

from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

a_     :    Tuple     		=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"


def 							__lowerCAmelCase     (							_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Dict		,				_UpperCamelCase		:	Optional[Any]=8						)		->				Optional[int]:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      height // scale_factor**2
 if height % scale_factor**2 != 0:
  new_height += 1
 SCREAMING_SNAKE_CASE		    =      width // scale_factor**2
 if width % scale_factor**2 != 0:
  new_width += 1
 return new_height * scale_factor, new_width * scale_factor




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

    def __init__(      self			:   Union[str, Any]				,     snake_case__			:   UNetaDConditionModel				,     snake_case__			:   DDPMScheduler				,     snake_case__			:   VQModel				,     ):

     """simple docstring"""






     super().__init__()

     self.register_modules(
         unet=snake_case__				,     scheduler=snake_case__				,     movq=snake_case__				,     )
     SCREAMING_SNAKE_CASE		    =      2 ** (len(self.movq.config.block_out_channels     ) - 1)

    def UpperCamelCase      (      self			:   Optional[Any]				,     snake_case__			:   Dict				,     snake_case__			:   List[Any]				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Optional[Any]				,     snake_case__			:   Any				,     snake_case__			:   List[Any]     ):

     """simple docstring"""






     if latents is None:
      SCREAMING_SNAKE_CASE		    =      randn_tensor(snake_case__				,     generator=snake_case__				,     device=snake_case__				,     dtype=snake_case__     )
     else:
      if latents.shape != shape:
       raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""     )
      SCREAMING_SNAKE_CASE		    =      latents.to(snake_case__     )

     SCREAMING_SNAKE_CASE		    =      latents * scheduler.init_noise_sigma
     return latents

    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   int=0     ):

     """simple docstring"""






     if is_accelerate_available():
      from accelerate import cpu_offload
     else:
      raise ImportError('Please install accelerate via `pip install accelerate`'     )

     SCREAMING_SNAKE_CASE		    =      torch.device(F"""cuda:{gpu_id}"""     )

     SCREAMING_SNAKE_CASE		    =      [
         self.unet,
         self.movq,
     ]
     for cpu_offloaded_model in models:
      if cpu_offloaded_model is not None:
       cpu_offload(snake_case__				,     snake_case__     )

    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   Optional[int]=0     ):

     """simple docstring"""






     if is_accelerate_available() and is_accelerate_version('>='				,     '0.17.0.dev0'     ):
      from accelerate import cpu_offload_with_hook
     else:
      raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'     )

     SCREAMING_SNAKE_CASE		    =      torch.device(F"""cuda:{gpu_id}"""     )

     if self.device.type != "cpu":
      self.to('cpu'				,     silence_dtype_warnings=snake_case__     )
      torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

     SCREAMING_SNAKE_CASE		    =      None
     for cpu_offloaded_model in [self.unet, self.movq]:
      SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      cpu_offload_with_hook(snake_case__				,     snake_case__				,     prev_module_hook=snake_case__     )

     # We'll offload the last model manually.
     SCREAMING_SNAKE_CASE		    =      hook

    @property
    # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     if not hasattr(self.unet				,     '_hf_hook'     ):
      return self.device
     for module in self.unet.modules():
      if (
          hasattr(snake_case__				,     '_hf_hook'     )
          and hasattr(module._hf_hook				,     'execution_device'     )
          and module._hf_hook.execution_device is not None
      ):
       return torch.device(module._hf_hook.execution_device     )
     return self.device




    @torch.no_grad()
    @replace_example_docstring(snake_case__     )
    def __call__(      self			:   int				,     snake_case__			:   Union[torch.FloatTensor, List[torch.FloatTensor]]				,     snake_case__			:   Union[torch.FloatTensor, List[torch.FloatTensor]]				,     snake_case__			:   int = 5_1_2				,     snake_case__			:   int = 5_1_2				,     snake_case__			:   int = 1_0_0				,     snake_case__			:   float = 4.0				,     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				,     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self._execution_device

     SCREAMING_SNAKE_CASE		    =      guidance_scale > 1.0

     if isinstance(snake_case__				,     snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      torch.cat(snake_case__				,     dim=0     )
     SCREAMING_SNAKE_CASE		    =      image_embeds.shape[0] * num_images_per_prompt
     if isinstance(snake_case__				,     snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      torch.cat(snake_case__				,     dim=0     )

     if do_classifier_free_guidance:
      SCREAMING_SNAKE_CASE		    =      image_embeds.repeat_interleave(snake_case__				,     dim=0     )
      SCREAMING_SNAKE_CASE		    =      negative_image_embeds.repeat_interleave(snake_case__				,     dim=0     )

     SCREAMING_SNAKE_CASE		    =      torch.cat([negative_image_embeds, image_embeds]				,     dim=0     ).to(dtype=self.unet.dtype				,     device=snake_case__     )

     self.scheduler.set_timesteps(snake_case__				,     device=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      self.scheduler.timesteps

     SCREAMING_SNAKE_CASE		    =      self.unet.config.in_channels

     SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      downscale_height_and_width(snake_case__				,     snake_case__				,     self.movq_scale_factor     )

     # create initial latent
     SCREAMING_SNAKE_CASE		    =      self.prepare_latents(
         (batch_size, num_channels_latents, height, width)				,     image_embeds.dtype				,     snake_case__				,     snake_case__				,     snake_case__				,     self.scheduler				,     )

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

      SCREAMING_SNAKE_CASE		    =      {'image_embeds': image_embeds}
      SCREAMING_SNAKE_CASE		    =      self.unet(
          sample=snake_case__				,     timestep=snake_case__				,     encoder_hidden_states=snake_case__				,     added_cond_kwargs=snake_case__				,     return_dict=snake_case__				,     )[0]

      if do_classifier_free_guidance:
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      noise_pred.split(latents.shape[1]				,     dim=1     )
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      noise_pred.chunk(2     )
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      variance_pred.chunk(2     )
       SCREAMING_SNAKE_CASE		    =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
       SCREAMING_SNAKE_CASE		    =      torch.cat([noise_pred, variance_pred_text]				,     dim=1     )

      if not (
          hasattr(self.scheduler.config				,     'variance_type'     )
          and self.scheduler.config.variance_type in ["learned", "learned_range"]
      ):
       SCREAMING_SNAKE_CASE    ,						SCREAMING_SNAKE_CASE		    =      noise_pred.split(latents.shape[1]				,     dim=1     )

      # compute the previous noisy sample x_t -> x_t-1
      SCREAMING_SNAKE_CASE		    =      self.scheduler.step(
          snake_case__				,     snake_case__				,     snake_case__				,     generator=snake_case__				,     )[0]
     # post-processing
     SCREAMING_SNAKE_CASE		    =      self.movq.decode(snake_case__				,     force_not_quantize=snake_case__     )['sample']

     if output_type not in ["pt", "np", "pil"]:
      raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"""     )

     if output_type in ["np", "pil"]:
      SCREAMING_SNAKE_CASE		    =      image * 0.5 + 0.5
      SCREAMING_SNAKE_CASE		    =      image.clamp(0				,     1     )
      SCREAMING_SNAKE_CASE		    =      image.cpu().permute(0				,     2				,     3				,     1     ).float().numpy()

     if output_type == "pil":
      SCREAMING_SNAKE_CASE		    =      self.numpy_to_pil(snake_case__     )

     if not return_dict:
      return (image,)

     return ImagePipelineOutput(images=snake_case__     )




import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
      from transformers.models.rag.configuration_rag import RagConfig
      from transformers.models.rag.tokenization_rag import RagTokenizer




@require_faiss
@require_torch
class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):

    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      tempfile.mkdtemp()
     SCREAMING_SNAKE_CASE		    =      8

     # DPR tok
     SCREAMING_SNAKE_CASE		    =      [
         '[UNK]',
         '[CLS]',
         '[SEP]',
         '[PAD]',
         '[MASK]',
         'want',
         '##want',
         '##ed',
         'wa',
         'un',
         'runn',
         '##ing',
         ',',
         'low',
         'lowest',
     ]
     SCREAMING_SNAKE_CASE		    =      os.path.join(self.tmpdirname				,     'dpr_tokenizer'     )
     os.makedirs(snake_case__				,     exist_ok=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      os.path.join(snake_case__				,     DPR_VOCAB_FILES_NAMES['vocab_file']     )
     with open(self.vocab_file				,     'w'				,     encoding='utf-8'     ) as vocab_writer:
      vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]     )     )

     # BART tok
     SCREAMING_SNAKE_CASE		    =      [
         'l',
         'o',
         'w',
         'e',
         'r',
         's',
         't',
         'i',
         'd',
         'n',
         '\u0120',
         '\u0120l',
         '\u0120n',
         '\u0120lo',
         '\u0120low',
         'er',
         '\u0120lowest',
         '\u0120newer',
         '\u0120wider',
         '<unk>',
     ]
     SCREAMING_SNAKE_CASE		    =      dict(zip(snake_case__				,     range(len(snake_case__     )     )     )     )
     SCREAMING_SNAKE_CASE		    =      ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
     SCREAMING_SNAKE_CASE		    =      {'unk_token': '<unk>'}

     SCREAMING_SNAKE_CASE		    =      os.path.join(self.tmpdirname				,     'bart_tokenizer'     )
     os.makedirs(snake_case__				,     exist_ok=snake_case__     )
     SCREAMING_SNAKE_CASE		    =      os.path.join(snake_case__				,     BART_VOCAB_FILES_NAMES['vocab_file']     )
     SCREAMING_SNAKE_CASE		    =      os.path.join(snake_case__				,     BART_VOCAB_FILES_NAMES['merges_file']     )
     with open(self.vocab_file				,     'w'				,     encoding='utf-8'     ) as fp:
      fp.write(json.dumps(snake_case__     ) + '\n'     )
     with open(self.merges_file				,     'w'				,     encoding='utf-8'     ) as fp:
      fp.write('\n'.join(snake_case__     )     )

    def UpperCamelCase      (      self			:   Optional[Any]     ):

     """simple docstring"""






     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     'dpr_tokenizer'     )     )

    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname				,     'bart_tokenizer'     )     )

    def UpperCamelCase      (      self			:   Tuple     ):

     """simple docstring"""






     shutil.rmtree(self.tmpdirname     )

    @require_tokenizers
    def UpperCamelCase      (      self			:   Optional[int]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      os.path.join(self.tmpdirname				,     'rag_tokenizer'     )
     SCREAMING_SNAKE_CASE		    =      RagConfig(question_encoder=DPRConfig().to_dict()				,     generator=BartConfig().to_dict()     )
     SCREAMING_SNAKE_CASE		    =      RagTokenizer(question_encoder=self.get_dpr_tokenizer()				,     generator=self.get_bart_tokenizer()     )
     rag_config.save_pretrained(snake_case__     )
     rag_tokenizer.save_pretrained(snake_case__     )
     SCREAMING_SNAKE_CASE		    =      RagTokenizer.from_pretrained(snake_case__				,     config=snake_case__     )
     self.assertIsInstance(new_rag_tokenizer.question_encoder				,     snake_case__     )
     self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()				,     rag_tokenizer.question_encoder.get_vocab()     )
     self.assertIsInstance(new_rag_tokenizer.generator				,     snake_case__     )
     self.assertEqual(new_rag_tokenizer.generator.get_vocab()				,     rag_tokenizer.generator.get_vocab()     )

    @slow
    def UpperCamelCase      (      self			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      RagTokenizer.from_pretrained('facebook/rag-token-nq'     )
     SCREAMING_SNAKE_CASE		    =      [
         'who got the first nobel prize in physics',
         'when is the next deadpool movie being released',
         'which mode is used for short wave broadcast service',
         'who is the owner of reading football club',
         'when is the next scandal episode coming out',
         'when is the last time the philadelphia won the superbowl',
         'what is the most current adobe flash player version',
         'how many episodes are there in dragon ball z',
         'what is the first step in the evolution of the eye',
         'where is gall bladder situated in human body',
         'what is the main mineral in lithium batteries',
         'who is the president of usa right now',
         'where do the greasers live in the outsiders',
         'panda is a national animal of which country',
         'what is the name of manchester united stadium',
     ]
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__     )
     self.assertIsNotNone(snake_case__     )




    @slow
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      RagTokenizer.from_pretrained('facebook/rag-sequence-nq'     )
     SCREAMING_SNAKE_CASE		    =      [
         'who got the first nobel prize in physics',
         'when is the next deadpool movie being released',
         'which mode is used for short wave broadcast service',
         'who is the owner of reading football club',
         'when is the next scandal episode coming out',
         'when is the last time the philadelphia won the superbowl',
         'what is the most current adobe flash player version',
         'how many episodes are there in dragon ball z',
         'what is the first step in the evolution of the eye',
         'where is gall bladder situated in human body',
         'what is the main mineral in lithium batteries',
         'who is the president of usa right now',
         'where do the greasers live in the outsiders',
         'panda is a national animal of which country',
         'what is the name of manchester united stadium',
     ]
     SCREAMING_SNAKE_CASE		    =      tokenizer(snake_case__     )
     self.assertIsNotNone(snake_case__     )

