
from typing import List, Union

import numpy as np

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
     from PIL import Image

     from ..image_utils import load_image

if is_torch_available():
     import torch

     from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING

_UpperCAmelCase      :				Union[str, Any]          = logging.get_logger(__name__)


@add_end_docstrings(_SCREAMING_SNAKE_CASE     )
class        lowercase      (	_SCREAMING_SNAKE_CASE     ):



  def __init__(				self       ,   *A_       ,   **A_ )						->      Tuple:
    """simple docstring"""
    super().__init__(*A_       ,   **A_ )
    requires_backends(self       ,   'vision' )
    self.check_model_type(A_ )



  def __call__(				self       ,   A_       ,   **A_ )						->      Dict:
    """simple docstring"""
    return super().__call__(A_       ,   **A_ )



  def __UpperCamelCase				(				self       ,   **A_ )						->      Dict:
    """simple docstring"""
    return {}, {}, {}



  def __UpperCamelCase				(				self       ,   A_ )						->      List[Any]:
    """simple docstring"""
    UpperCamelCase						=  load_image(A_ )
    UpperCamelCase						=  image.size
    UpperCamelCase						=  self.image_processor(images=A_       ,   return_tensors=self.framework )
    return model_inputs



  def __UpperCamelCase				(				self       ,   A_ )						->      str:
    """simple docstring"""
    UpperCamelCase						=  self.model(**A_ )
    return model_outputs



  def __UpperCamelCase				(				self       ,   A_ )						->      Dict:
    """simple docstring"""
    UpperCamelCase						=  model_outputs.predicted_depth
    UpperCamelCase						=  torch.nn.functional.interpolate(
        predicted_depth.unsqueeze(1 )       ,   size=self.image_size[::-1]       ,   mode='bicubic'       ,   align_corners=A_ )
    UpperCamelCase						=  prediction.squeeze().cpu().numpy()
    UpperCamelCase						=  (output * 255 / np.max(A_ )).astype('uint8' )
    UpperCamelCase						=  Image.fromarray(A_ )
    UpperCamelCase						=  {}
    UpperCamelCase						=  predicted_depth
    UpperCamelCase						=  depth
    return output_dict


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      :				str          = logging.get_logger(__name__)  # pylint: disable=invalid-name


class        lowercase      (	_SCREAMING_SNAKE_CASE						,     _SCREAMING_SNAKE_CASE     ):



		@register_to_config
		def __init__(				self       ,   A_       ,   A_ = None       ,   A_ = None )						->      Any:
				"""simple docstring"""
				super().__init__()

				UpperCamelCase						=  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"

						UpperCamelCase						=  torch.zeros(A_       ,   A_ )
				else:
						UpperCamelCase						=  None

				UpperCamelCase						=  torch.nn.Parameter(A_ )






class        lowercase      (	_SCREAMING_SNAKE_CASE     ):
		__lowercase : VQModel
		__lowercase : CLIPTextModel
		__lowercase : CLIPTokenizer
		__lowercase : TransformeraDModel
		__lowercase : LearnedClassifierFreeSamplingEmbeddings
		__lowercase : VQDiffusionScheduler



		def __init__(				self       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   A_       ,   )						->      Optional[Any]:
				"""simple docstring"""
				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_ )						->      Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase						=  len(A_ ) if isinstance(A_       ,   A_ ) else 1

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

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

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

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

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

						# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
						UpperCamelCase						=  negative_prompt_embeds.shape[1]
						UpperCamelCase						=  negative_prompt_embeds.repeat(1       ,   A_       ,   1 )
						UpperCamelCase						=  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
						UpperCamelCase						=  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]:
				"""simple docstring"""
				if isinstance(A_       ,   A_ ):
						UpperCamelCase						=  1
				elif isinstance(A_       ,   A_ ):
						UpperCamelCase						=  len(A_ )
				else:
						raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(A_ )}''' )

				UpperCamelCase						=  batch_size * num_images_per_prompt

				UpperCamelCase						=  guidance_scale > 1.0

				UpperCamelCase						=  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

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

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

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

				UpperCamelCase						=  latents

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

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

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

						UpperCamelCase						=  self.truncate(A_       ,   A_ )

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

						# compute the previous noisy sample x_t -> x_t-1
						UpperCamelCase						=  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_ )

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

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

				if output_type == "pil":
						UpperCamelCase						=  self.numpy_to_pil(A_ )

				if not return_dict:
						return (image,)

				return ImagePipelineOutput(images=A_ )



		def __UpperCamelCase				(				self       ,   A_       ,   A_ )						->      torch.FloatTensor:
				"""simple docstring"""
				UpperCamelCase			,						UpperCamelCase						=  torch.sort(A_       ,   1       ,   descending=A_ )
				UpperCamelCase						=  torch.exp(A_ )
				UpperCamelCase						=  sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

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

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

				UpperCamelCase						=  log_p_x_0.clone()

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

				return rv
