'''simple docstring'''
from typing import List, Optional, Union

import torch
from transformers import (
    XLMRobertaTokenizer,
)

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


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

lowercase__  :      Dict        =					"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained(\"kandinsky-community/Kandinsky-2-1-prior\")\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> negative_image_emb = out.negative_image_embeds\n\n        >>> pipe = KandinskyPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-1\")\n        >>> pipe.to(\"cuda\")\n\n        >>> image = pipe(\n        ...     prompt,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=negative_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ... ).images\n\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def  __lowerCamelCase       (      _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  Any=8					):


      '''simple docstring'''


      UpperCAmelCase_            =  h // scale_factor**2
      if h % scale_factor**2 != 0:
            new_h += 1
      UpperCAmelCase_            =  w // scale_factor**2
      if w % scale_factor**2 != 0:
            new_w += 1
      return new_h * scale_factor, new_w * scale_factor







class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  def __init__(      self			:       int  ,							UpperCAmelCase__			:       MultilingualCLIP  ,							UpperCAmelCase__			:       XLMRobertaTokenizer  ,							UpperCAmelCase__			:       UNetaDConditionModel  ,							UpperCAmelCase__			:       Union[DDIMScheduler, DDPMScheduler]  ,							UpperCAmelCase__			:       VQModel  ,							)				->Optional[int]:
        super().__init__()

        self.register_modules(
            text_encoder=UpperCAmelCase__  ,							tokenizer=UpperCAmelCase__  ,							unet=UpperCAmelCase__  ,							scheduler=UpperCAmelCase__  ,							movq=UpperCAmelCase__  ,							)
        UpperCAmelCase_            =  2 ** (len(self.movq.config.block_out_channels  ) - 1)






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       List[Any]  ,							UpperCAmelCase__			:       Tuple  )				->Dict:
        if latents is None:
              UpperCAmelCase_            =  randn_tensor(UpperCAmelCase__  ,							generator=UpperCAmelCase__  ,							device=UpperCAmelCase__  ,							dtype=UpperCAmelCase__  )
        else:
              if latents.shape != shape:
                    raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
              UpperCAmelCase_            =  latents.to(UpperCAmelCase__  )

        UpperCAmelCase_            =  latents * scheduler.init_noise_sigma
        return latents






  def  lowerCAmelCase__ (      self			:       Tuple  ,							UpperCAmelCase__			:       Optional[int]  ,							UpperCAmelCase__			:       Any  ,							UpperCAmelCase__			:       Optional[Any]  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Any=None  ,							)				->List[Any]:
        UpperCAmelCase_            =  len(UpperCAmelCase__  ) if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ) else 1
        # get prompt text embeddings
        UpperCAmelCase_            =  self.tokenizer(
            UpperCAmelCase__  ,							padding='''max_length'''  ,							truncation=UpperCAmelCase__  ,							max_length=77  ,							return_attention_mask=UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_tensors='''pt'''  ,							)

        UpperCAmelCase_            =  text_inputs.input_ids
        UpperCAmelCase_            =  self.tokenizer(UpperCAmelCase__  ,							padding='''longest'''  ,							return_tensors='''pt'''  ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(UpperCAmelCase__  ,							UpperCAmelCase__  ):
              UpperCAmelCase_            =  self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
              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.to(UpperCAmelCase__  )
        UpperCAmelCase_            =  text_inputs.attention_mask.to(UpperCAmelCase__  )

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.text_encoder(
            input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  )

        UpperCAmelCase_            =  prompt_embeds.repeat_interleave(UpperCAmelCase__  ,							dim=0  )
        UpperCAmelCase_            =  text_encoder_hidden_states.repeat_interleave(UpperCAmelCase__  ,							dim=0  )
        UpperCAmelCase_            =  text_mask.repeat_interleave(UpperCAmelCase__  ,							dim=0  )

        if do_classifier_free_guidance:
              UpperCAmelCase_            =  42
              if negative_prompt is None:
                    UpperCAmelCase_            =  [''''''] * batch_size
              elif type(UpperCAmelCase__  ) is not type(UpperCAmelCase__  ):
                    raise TypeError(
                        f"""`negative_prompt` should be the same type to `prompt`, but got {type(UpperCAmelCase__  )} !="""
                        f""" {type(UpperCAmelCase__  )}."""  )
              elif isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ):
                    UpperCAmelCase_            =  [negative_prompt]
              elif batch_size != len(UpperCAmelCase__  ):
                    raise ValueError(
                        f"""`negative_prompt`: {negative_prompt} has batch size {len(UpperCAmelCase__  )}, but `prompt`:"""
                        f""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
                        ''' the batch size of `prompt`.'''  )
              else:
                    UpperCAmelCase_            =  negative_prompt

              UpperCAmelCase_            =  self.tokenizer(
                  UpperCAmelCase__  ,							padding='''max_length'''  ,							max_length=77  ,							truncation=UpperCAmelCase__  ,							return_attention_mask=UpperCAmelCase__  ,							add_special_tokens=UpperCAmelCase__  ,							return_tensors='''pt'''  ,							)
              UpperCAmelCase_            =  uncond_input.input_ids.to(UpperCAmelCase__  )
              UpperCAmelCase_            =  uncond_input.attention_mask.to(UpperCAmelCase__  )

              UpperCAmelCase_      ,		UpperCAmelCase_            =  self.text_encoder(
                  input_ids=UpperCAmelCase__  ,							attention_mask=UpperCAmelCase__  )

              # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

              UpperCAmelCase_            =  negative_prompt_embeds.shape[1]
              UpperCAmelCase_            =  negative_prompt_embeds.repeat(1  ,							UpperCAmelCase__  )
              UpperCAmelCase_            =  negative_prompt_embeds.view(batch_size * num_images_per_prompt  ,							UpperCAmelCase__  )

              UpperCAmelCase_            =  uncond_text_encoder_hidden_states.shape[1]
              UpperCAmelCase_            =  uncond_text_encoder_hidden_states.repeat(1  ,							UpperCAmelCase__  ,							1  )
              UpperCAmelCase_            =  uncond_text_encoder_hidden_states.view(
                  batch_size * num_images_per_prompt  ,							UpperCAmelCase__  ,							-1  )
              UpperCAmelCase_            =  uncond_text_mask.repeat_interleave(UpperCAmelCase__  ,							dim=0  )

              # done duplicates

              # 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]  )
              UpperCAmelCase_            =  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]  )

              UpperCAmelCase_            =  torch.cat([uncond_text_mask, text_mask]  )

        return prompt_embeds, text_encoder_hidden_states, text_mask






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Optional[Any]=0  )				->List[str]:
        if is_accelerate_available():
              from accelerate import cpu_offload
        else:
              raise ImportError('''Please install accelerate via `pip install accelerate`'''  )

        UpperCAmelCase_            =  torch.device(f"""cuda:{gpu_id}"""  )

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






  def  lowerCAmelCase__ (      self			:       int  ,							UpperCAmelCase__			:       Dict=0  )				->List[str]:
        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.'''  )

        UpperCAmelCase_            =  torch.device(f"""cuda:{gpu_id}"""  )

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

        UpperCAmelCase_            =  None
        for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
              UpperCAmelCase_      ,		UpperCAmelCase_            =  cpu_offload_with_hook(UpperCAmelCase__  ,							UpperCAmelCase__  ,							prev_module_hook=UpperCAmelCase__  )

        if self.safety_checker is not None:
              UpperCAmelCase_      ,		UpperCAmelCase_            =  cpu_offload_with_hook(self.safety_checker  ,							UpperCAmelCase__  ,							prev_module_hook=UpperCAmelCase__  )

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






  @property
  # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  def  lowerCAmelCase__ (      self			:       List[Any]  )				->int:
        if not hasattr(self.unet  ,							'''_hf_hook'''  ):
              return self.device
        for module in self.unet.modules():
              if (
                  hasattr(UpperCAmelCase__  ,							'''_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(UpperCAmelCase__  )
  def __call__(      self			:       int  ,							UpperCAmelCase__			:       Union[str, List[str]]  ,							UpperCAmelCase__			:       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							UpperCAmelCase__			:       Union[torch.FloatTensor, List[torch.FloatTensor]]  ,							UpperCAmelCase__			:       Optional[Union[str, List[str]]] = None  ,							UpperCAmelCase__			:       int = 512  ,							UpperCAmelCase__			:       int = 512  ,							UpperCAmelCase__			:       int = 100  ,							UpperCAmelCase__			:       float = 4.0  ,							UpperCAmelCase__			:       int = 1  ,							UpperCAmelCase__			:       Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,							UpperCAmelCase__			:       Optional[torch.FloatTensor] = None  ,							UpperCAmelCase__			:       Optional[str] = "pil"  ,							UpperCAmelCase__			:       bool = True  ,							)				->str:

        if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ):
              UpperCAmelCase_            =  1
        elif isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ):
              UpperCAmelCase_            =  len(UpperCAmelCase__  )
        else:
              raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(UpperCAmelCase__  )}"""  )

        UpperCAmelCase_            =  self._execution_device

        UpperCAmelCase_            =  batch_size * num_images_per_prompt
        UpperCAmelCase_            =  guidance_scale > 1.0

        UpperCAmelCase_      ,		UpperCAmelCase_      ,		UpperCAmelCase_            =  self._encode_prompt(
            UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )

        if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ):
              UpperCAmelCase_            =  torch.cat(UpperCAmelCase__  ,							dim=0  )
        if isinstance(UpperCAmelCase__  ,							UpperCAmelCase__  ):
              UpperCAmelCase_            =  torch.cat(UpperCAmelCase__  ,							dim=0  )

        if do_classifier_free_guidance:
              UpperCAmelCase_            =  image_embeds.repeat_interleave(UpperCAmelCase__  ,							dim=0  )
              UpperCAmelCase_            =  negative_image_embeds.repeat_interleave(UpperCAmelCase__  ,							dim=0  )

        UpperCAmelCase_            =  torch.cat([negative_image_embeds, image_embeds]  ,							dim=0  ).to(
            dtype=prompt_embeds.dtype  ,							device=UpperCAmelCase__  )

        self.scheduler.set_timesteps(UpperCAmelCase__  ,							device=UpperCAmelCase__  )
        UpperCAmelCase_            =  self.scheduler.timesteps

        UpperCAmelCase_            =  self.unet.config.in_channels

        UpperCAmelCase_      ,		UpperCAmelCase_            =  get_new_h_w(UpperCAmelCase__  ,							UpperCAmelCase__  ,							self.movq_scale_factor  )

        # create initial latent
        UpperCAmelCase_            =  self.prepare_latents(
            (batch_size, num_channels_latents, height, width)  ,							text_encoder_hidden_states.dtype  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							self.scheduler  ,							)

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

              UpperCAmelCase_            =  {'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
              UpperCAmelCase_            =  self.unet(
                  sample=UpperCAmelCase__  ,							timestep=UpperCAmelCase__  ,							encoder_hidden_states=UpperCAmelCase__  ,							added_cond_kwargs=UpperCAmelCase__  ,							return_dict=UpperCAmelCase__  ,							)[0]

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

              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase_            =  self.scheduler.step(
                  UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							generator=UpperCAmelCase__  ,							).prev_sample
        # post-processing
        UpperCAmelCase_            =  self.movq.decode(UpperCAmelCase__  ,							force_not_quantize=UpperCAmelCase__  )['''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"]:
              UpperCAmelCase_            =  image * 0.5 + 0.5
              UpperCAmelCase_            =  image.clamp(0  ,							1  )
              UpperCAmelCase_            =  image.cpu().permute(0  ,							2  ,							3  ,							1  ).float().numpy()

        if output_type == "pil":
              UpperCAmelCase_            =  self.numpy_to_pil(UpperCAmelCase__  )

        if not return_dict:
              return (image,)

        return ImagePipelineOutput(images=UpperCAmelCase__  )






'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin







class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	XLMTokenizer
  lowerCAmelCase__										=	False






  def  lowerCAmelCase__ (      self			:       int  )				->Union[str, Any]:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        UpperCAmelCase_            =  [
            '''l''',
            '''o''',
            '''w''',
            '''e''',
            '''r''',
            '''s''',
            '''t''',
            '''i''',
            '''d''',
            '''n''',
            '''w</w>''',
            '''r</w>''',
            '''t</w>''',
            '''lo''',
            '''low''',
            '''er</w>''',
            '''low</w>''',
            '''lowest</w>''',
            '''newer</w>''',
            '''wider</w>''',
            '''<unk>''',
        ]
        UpperCAmelCase_            =  dict(zip(UpperCAmelCase__  ,							range(len(UpperCAmelCase__  )  )  )  )
        UpperCAmelCase_            =  ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''vocab_file''']  )
        UpperCAmelCase_            =  os.path.join(self.tmpdirname  ,							VOCAB_FILES_NAMES['''merges_file''']  )
        with open(self.vocab_file  ,							'''w'''  ) as fp:
              fp.write(json.dumps(UpperCAmelCase__  )  )
        with open(self.merges_file  ,							'''w'''  ) as fp:
              fp.write('''\n'''.join(UpperCAmelCase__  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       Any  )				->List[Any]:
        UpperCAmelCase_            =  '''lower newer'''
        UpperCAmelCase_            =  '''lower newer'''
        return input_text, output_text






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  )				->Tuple:
        UpperCAmelCase_            =  XLMTokenizer(self.vocab_file  ,							self.merges_file  )

        UpperCAmelCase_            =  '''lower'''
        UpperCAmelCase_            =  ['''low''', '''er</w>''']
        UpperCAmelCase_            =  tokenizer.tokenize(UpperCAmelCase__  )
        self.assertListEqual(UpperCAmelCase__  ,							UpperCAmelCase__  )

        UpperCAmelCase_            =  tokens + ['''<unk>''']
        UpperCAmelCase_            =  [14, 15, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase__  )  ,							UpperCAmelCase__  )






  @slow
  def  lowerCAmelCase__ (      self			:       Any  )				->str:
        UpperCAmelCase_            =  XLMTokenizer.from_pretrained('''xlm-mlm-en-2048'''  )

        UpperCAmelCase_            =  tokenizer.encode('''sequence builders'''  ,							add_special_tokens=UpperCAmelCase__  )
        UpperCAmelCase_            =  tokenizer.encode('''multi-sequence build'''  ,							add_special_tokens=UpperCAmelCase__  )

        UpperCAmelCase_            =  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__  )
        UpperCAmelCase_            =  tokenizer.build_inputs_with_special_tokens(UpperCAmelCase__  ,							UpperCAmelCase__  )

        assert encoded_sentence == [0] + text + [1]
        assert encoded_pair == [0] + text + [1] + text_a + [1]





