.vq_embed_dim
								_lowerCAmelCase										=(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
								_lowerCAmelCase										=self.vqvae.quantize.get_codebook_entry(__A		,						shape=__A				)
								_lowerCAmelCase										=self.vqvae.decode(__A		,						force_not_quantize=__A				).sample

								_lowerCAmelCase										=(image / 2 + 0.5).clamp(0		,						1				)
								_lowerCAmelCase										=image.cpu().permute(0		,						2		,						3		,						1				).numpy()

								if output_type == "pil":
														_lowerCAmelCase										=self.numpy_to_pil(__A				)

								if not return_dict:
														return (image,)

								return ImagePipelineOutput(images=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     torch.FloatTensor:
								_lowerCAmelCase						,				_lowerCAmelCase										=torch.sort(__A		,						1		,						descending=__A				)
								_lowerCAmelCase										=torch.exp(__A				)
								_lowerCAmelCase										=sorted_p_x_0.cumsum(dim=1				) < truncation_rate

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

								_lowerCAmelCase										=keep_mask.gather(1		,						indices.argsort(1				)				)

								_lowerCAmelCase										=log_p_x_0.clone()

								_lowerCAmelCase										=-torch.inf  # -inf = log(0)

								return rv






'''simple docstring'''

import math

import torch
from torch import nn

from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			__lowercase):







		"""simple docstring"""






		@register_to_config
		def __init__(   self		,						__A = 128		,						__A = 256		,						__A = 2_000.0		,						__A = 768		,						__A = 12		,						__A = 12		,						__A = 64		,						__A = 2048		,						__A = 0.1		,						) ->     str:
								super().__init__()

								_lowerCAmelCase										=nn.Sequential(
								    nn.Linear(__A		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						nn.Linear(d_model * 4		,						d_model * 4		,						bias=__A				)		,						nn.SiLU()		,						)

								_lowerCAmelCase										=nn.Embedding(__A		,						__A				)
								_lowerCAmelCase										=False

								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)

								_lowerCAmelCase										=nn.ModuleList()
								for lyr_num in range(__A				):
														# FiLM conditional T5 decoder
														_lowerCAmelCase										=DecoderLayer(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						d_ff=__A		,						dropout_rate=__A				)
														self.decoders.append(__A				)

								_lowerCAmelCase										=TaLayerNorm(__A				)

								_lowerCAmelCase										=nn.Dropout(p=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Any:
								_lowerCAmelCase										=torch.mul(query_input.unsqueeze(-1				)		,						key_input.unsqueeze(-2				)				)
								return mask.unsqueeze(-3				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase						,				_lowerCAmelCase						,				_lowerCAmelCase										=decoder_input_tokens.shape
								assert decoder_noise_time.shape == (batch,)

								# decoder_noise_time is in [0, 1), so rescale to expected timing range.
								_lowerCAmelCase										=get_timestep_embedding(
								    decoder_noise_time * self.config.max_decoder_noise_time		,						embedding_dim=self.config.d_model		,						max_period=self.config.max_decoder_noise_time		,						).to(dtype=self.dtype				)

								_lowerCAmelCase										=self.conditioning_emb(__A				).unsqueeze(1				)

								assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)

								_lowerCAmelCase										=decoder_input_tokens.shape[1]

								# If we want to use relative positions for audio context, we can just offset
								# this sequence by the length of encodings_and_masks.
								_lowerCAmelCase										=torch.broadcast_to(
								    torch.arange(__A		,						device=decoder_input_tokens.device				)		,						(batch, seq_length)		,						)

								_lowerCAmelCase										=self.position_encoding(__A				)

								_lowerCAmelCase										=self.continuous_inputs_projection(__A				)
								inputs += position_encodings
								_lowerCAmelCase										=self.dropout(__A				)

								# decoder: No padding present.
								_lowerCAmelCase										=torch.ones(
								    decoder_input_tokens.shape[:2]		,						device=decoder_input_tokens.device		,						dtype=inputs.dtype				)

								# Translate encoding masks to encoder-decoder masks.
								_lowerCAmelCase										=[(x, self.encoder_decoder_mask(__A		,						__A				)) for x, y in encodings_and_masks]

								# cross attend style: concat encodings
								_lowerCAmelCase										=torch.cat([x[0] for x in encodings_and_encdec_masks]		,						dim=1				)
								_lowerCAmelCase										=torch.cat([x[1] for x in encodings_and_encdec_masks]		,						dim=-1				)

								for lyr in self.decoders:
														_lowerCAmelCase										=lyr(
														    __A		,						conditioning_emb=__A		,						encoder_hidden_states=__A		,						encoder_attention_mask=__A		,						)[0]

								_lowerCAmelCase										=self.decoder_norm(__A				)
								_lowerCAmelCase										=self.post_dropout(__A				)

								_lowerCAmelCase										=self.spec_out(__A				)
								return spec_out

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A		,						__A=1E-6				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.ModuleList()

								# cond self attention: layer 0
								self.layer.append(
								    TaLayerSelfAttentionCond(d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A				)				)

								# cross attention: layer 1
								self.layer.append(
								    TaLayerCrossAttention(
								        d_model=__A		,						d_kv=__A		,						num_heads=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A		,						)				)

								# Film Cond MLP + dropout: last layer
								self.layer.append(
								    TaLayerFFCond(d_model=__A		,						d_ff=__A		,						dropout_rate=__A		,						layer_norm_epsilon=__A				)				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						__A=None		,						) ->     Any:
								_lowerCAmelCase										=self.layer[0](
								    __A		,						conditioning_emb=__A		,						attention_mask=__A		,						)

								if encoder_hidden_states is not None:
														_lowerCAmelCase										=torch.where(encoder_attention_mask > 0		,						0		,						-1E10				).to(
														    encoder_hidden_states.dtype				)

														_lowerCAmelCase										=self.layer[1](
														    __A		,						key_value_states=__A		,						attention_mask=__A		,						)

								# Apply Film Conditional Feed Forward layer
								_lowerCAmelCase										=self.layer[-1](__A		,						__A				)

								return (hidden_states,)

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaLayerNorm(__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     List[Any]:
								# pre_self_attention_layer_norm
								_lowerCAmelCase										=self.layer_norm(__A				)

								if conditioning_emb is not None:
														_lowerCAmelCase										=self.FiLMLayer(__A		,						__A				)

								# Self-attention block
								_lowerCAmelCase										=self.attention(__A				)

								_lowerCAmelCase										=hidden_states + self.dropout(__A				)

								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A		,						__A				) ->     Optional[int]:
								super().__init__()
								_lowerCAmelCase										=Attention(query_dim=__A		,						heads=__A		,						dim_head=__A		,						out_bias=__A		,						scale_qk=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None		,						__A=None		,						) ->     Tuple:
								_lowerCAmelCase										=self.layer_norm(__A				)
								_lowerCAmelCase										=self.attention(
								    __A		,						encoder_hidden_states=__A		,						attention_mask=attention_mask.squeeze(1				)		,						)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return layer_output

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=TaDenseGatedActDense(d_model=__A		,						d_ff=__A		,						dropout_rate=__A				)
								_lowerCAmelCase										=TaFiLMLayer(in_features=d_model * 4		,						out_features=__A				)
								_lowerCAmelCase										=TaLayerNorm(__A		,						eps=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A=None				) ->     List[Any]:
								_lowerCAmelCase										=self.layer_norm(__A				)
								if conditioning_emb is not None:
														_lowerCAmelCase										=self.film(__A		,						__A				)

								_lowerCAmelCase										=self.DenseReluDense(__A				)
								_lowerCAmelCase										=hidden_states + self.dropout(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A		,						__A				) ->     Union[str, Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Linear(__A		,						__A		,						bias=__A				)
								_lowerCAmelCase										=nn.Dropout(__A				)
								_lowerCAmelCase										=NewGELUActivation()






		def 		UpperCamelCase__       (   self		,						__A				) ->     List[Any]:
								_lowerCAmelCase										=self.act(self.wi_a(__A				)				)
								_lowerCAmelCase										=self.wi_a(__A				)
								_lowerCAmelCase										=hidden_gelu * hidden_linear
								_lowerCAmelCase										=self.dropout(__A				)

								_lowerCAmelCase										=self.wo(__A				)
								return hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=1E-6				) ->     int:
								super().__init__()
								_lowerCAmelCase										=nn.Parameter(torch.ones(__A				)				)
								_lowerCAmelCase										=eps






		def 		UpperCamelCase__       (   self		,						__A				) ->     Dict:
								# T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
								# Square Layer Normalization https://arxiv.org/abs/1910.07467 thus variance is calculated
								# w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
								# half-precision inputs is done in fp32

								_lowerCAmelCase										=hidden_states.to(torch.floataa				).pow(2				).mean(-1		,						keepdim=__A				)
								_lowerCAmelCase										=hidden_states * torch.rsqrt(variance + self.variance_epsilon				)

								# convert into half-precision if necessary
								if self.weight.dtype in [torch.floataa, torch.bfloataa]:
														_lowerCAmelCase										=hidden_states.to(self.weight.dtype				)

								return self.weight * hidden_states

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self		,						__A				) ->     torch.Tensor:
								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi				) * (input + 0.044_715 * torch.pow(__A		,						3.0				))				))

class   SCREAMING_SNAKE_CASE			(						nn.Module):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A				) ->     Optional[Any]:
								super().__init__()
								_lowerCAmelCase										=nn.Linear(__A		,						out_features * 2		,						bias=__A				)






		def 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     Optional[Any]:
								_lowerCAmelCase										=self.scale_bias(__A				)
								_lowerCAmelCase						,				_lowerCAmelCase										=torch.chunk(__A		,						2		,						-1				)
								_lowerCAmelCase										=x * (1 + scale) + shift
								return x





