






'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_mbart import MBartTokenizer
else:
						A:      Any  				=				None


A:      Optional[int]  				=				logging.get_logger(__name__)


A:      List[str]  				=				{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

A:      Dict  				=				{
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

A:      Tuple  				=				{
    "facebook/mbart-large-en-ro": 1_0_2_4,
    "facebook/mbart-large-cc25": 1_0_2_4,
}

# fmt: off
A:      Optional[Any]  				=				["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	SCREAMING_SNAKE_CASE_					:      Any        =  VOCAB_FILES_NAMES
	SCREAMING_SNAKE_CASE_					:      Optional[int]        =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	SCREAMING_SNAKE_CASE_					:      Any        =  PRETRAINED_VOCAB_FILES_MAP
	SCREAMING_SNAKE_CASE_					:      str        =  ['input_ids', 'attention_mask']
	SCREAMING_SNAKE_CASE_					:      str        =  MBartTokenizer

	SCREAMING_SNAKE_CASE_					:      List[int]        =  []
	SCREAMING_SNAKE_CASE_					:      List[int]        =  []
	def __init__(							self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase="<s>"  ,      _lowercase="</s>"  ,      _lowercase="</s>"  ,      _lowercase="<s>"  ,      _lowercase="<unk>"  ,      _lowercase="<pad>"  ,      _lowercase="<mask>"  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      **_lowercase  ,      )						->  List[str]:
							# Mask token behave like a normal word, i.e. include the space before it
							lowercase_      :   Union[str, Any]        =			AddedToken(_lowercase  ,      lstrip=_lowercase  ,      rstrip=_lowercase       ) if isinstance(_lowercase  ,      _lowercase       ) else mask_token

							super().__init__(
							    vocab_file=_lowercase  ,      tokenizer_file=_lowercase  ,      bos_token=_lowercase  ,      eos_token=_lowercase  ,      sep_token=_lowercase  ,      cls_token=_lowercase  ,      unk_token=_lowercase  ,      pad_token=_lowercase  ,      mask_token=_lowercase  ,      src_lang=_lowercase  ,      tgt_lang=_lowercase  ,      additional_special_tokens=_lowercase  ,      **_lowercase  ,      )

							lowercase_      :   List[Any]        =			vocab_file
							lowercase_      :   List[Any]        =			False if not self.vocab_file else True

							lowercase_      :   Optional[int]        =			FAIRSEQ_LANGUAGE_CODES.copy()

							if additional_special_tokens is not None:
													# Only add those special tokens if they are not already there.
													_additional_special_tokens.extend(
													    [t for t in additional_special_tokens if t not in _additional_special_tokens]       )

							self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}       )
							lowercase_      :   Tuple        =			{
							    lang_code: self.convert_tokens_to_ids(_lowercase       ) for lang_code in FAIRSEQ_LANGUAGE_CODES
							}

							lowercase_      :   List[Any]        =			src_lang if src_lang is not None else 'en_XX'
							lowercase_      :   int        =			self.convert_tokens_to_ids(self._src_lang       )
							lowercase_      :   Any        =			tgt_lang
							self.set_src_lang_special_tokens(self._src_lang       )
	@property
	def        lowerCamelCase__		(							self       )						->  str:
							return self._src_lang
	@src_lang.setter
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  None:
							lowercase_      :   int        =			new_src_lang
							self.set_src_lang_special_tokens(self._src_lang       )
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							if token_ids_a is None:
													return self.prefix_tokens + token_ids_a + self.suffix_tokens
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  List[int]:
							lowercase_      :   Union[str, Any]        =			[self.sep_token_id]
							lowercase_      :   int        =			[self.cls_token_id]

							if token_ids_a is None:
													return len(cls + token_ids_a + sep       ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      **_lowercase       )						->  Union[str, Any]:
							if src_lang is None or tgt_lang is None:
													raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'       )
							lowercase_      :   int        =			src_lang
							lowercase_      :   Any        =			self(_lowercase  ,      add_special_tokens=_lowercase  ,      return_tensors=_lowercase  ,      **_lowercase       )
							lowercase_      :   Optional[int]        =			self.convert_tokens_to_ids(_lowercase       )
							lowercase_      :   Optional[int]        =			tgt_lang_id
							return inputs
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = "en_XX"  ,      _lowercase = None  ,      _lowercase = "ro_RO"  ,      **_lowercase  ,      )						->  BatchEncoding:
							lowercase_      :   Optional[int]        =			src_lang
							lowercase_      :   Union[str, Any]        =			tgt_lang
							return super().prepare_seqaseq_batch(_lowercase  ,      _lowercase  ,      **_lowercase       )
	def        lowerCamelCase__		(							self       )						->  Union[str, Any]:
							return self.set_src_lang_special_tokens(self.src_lang       )
	def        lowerCamelCase__		(							self       )						->  int:
							return self.set_tgt_lang_special_tokens(self.tgt_lang       )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  None:
							lowercase_      :   str        =			self.convert_tokens_to_ids(_lowercase       )
							lowercase_      :   Any        =			[]
							lowercase_      :   Optional[int]        =			[self.eos_token_id, self.cur_lang_code]

							lowercase_      :   Optional[int]        =			self.convert_ids_to_tokens(self.prefix_tokens       )
							lowercase_      :   Optional[int]        =			self.convert_ids_to_tokens(self.suffix_tokens       )

							lowercase_      :   Any        =			processors.TemplateProcessing(
							    single=prefix_tokens_str + ['$A'] + suffix_tokens_str  ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str  ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,      self.prefix_tokens + self.suffix_tokens       )       )  ,      )
	def        lowerCamelCase__		(							self  ,      _lowercase       )						->  None:
							lowercase_      :   Union[str, Any]        =			self.convert_tokens_to_ids(_lowercase       )
							lowercase_      :   List[str]        =			[]
							lowercase_      :   Optional[int]        =			[self.eos_token_id, self.cur_lang_code]

							lowercase_      :   Any        =			self.convert_ids_to_tokens(self.prefix_tokens       )
							lowercase_      :   List[Any]        =			self.convert_ids_to_tokens(self.suffix_tokens       )

							lowercase_      :   Union[str, Any]        =			processors.TemplateProcessing(
							    single=prefix_tokens_str + ['$A'] + suffix_tokens_str  ,      pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str  ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str  ,      self.prefix_tokens + self.suffix_tokens       )       )  ,      )







	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase = None       )						->  Tuple[str]:
							if not self.can_save_slow_tokenizer:
													raise ValueError(
													    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
													    'tokenizer.'       )

							if not os.path.isdir(_lowercase       ):
													logger.error(f"Vocabulary path ({save_directory}) should be a directory."       )
													return
							lowercase_      :   Tuple        =			os.path.join(
							    _lowercase  ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(_lowercase       ):
													copyfile(self.vocab_file  ,      _lowercase       )

							return (out_vocab_file,)












'''simple docstring'''

from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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:      List[Any]  				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

A:      Union[str, Any]  				=				"\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to(\"cuda\")\n\n        >>> prompt = \"A red cartoon frog, 4k\"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-decoder\", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to(\"cuda\")\n\n        >>> init_image = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/frog.png\"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save(\"red_frog.png\")\n        ```\n"






def 	_UpperCAmelCase    (  a		:	Tuple						,			a		:	Union[str, Any]						,			a		:	List[Any]=8							)			->			Dict:





						"""simple docstring"""
						lowercase_      :   List[Any]        =			height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						lowercase_      :   List[str]        =			width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor






def 	_UpperCAmelCase    (  a		:	Any						,			a		:	Dict=5_1_2						,			a		:	Optional[Any]=5_1_2							)			->			Tuple:





						"""simple docstring"""
						lowercase_      :   int        =			pil_image.resize((w, h)						,			resample=Image.BICUBIC						,			reducing_gap=1							)
						lowercase_      :   int        =			np.array(pil_image.convert('RGB'							)							)
						lowercase_      :   Optional[int]        =			arr.astype(np.floataa							) / 1_27.5 - 1
						lowercase_      :   Any        =			np.transpose(a						,			[2, 0, 1]							)
						lowercase_      :   Any        =			torch.from_numpy(a							).unsqueeze(0							)
						return image






class 							__magic_name__     (	UpperCAmelCase_				):







	"""simple docstring"""



	def __init__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      )						->  List[Any]:
							super().__init__()

							self.register_modules(
							    unet=_lowercase  ,      scheduler=_lowercase  ,      movq=_lowercase  ,      )
							lowercase_      :   Dict        =			2 ** (len(self.movq.config.block_out_channels       ) - 1)
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase       )						->  int:
							# get the original timestep using init_timestep
							lowercase_      :   List[Any]        =			min(int(num_inference_steps * strength       )  ,      _lowercase       )

							lowercase_      :   Tuple        =			max(num_inference_steps - init_timestep  ,      0       )
							lowercase_      :   Optional[Any]        =			self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start
	def        lowerCamelCase__		(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase=None       )						->  Any:
							if not isinstance(_lowercase  ,      (torch.Tensor, PIL.Image.Image, list)       ):
													raise ValueError(
													    f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(_lowercase       )}"       )

							lowercase_      :   Dict        =			image.to(device=_lowercase  ,      dtype=_lowercase       )

							lowercase_      :   Dict        =			batch_size * num_images_per_prompt

							if image.shape[1] == 4:
													lowercase_      :   str        =			image

							else:
													if isinstance(_lowercase  ,      _lowercase       ) and len(_lowercase       ) != batch_size:
																			raise ValueError(
																			    f"You have passed a list of generators of length {len(_lowercase       )}, but requested an effective batch"
																			    f" size of {batch_size}. Make sure the batch size matches the length of the generators."       )

													elif isinstance(_lowercase  ,      _lowercase       ):
																			lowercase_      :   List[Any]        =			[
																			    self.movq.encode(image[i : i + 1]       ).latent_dist.sample(generator[i]       ) for i in range(_lowercase       )
																			]
																			lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
													else:
																			lowercase_      :   Union[str, Any]        =			self.movq.encode(_lowercase       ).latent_dist.sample(_lowercase       )

													lowercase_      :   str        =			self.movq.config.scaling_factor * init_latents

							lowercase_      :   int        =			torch.cat([init_latents]  ,      dim=0       )

							lowercase_      :   Dict        =			init_latents.shape
							lowercase_      :   Dict        =			randn_tensor(_lowercase  ,      generator=_lowercase  ,      device=_lowercase  ,      dtype=_lowercase       )

							# get latents
							lowercase_      :   List[str]        =			self.scheduler.add_noise(_lowercase  ,      _lowercase  ,      _lowercase       )

							lowercase_      :   Optional[Any]        =			init_latents

							return latents
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							if is_accelerate_available():
													from accelerate import cpu_offload
							else:
													raise ImportError('Please install accelerate via `pip install accelerate`'       )

							lowercase_      :   List[Any]        =			torch.device(f"cuda:{gpu_id}"       )

							lowercase_      :   Optional[Any]        =			[
							    self.unet,
							    self.movq,
							]
							for cpu_offloaded_model in models:
													if cpu_offloaded_model is not None:
																			cpu_offload(_lowercase  ,      _lowercase       )
	def        lowerCamelCase__		(							self  ,      _lowercase=0       )						->  int:
							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.'       )

							lowercase_      :   List[Any]        =			torch.device(f"cuda:{gpu_id}"       )

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

							lowercase_      :   Tuple        =			None
							for cpu_offloaded_model in [self.unet, self.movq]:
													lowercase_  ,      lowercase_      :   Dict        =			cpu_offload_with_hook(_lowercase  ,      _lowercase  ,      prev_module_hook=_lowercase       )

							# We'll offload the last model manually.
							lowercase_      :   List[str]        =			hook
	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def        lowerCamelCase__		(							self       )						->  List[str]:
							if not hasattr(self.unet  ,      '_hf_hook'       ):
													return self.device
							for module in self.unet.modules():
													if (
													    hasattr(_lowercase  ,      '_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(_lowercase       )
	def __call__(							self  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase = 512  ,      _lowercase = 512  ,      _lowercase = 100  ,      _lowercase = 4.0  ,      _lowercase = 0.3  ,      _lowercase = 1  ,      _lowercase = None  ,      _lowercase = "pil"  ,      _lowercase = True  ,      )						->  str:
							lowercase_      :   List[Any]        =			self._execution_device

							lowercase_      :   List[Any]        =			guidance_scale > 1.0

							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Union[str, Any]        =			torch.cat(_lowercase  ,      dim=0       )
							lowercase_      :   Optional[Any]        =			image_embeds.shape[0]
							if isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   List[str]        =			torch.cat(_lowercase  ,      dim=0       )

							if do_classifier_free_guidance:
													lowercase_      :   List[str]        =			image_embeds.repeat_interleave(_lowercase  ,      dim=0       )
													lowercase_      :   Dict        =			negative_image_embeds.repeat_interleave(_lowercase  ,      dim=0       )

							lowercase_      :   Dict        =			torch.cat([negative_image_embeds, image_embeds]  ,      dim=0       ).to(dtype=self.unet.dtype  ,      device=_lowercase       )

							if not isinstance(_lowercase  ,      _lowercase       ):
													lowercase_      :   Union[str, Any]        =			[image]
							if not all(isinstance(_lowercase  ,      (PIL.Image.Image, torch.Tensor)       ) for i in image       ):
													raise ValueError(
													    f"Input is in incorrect format: {[type(_lowercase       ) for i in image]}. Currently, we only support  PIL image and pytorch tensor"       )

							lowercase_      :   List[Any]        =			torch.cat([prepare_image(_lowercase  ,      _lowercase  ,      _lowercase       ) for i in image]  ,      dim=0       )
							lowercase_      :   Dict        =			image.to(dtype=image_embeds.dtype  ,      device=_lowercase       )

							lowercase_      :   Dict        =			self.movq.encode(_lowercase       )['latents']
							lowercase_      :   Optional[Any]        =			latents.repeat_interleave(_lowercase  ,      dim=0       )
							self.scheduler.set_timesteps(_lowercase  ,      device=_lowercase       )
							lowercase_  ,      lowercase_      :   str        =			self.get_timesteps(_lowercase  ,      _lowercase  ,      _lowercase       )
							lowercase_      :   int        =			timesteps[:1].repeat(batch_size * num_images_per_prompt       )
							lowercase_  ,      lowercase_      :   Union[str, Any]        =			downscale_height_and_width(_lowercase  ,      _lowercase  ,      self.movq_scale_factor       )
							lowercase_      :   List[str]        =			self.prepare_latents(
							    _lowercase  ,      _lowercase  ,      _lowercase  ,      _lowercase  ,      image_embeds.dtype  ,      _lowercase  ,      _lowercase       )
							for i, t in enumerate(self.progress_bar(_lowercase       )       ):
													# expand the latents if we are doing classifier free guidance
													lowercase_      :   int        =			torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

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

													if do_classifier_free_guidance:
																			lowercase_  ,      lowercase_      :   Dict        =			noise_pred.split(latents.shape[1]  ,      dim=1       )
																			lowercase_  ,      lowercase_      :   Optional[int]        =			noise_pred.chunk(2       )
																			lowercase_  ,      lowercase_      :   Tuple        =			variance_pred.chunk(2       )
																			lowercase_      :   List[str]        =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																			lowercase_      :   Tuple        =			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"]
													):
																			lowercase_  ,      lowercase_      :   List[str]        =			noise_pred.split(latents.shape[1]  ,      dim=1       )

													# compute the previous noisy sample x_t -> x_t-1
													lowercase_      :   Dict        =			self.scheduler.step(
													    _lowercase  ,      _lowercase  ,      _lowercase  ,      generator=_lowercase  ,      )[0]

							# post-processing
							lowercase_      :   Any        =			self.movq.decode(_lowercase  ,      force_not_quantize=_lowercase       )['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"]:
													lowercase_      :   Dict        =			image * 0.5 + 0.5
													lowercase_      :   Dict        =			image.clamp(0  ,      1       )
													lowercase_      :   int        =			image.cpu().permute(0  ,      2  ,      3  ,      1       ).float().numpy()

							if output_type == "pil":
													lowercase_      :   int        =			self.numpy_to_pil(_lowercase       )

							if not return_dict:
													return (image,)

							return ImagePipelineOutput(images=_lowercase       )
