




'''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


a  :				Union[str, Any]									=  logging.get_logger(__name__)  # pylint: disable=invalid-name






class 						a				(   _lowerCamelCase   ,     _lowerCamelCase     ):


				@register_to_config
				def __init__(		self    :					Union[str, Any]       ,				lowercase_    :					bool       ,				lowercase_    :					Optional[int] = None       ,				lowercase_    :					Optional[int] = None					):
									super().__init__()

									snake_case_									=				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"

														snake_case_									=				torch.zeros(lowercase_       ,				lowercase_					)
									else:
														snake_case_									=				None

									snake_case_									=				torch.nn.Parameter(lowercase_					)







class 						a				(   _lowerCamelCase     ):
				snake_case_      =		42
				snake_case_      =		42
				snake_case_      =		42
				snake_case_      =		42
				snake_case_      =		42
				snake_case_      =		42


				def __init__(		self    :					Any       ,				lowercase_    :					VQModel       ,				lowercase_    :					CLIPTextModel       ,				lowercase_    :					CLIPTokenizer       ,				lowercase_    :					TransformeraDModel       ,				lowercase_    :					VQDiffusionScheduler       ,				lowercase_    :					LearnedClassifierFreeSamplingEmbeddings       ,				):
									super().__init__()

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


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					Dict       ,				lowercase_    :					Any       ,				lowercase_    :					Any					):
									snake_case_									=				len(lowercase_					) if isinstance(lowercase_       ,				lowercase_					) else 1

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

									if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
														snake_case_									=				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}"					)
														snake_case_									=				text_input_ids[:, : self.tokenizer.model_max_length]
									snake_case_									=				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
									snake_case_									=				prompt_embeds / prompt_embeds.norm(dim=-1       ,				keepdim=lowercase_					)

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

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

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

														# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
														snake_case_									=				negative_prompt_embeds.shape[1]
														snake_case_									=				negative_prompt_embeds.repeat(1       ,				lowercase_       ,				1					)
														snake_case_									=				negative_prompt_embeds.view(batch_size * num_images_per_prompt       ,				lowercase_       ,				-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
														snake_case_									=				torch.cat([negative_prompt_embeds, prompt_embeds]					)

									return prompt_embeds


				@torch.no_grad()
				def __call__(		self    :					Union[str, Any]       ,				lowercase_    :					Union[str, List[str]]       ,				lowercase_    :					int = 100       ,				lowercase_    :					float = 5.0       ,				lowercase_    :					float = 1.0       ,				lowercase_    :					int = 1       ,				lowercase_    :					Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,				lowercase_    :					Optional[torch.FloatTensor] = None       ,				lowercase_    :					Optional[str] = "pil"       ,				lowercase_    :					bool = True       ,				lowercase_    :					Optional[Callable[[int, int, torch.FloatTensor], None]] = None       ,				lowercase_    :					int = 1       ,				):
									if isinstance(lowercase_       ,				lowercase_					):
														snake_case_									=				1
									elif isinstance(lowercase_       ,				lowercase_					):
														snake_case_									=				len(lowercase_					)
									else:
														raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowercase_					)}"					)

									snake_case_									=				batch_size * num_images_per_prompt

									snake_case_									=				guidance_scale > 1.0

									snake_case_									=				self._encode_prompt(lowercase_       ,				lowercase_       ,				lowercase_					)

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

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

									snake_case_									=				(batch_size, self.transformer.num_latent_pixels)
									if latents is None:
														snake_case_									=				self.transformer.num_vector_embeds - 1
														snake_case_									=				torch.full(lowercase_       ,				lowercase_					).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)."					)
														snake_case_									=				latents.to(self.device					)

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

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

									snake_case_									=				latents

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

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

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

														snake_case_									=				self.truncate(lowercase_       ,				lowercase_					)

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

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

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

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

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

									if output_type == "pil":
														snake_case_									=				self.numpy_to_pil(lowercase_					)

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=lowercase_					)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					torch.FloatTensor       ,				lowercase_    :					float					):
									snake_case_			,snake_case_									=				torch.sort(lowercase_       ,				1       ,				descending=lowercase_					)
									snake_case_									=				torch.exp(lowercase_					)
									snake_case_									=				sorted_p_x_0.cumsum(dim=1					) < truncation_rate

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

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

									snake_case_									=				log_p_x_0.clone()

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

									return rv






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


a_   :int        =      {
    "configuration_longt5": ["LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP", "LongT5Config", "LongT5OnnxConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :List[str]        =      [
     "LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LongT5EncoderModel",
     "LongT5ForConditionalGeneration",
     "LongT5Model",
     "LongT5PreTrainedModel",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_   :int        =      [
     "FlaxLongT5ForConditionalGeneration",
     "FlaxLongT5Model",
     "FlaxLongT5PreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_longta import LONGT5_PRETRAINED_CONFIG_ARCHIVE_MAP, LongTaConfig, LongTaOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_longta import (
      LONGT5_PRETRAINED_MODEL_ARCHIVE_LIST,
      LongTaEncoderModel,
      LongTaForConditionalGeneration,
      LongTaModel,
      LongTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_longta import (
      FlaxLongTaForConditionalGeneration,
      FlaxLongTaModel,
      FlaxLongTaPreTrainedModel,
  )


else:
 import sys

 a_   :Optional[int]        =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






