
"""simple docstring"""



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,
)


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

UpperCamelCase__          =	'\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   (     _snake_case    ,_snake_case    ,_snake_case=8 ):
    UpperCAmelCase__	:      List[str]     							=					height // scale_factor**2
    if height % scale_factor**2 != 0:
        new_height += 1
    UpperCAmelCase__	:      Tuple     							=					width // scale_factor**2
    if width % scale_factor**2 != 0:
        new_width += 1
    return new_height * scale_factor, new_width * scale_factor





class 				a			(					lowercase      ):



      def __init__(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	):
          super().__init__()

          self.register_modules(
              unet=UpperCamelCase_     ,	scheduler=UpperCamelCase_     ,	movq=UpperCamelCase_     ,	)
          UpperCAmelCase__	:      int     							=					2 ** (len(self.movq.config.block_out_channels      ) - 1)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_      ):
          if latents is None:
              UpperCAmelCase__	:      Tuple     							=					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__	:      Optional[Any]     							=					latents.to(UpperCamelCase_      )

          UpperCAmelCase__	:      Tuple     							=					latents * scheduler.init_noise_sigma
          return latents



      def  __snake_case							(   self     ,	UpperCamelCase_=0      ):
          if is_accelerate_available():
              from accelerate import cpu_offload
          else:
              raise ImportError('Please install accelerate via `pip install accelerate`'      )

          UpperCAmelCase__	:      int     							=					torch.device(F'''cuda:{gpu_id}'''      )

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



      def  __snake_case							(   self     ,	UpperCamelCase_=0      ):
          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__	:      int     							=					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__	:      Dict     							=					None
          for cpu_offloaded_model in [self.unet, self.movq]:
              UpperCAmelCase__							,							UpperCAmelCase__	:      Any     							=					cpu_offload_with_hook(UpperCamelCase_     ,	UpperCamelCase_     ,	prev_module_hook=UpperCamelCase_      )

          # We'll offload the last model manually.
          UpperCAmelCase__	:      List[Any]     							=					hook



      @property
      # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
      def  __snake_case							(   self      ):
          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     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_ = 512     ,	UpperCamelCase_ = 512     ,	UpperCamelCase_ = 100     ,	UpperCamelCase_ = 4.0     ,	UpperCamelCase_ = 1     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = "pil"     ,	UpperCamelCase_ = True     ,	):
          UpperCAmelCase__	:      Any     							=					self._execution_device

          UpperCAmelCase__	:      int     							=					guidance_scale > 1.0

          if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
              UpperCAmelCase__	:      Optional[Any]     							=					torch.cat(UpperCamelCase_     ,	dim=0      )
          UpperCAmelCase__	:      str     							=					image_embeds.shape[0] * num_images_per_prompt
          if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ):
              UpperCAmelCase__	:      Optional[Any]     							=					torch.cat(UpperCamelCase_     ,	dim=0      )

          if do_classifier_free_guidance:
              UpperCAmelCase__	:      List[Any]     							=					image_embeds.repeat_interleave(UpperCamelCase_     ,	dim=0      )
              UpperCAmelCase__	:      Tuple     							=					negative_image_embeds.repeat_interleave(UpperCamelCase_     ,	dim=0      )

          UpperCAmelCase__	:      List[str]     							=					torch.cat([negative_image_embeds, image_embeds]     ,	dim=0      ).to(dtype=self.unet.dtype     ,	device=UpperCamelCase_      )

          self.scheduler.set_timesteps(UpperCamelCase_     ,	device=UpperCamelCase_      )
          UpperCAmelCase__	:      Optional[Any]     							=					self.scheduler.timesteps

          UpperCAmelCase__	:      List[Any]     							=					self.unet.config.in_channels

          UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[Any]     							=					downscale_height_and_width(UpperCamelCase_     ,	UpperCamelCase_     ,	self.movq_scale_factor      )

          # create initial latent
          UpperCAmelCase__	:      int     							=					self.prepare_latents(
              (batch_size, num_channels_latents, height, width)     ,	image_embeds.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__	:      Tuple     							=					torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents

              UpperCAmelCase__	:      int     							=					{'image_embeds': image_embeds}
              UpperCAmelCase__	:      int     							=					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__	:      Any     							=					noise_pred.split(latents.shape[1]     ,	dim=1      )
                  UpperCAmelCase__							,							UpperCAmelCase__	:      Optional[int]     							=					noise_pred.chunk(2      )
                  UpperCAmelCase__							,							UpperCAmelCase__	:      int     							=					variance_pred.chunk(2      )
                  UpperCAmelCase__	:      Union[str, Any]     							=					noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
                  UpperCAmelCase__	:      int     							=					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__	:      int     							=					noise_pred.split(latents.shape[1]     ,	dim=1      )

              # compute the previous noisy sample x_t -> x_t-1
              UpperCAmelCase__	:      Optional[Any]     							=					self.scheduler.step(
                  UpperCamelCase_     ,	UpperCamelCase_     ,	UpperCamelCase_     ,	generator=UpperCamelCase_     ,	)[0]
          # post-processing
          UpperCAmelCase__	:      Union[str, Any]     							=					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__	:      Tuple     							=					image * 0.5 + 0.5
              UpperCAmelCase__	:      Optional[int]     							=					image.clamp(0     ,	1      )
              UpperCAmelCase__	:      Optional[Any]     							=					image.cpu().permute(0     ,	2     ,	3     ,	1      ).float().numpy()

          if output_type == "pil":
              UpperCAmelCase__	:      Union[str, Any]     							=					self.numpy_to_pil(UpperCamelCase_      )

          if not return_dict:
              return (image,)

          return ImagePipelineOutput(images=UpperCamelCase_      )



"""simple docstring"""



import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece.model')
UpperCamelCase__          =	get_tests_dir('fixtures/test_sentencepiece_bpe.model')

UpperCamelCase__          =	'pt' if is_torch_available() else 'tf'





@require_sentencepiece
@require_tokenizers
class 				a			(					lowercase						,		unittest.TestCase      ):
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizer
      UpperCamelCase							:   Optional[int]           =     CamembertTokenizerFast
      UpperCamelCase							:   str           =     True
      UpperCamelCase							:   Tuple           =     True



      def  __snake_case							(   self      ):
          super().setUp()

          # We have a SentencePiece fixture for testing
          UpperCAmelCase__	:      int     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[int]     							=					'<pad>'
          UpperCAmelCase__	:      Tuple     							=					1

          self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase_      )     ,	UpperCamelCase_      )
          self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase_      )     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					list(self.get_tokenizer().get_vocab().keys()      )

          self.assertEqual(vocab_keys[0]     ,	'<s>NOTUSED'      )
          self.assertEqual(vocab_keys[1]     ,	'<pad>'      )
          self.assertEqual(vocab_keys[-1]     ,	'<mask>'      )
          self.assertEqual(len(UpperCamelCase_      )     ,	1_004      )



      def  __snake_case							(   self      ):
          self.assertEqual(self.get_tokenizer().vocab_size     ,	1_005      )



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Dict     							=					CamembertTokenizer(UpperCamelCase_      )
          tokenizer.save_pretrained(self.tmpdirname      )
          UpperCAmelCase__	:      str     							=					CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

          UpperCAmelCase__	:      Union[str, Any]     							=					'I was born in 92000, and this is falsé.'

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      Dict     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          # <unk> tokens are not the same for `rust` than for `slow`.
          # Because spm gives back raw token instead of `unk` in EncodeAsPieces
          # tokens = tokenizer.tokenize(sequence)
          UpperCAmelCase__	:      List[Any]     							=					tokenizer.convert_ids_to_tokens(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      def  __snake_case							(   self      ):
          if not self.test_rust_tokenizer:
              return

          UpperCAmelCase__	:      Any     							=					self.get_tokenizer()
          UpperCAmelCase__	:      List[str]     							=					self.get_rust_tokenizer()

          UpperCAmelCase__	:      List[str]     							=					'I was born in 92000, and this is falsé.'

          UpperCAmelCase__	:      List[str]     							=					tokenizer.tokenize(UpperCamelCase_      )
          UpperCAmelCase__	:      str     							=					rust_tokenizer.tokenize(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      Optional[Any]     							=					tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          UpperCAmelCase__	:      Union[str, Any]     							=					rust_tokenizer.encode(UpperCamelCase_     ,	add_special_tokens=UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      str     							=					self.get_rust_tokenizer()
          UpperCAmelCase__	:      Union[str, Any]     							=					tokenizer.encode(UpperCamelCase_      )
          UpperCAmelCase__	:      List[str]     							=					rust_tokenizer.encode(UpperCamelCase_      )
          self.assertListEqual(UpperCamelCase_     ,	UpperCamelCase_      )



      @slow
      def  __snake_case							(   self      ):
          # fmt: off
          UpperCAmelCase__	:      Any     							=					{'input_ids': [[5, 54, 7_196, 297, 30, 23, 776, 18, 11, 3_215, 3_705, 8_252, 22, 3_164, 1_181, 2_116, 29, 16, 813, 25, 791, 3_314, 20, 3_446, 38, 27_575, 120, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 468, 17, 11, 9_088, 20, 1_517, 8, 22_804, 18_818, 10, 38, 629, 607, 607, 142, 19, 7_196, 867, 56, 10_326, 24, 2_267, 20, 416, 5_072, 15_612, 233, 734, 7, 2_399, 27, 16, 3_015, 1_649, 7, 24, 20, 4_338, 2_399, 27, 13, 3_400, 14, 13, 6_189, 8, 930, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
          # fmt: on

          # camembert is a french model. So we also use french texts.
          UpperCAmelCase__	:      Union[str, Any]     							=					[
              'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
              'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
              'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
              'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
              'telles que la traduction et la synthèse de texte.',
          ]

          self.tokenizer_integration_test_util(
              expected_encoding=UpperCamelCase_     ,	model_name='camembert-base'     ,	revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'     ,	sequences=UpperCamelCase_     ,	)

