


'''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


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






class 	lowerCAmelCase__			(			UpperCAmelCase__    ,       UpperCAmelCase__	):




			@register_to_config
			def __init__(		self  :     str	,  lowerCamelCase__  :     bool	,  lowerCamelCase__  :     Optional[int] = None	,  lowerCamelCase__  :     Optional[int] = None )   ->Optional[int]:



					'''simple docstring'''







					super().__init__()

					_UpperCAmelCase		:       Optional[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"

							_UpperCAmelCase		:       str								=      torch.zeros(lowerCamelCase__	,  lowerCamelCase__ )
					else:
							_UpperCAmelCase		:       str								=      None

					_UpperCAmelCase		:       str								=      torch.nn.Parameter(lowerCamelCase__ )






class 	lowerCAmelCase__			(			UpperCAmelCase__	):
			lowerCAmelCase :	VQModel
			lowerCAmelCase :	CLIPTextModel
			lowerCAmelCase :	CLIPTokenizer
			lowerCAmelCase :	TransformeraDModel
			lowerCAmelCase :	LearnedClassifierFreeSamplingEmbeddings
			lowerCAmelCase :	VQDiffusionScheduler




			def __init__(		self  :     int	,  lowerCamelCase__  :     VQModel	,  lowerCamelCase__  :     CLIPTextModel	,  lowerCamelCase__  :     CLIPTokenizer	,  lowerCamelCase__  :     TransformeraDModel	,  lowerCamelCase__  :     VQDiffusionScheduler	,  lowerCamelCase__  :     LearnedClassifierFreeSamplingEmbeddings	,  )   ->Dict:



					'''simple docstring'''







					super().__init__()

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




			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     Union[str, Any]	,  lowerCamelCase__  :     Optional[int]	,  lowerCamelCase__  :     List[Any] )   ->int:



					'''simple docstring'''







					_UpperCAmelCase		:       Optional[Any]								=      len(lowerCamelCase__ ) if isinstance(lowerCamelCase__	,  lowerCamelCase__ ) else 1

					# get prompt text embeddings
					_UpperCAmelCase		:       Union[str, Any]								=      self.tokenizer(
					    lowerCamelCase__	,  padding="max_length"	,  max_length=self.tokenizer.model_max_length	,  return_tensors="pt"	,  )
					_UpperCAmelCase		:       Union[str, Any]								=      text_inputs.input_ids

					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
							_UpperCAmelCase		:       Tuple								=      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		:       List[Any]								=      text_input_ids[:, : self.tokenizer.model_max_length]
					_UpperCAmelCase		:       int								=      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		:       Union[str, Any]								=      prompt_embeds / prompt_embeds.norm(dim=-1	,  keepdim=lowerCamelCase__ )

					# duplicate text embeddings for each generation per prompt
					_UpperCAmelCase		:       Tuple								=      prompt_embeds.repeat_interleave(lowerCamelCase__	,  dim=0 )

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

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

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

					return prompt_embeds




			@torch.no_grad()
			def __call__(		self  :     Tuple	,  lowerCamelCase__  :     Union[str, List[str]]	,  lowerCamelCase__  :     int = 1_00	,  lowerCamelCase__  :     float = 5.0	,  lowerCamelCase__  :     float = 1.0	,  lowerCamelCase__  :     int = 1	,  lowerCamelCase__  :     Optional[Union[torch.Generator, List[torch.Generator]]] = None	,  lowerCamelCase__  :     Optional[torch.FloatTensor] = None	,  lowerCamelCase__  :     Optional[str] = "pil"	,  lowerCamelCase__  :     bool = True	,  lowerCamelCase__  :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None	,  lowerCamelCase__  :     int = 1	,  )   ->Union[ImagePipelineOutput, Tuple]:



					'''simple docstring'''







					if isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Tuple								=      1
					elif isinstance(lowerCamelCase__	,  lowerCamelCase__ ):
							_UpperCAmelCase		:       Optional[Any]								=      len(lowerCamelCase__ )
					else:
							raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCamelCase__ )}""" )

					_UpperCAmelCase		:       List[str]								=      batch_size * num_images_per_prompt

					_UpperCAmelCase		:       Optional[Any]								=      guidance_scale > 1.0

					_UpperCAmelCase		:       Union[str, Any]								=      self._encode_prompt(lowerCamelCase__	,  lowerCamelCase__	,  lowerCamelCase__ )

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

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

					_UpperCAmelCase		:       int								=      (batch_size, self.transformer.num_latent_pixels)
					if latents is None:
							_UpperCAmelCase		:       Optional[int]								=      self.transformer.num_vector_embeds - 1
							_UpperCAmelCase		:       Optional[int]								=      torch.full(lowerCamelCase__	,  lowerCamelCase__ ).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		:       Optional[Any]								=      latents.to(self.device )

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

					_UpperCAmelCase		:       str								=      self.scheduler.timesteps.to(self.device )

					_UpperCAmelCase		:       Any								=      latents

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

							# predict the un-noised image
							# model_output == `log_p_x_0`
							_UpperCAmelCase		:       Any								=      self.transformer(lowerCamelCase__	,  encoder_hidden_states=lowerCamelCase__	,  timestep=lowerCamelCase__ ).sample

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

							_UpperCAmelCase		:       Dict								=      self.truncate(lowerCamelCase__	,  lowerCamelCase__ )

							# remove `log(0)`'s (`-inf`s)
							_UpperCAmelCase		:       Optional[int]								=      model_output.clamp(-70 )

							# compute the previous noisy sample x_t -> x_t-1
							_UpperCAmelCase		:       Any								=      self.scheduler.step(lowerCamelCase__	,  timestep=lowerCamelCase__	,  sample=lowerCamelCase__	,  generator=lowerCamelCase__ ).prev_sample

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

					_UpperCAmelCase		:       Any								=      self.vqvae.config.vq_embed_dim
					_UpperCAmelCase		:       Dict								=      (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
					_UpperCAmelCase		:       Optional[int]								=      self.vqvae.quantize.get_codebook_entry(lowerCamelCase__	,  shape=lowerCamelCase__ )
					_UpperCAmelCase		:       int								=      self.vqvae.decode(lowerCamelCase__	,  force_not_quantize=lowerCamelCase__ ).sample

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

					if output_type == "pil":
							_UpperCAmelCase		:       List[str]								=      self.numpy_to_pil(lowerCamelCase__ )

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=lowerCamelCase__ )






			def 							lowerCAmelCase__    (		self  :     Optional[Any]	,  lowerCamelCase__  :     torch.FloatTensor	,  lowerCamelCase__  :     float )   ->torch.FloatTensor:



					'''simple docstring'''







					_UpperCAmelCase					,    _UpperCAmelCase		:       Tuple								=      torch.sort(lowerCamelCase__	,  1	,  descending=lowerCamelCase__ )
					_UpperCAmelCase		:       Any								=      torch.exp(lowerCamelCase__ )
					_UpperCAmelCase		:       Union[str, Any]								=      sorted_p_x_0.cumsum(dim=1 ) < truncation_rate

					# Ensure that at least the largest probability is not zeroed out
					_UpperCAmelCase		:       List[str]								=      torch.full_like(keep_mask[:, 0:1, :]	,  lowerCamelCase__ )
					_UpperCAmelCase		:       Optional[Any]								=      torch.cat((all_true, keep_mask)	,  dim=1 )
					_UpperCAmelCase		:       str								=      keep_mask[:, :-1, :]

					_UpperCAmelCase		:       str								=      keep_mask.gather(1	,  indices.argsort(1 ) )

					_UpperCAmelCase		:       int								=      log_p_x_0.clone()

					_UpperCAmelCase		:       int								=      -torch.inf  # -inf = log(0)

					return rv







'''simple docstring'''


# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
lowerCamelCase__    	=     abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def  __lowerCAmelCase (__lowerCAmelCase   ):
		from transformers.testing_utils import pytest_addoption_shared

		pytest_addoption_shared(__lowerCAmelCase   )






def  __lowerCAmelCase (__lowerCAmelCase   ):
		from transformers.testing_utils import pytest_terminal_summary_main

		_UpperCAmelCase		:       Optional[int]								=      terminalreporter.config.getoption("--make-reports"   )
		if make_reports:
				pytest_terminal_summary_main(__lowerCAmelCase      , id=__lowerCAmelCase   )



