

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


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

lowerCAmelCase_			      =      '''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''


def     __SCREAMING_SNAKE_CASE   (SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=8			):
						snake_case_  =      h // scale_factor**2
						if h % scale_factor**2 != 0:
												new_h += 1
						snake_case_  =      w // scale_factor**2
						if w % scale_factor**2 != 0:
												new_w += 1
						return new_h * scale_factor, new_w * scale_factor





class       snake_case_  (						__A   ):







			'''simple docstring'''

			def __init__(	self					:							Dict				,  _UpperCamelCase					:							MultilingualCLIP				,  _UpperCamelCase					:							XLMRobertaTokenizer				,  _UpperCamelCase					:							UNetaDConditionModel				,  _UpperCamelCase					:							Union[DDIMScheduler, DDPMScheduler]				,  _UpperCamelCase					:							VQModel				,  ) ->List[Any]:
									super().__init__()

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







			def 							snake_case__(	self					:							Optional[Any]				,  _UpperCamelCase					:							Optional[Any]				,  _UpperCamelCase					:							Any				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							int				,  _UpperCamelCase					:							Union[str, Any]				,  _UpperCamelCase					:							Union[str, Any]				) ->Union[str, Any]:
									if latents is None:
															snake_case_  =      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}'''				)
															snake_case_  =      latents.to(_UpperCamelCase				)

									snake_case_  =      latents * scheduler.init_noise_sigma
									return latents







			def 							snake_case__(	self					:							str				,  _UpperCamelCase					:							Tuple				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							List[Any]				,  _UpperCamelCase					:							List[str]				,  _UpperCamelCase					:							str=None				,  ) ->Tuple:
									snake_case_  =      len(_UpperCamelCase				) if isinstance(_UpperCamelCase				,  _UpperCamelCase				) else 1
									# get prompt text embeddings
									snake_case_  =      self.tokenizer(
									    _UpperCamelCase				,  padding='''max_length'''				,  truncation=_UpperCamelCase				,  max_length=7_7				,  return_attention_mask=_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				,  return_tensors='''pt'''				,  )

									snake_case_  =      text_inputs.input_ids
									snake_case_  =      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				):
															snake_case_  =      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}'''				)

									snake_case_  =      text_input_ids.to(_UpperCamelCase				)
									snake_case_  =      text_inputs.attention_mask.to(_UpperCamelCase				)

									snake_case_,       snake_case_  =      self.text_encoder(
									    input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				)

									snake_case_  =      prompt_embeds.repeat_interleave(_UpperCamelCase				,  dim=0				)
									snake_case_  =      text_encoder_hidden_states.repeat_interleave(_UpperCamelCase				,  dim=0				)
									snake_case_  =      text_mask.repeat_interleave(_UpperCamelCase				,  dim=0				)

									if do_classifier_free_guidance:
															snake_case_  =      42
															if negative_prompt is None:
																					snake_case_  =      [''''''] * 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				):
																					snake_case_  =      [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:
																					snake_case_  =      negative_prompt

															snake_case_  =      self.tokenizer(
															    _UpperCamelCase				,  padding='''max_length'''				,  max_length=7_7				,  truncation=_UpperCamelCase				,  return_attention_mask=_UpperCamelCase				,  add_special_tokens=_UpperCamelCase				,  return_tensors='''pt'''				,  )
															snake_case_  =      uncond_input.input_ids.to(_UpperCamelCase				)
															snake_case_  =      uncond_input.attention_mask.to(_UpperCamelCase				)

															snake_case_,       snake_case_  =      self.text_encoder(
															    input_ids=_UpperCamelCase				,  attention_mask=_UpperCamelCase				)

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

															snake_case_  =      negative_prompt_embeds.shape[1]
															snake_case_  =      negative_prompt_embeds.repeat(1				,  _UpperCamelCase				)
															snake_case_  =      negative_prompt_embeds.view(batch_size * num_images_per_prompt				,  _UpperCamelCase				)

															snake_case_  =      uncond_text_encoder_hidden_states.shape[1]
															snake_case_  =      uncond_text_encoder_hidden_states.repeat(1				,  _UpperCamelCase				,  1				)
															snake_case_  =      uncond_text_encoder_hidden_states.view(
															    batch_size * num_images_per_prompt				,  _UpperCamelCase				,  -1				)
															snake_case_  =      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
															snake_case_  =      torch.cat([negative_prompt_embeds, prompt_embeds]				)
															snake_case_  =      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]				)

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

									return prompt_embeds, text_encoder_hidden_states, text_mask







			def 							snake_case__(	self					:							Dict				,  _UpperCamelCase					:							int=0				) ->Union[str, Any]:
									if is_accelerate_available():
															from accelerate import cpu_offload
									else:
															raise ImportError('''Please install accelerate via `pip install accelerate`'''				)

									snake_case_  =      torch.device(f'''cuda:{gpu_id}'''				)

									snake_case_  =      [
									    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 							snake_case__(	self					:							str				,  _UpperCamelCase					:							str=0				) ->Tuple:
									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.'''				)

									snake_case_  =      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)

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

									if self.safety_checker is not None:
															snake_case_,       snake_case_  =      cpu_offload_with_hook(self.safety_checker				,  _UpperCamelCase				,  prev_module_hook=_UpperCamelCase				)

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







			@property
			# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
			def 							snake_case__(	self					:							Optional[int]				) ->Optional[Any]:
									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					:							Tuple				,  _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 = 5_1_2				,  _UpperCamelCase					:							int = 5_1_2				,  _UpperCamelCase					:							int = 1_0_0				,  _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				,  ) ->Optional[int]:

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

									snake_case_  =      self._execution_device

									snake_case_  =      batch_size * num_images_per_prompt
									snake_case_  =      guidance_scale > 1.0

									snake_case_,       snake_case_,       snake_case_  =      self._encode_prompt(
									    _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				,  _UpperCamelCase				)

									if isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      torch.cat(_UpperCamelCase				,  dim=0				)
									if isinstance(_UpperCamelCase				,  _UpperCamelCase				):
															snake_case_  =      torch.cat(_UpperCamelCase				,  dim=0				)

									if do_classifier_free_guidance:
															snake_case_  =      image_embeds.repeat_interleave(_UpperCamelCase				,  dim=0				)
															snake_case_  =      negative_image_embeds.repeat_interleave(_UpperCamelCase				,  dim=0				)

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

									self.scheduler.set_timesteps(_UpperCamelCase				,  device=_UpperCamelCase				)
									snake_case_  =      self.scheduler.timesteps

									snake_case_  =      self.unet.config.in_channels

									snake_case_,       snake_case_  =      get_new_h_w(_UpperCamelCase				,  _UpperCamelCase				,  self.movq_scale_factor				)

									# create initial latent
									snake_case_  =      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
															snake_case_  =      torch.cat([latents] * 2				) if do_classifier_free_guidance else latents

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

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

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

									if output_type == "pil":
															snake_case_  =      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_     ,	)

