

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:
					_lowerCAmelCase  :      Optional[int]			 =None


_lowerCAmelCase  :      int			 =logging.get_logger(__name__)


_lowerCAmelCase  :      Dict			 ={"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCAmelCase  :      Union[str, Any]			 ={
    """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""",
    },
}

_lowerCAmelCase  :      int			 ={
    """facebook/mbart-large-en-ro""": 10_24,
    """facebook/mbart-large-cc25""": 10_24,
}

# fmt: off
_lowerCAmelCase  :      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 __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   VOCAB_FILES_NAMES
					__magic_name__        =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__magic_name__        =   PRETRAINED_VOCAB_FILES_MAP
					__magic_name__        =   ["input_ids", "attention_mask"]
					__magic_name__        =   MBartTokenizer

					__magic_name__        =   []
					__magic_name__        =   []





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

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

						UpperCAmelCase__:	int									= vocab_file
						UpperCAmelCase__:	Union[str, Any]									= False if not self.vocab_file else True

						UpperCAmelCase__:	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}						)
						UpperCAmelCase__:	Dict									= {
						    lang_code: self.convert_tokens_to_ids(lowerCamelCase__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						UpperCAmelCase__:	List[str]									= src_lang if src_lang is not None else "en_XX"
						UpperCAmelCase__:	str									= self.convert_tokens_to_ids(self._src_lang						)
						UpperCAmelCase__:	List[str]									= tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._src_lang





					@src_lang.setter
					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Any									= new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						UpperCAmelCase__:	str									= [self.sep_token_id]
						UpperCAmelCase__:	Union[str, Any]									= [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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						):
						if src_lang is None or tgt_lang is None:
							raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model"						)
						UpperCAmelCase__:	Optional[Any]									= src_lang
						UpperCAmelCase__:	Tuple									= self(lowerCamelCase__				,      add_special_tokens=lowerCamelCase__				,      return_tensors=lowerCamelCase__				,      **lowerCamelCase__						)
						UpperCAmelCase__:	List[str]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						UpperCAmelCase__:	Any									= tgt_lang_id
						return inputs





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = "en_XX"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "ro_RO"				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Dict									= src_lang
						UpperCAmelCase__:	Tuple									= tgt_lang
						return super().prepare_seqaseq_batch(lowerCamelCase__				,      lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						return self.set_src_lang_special_tokens(self.src_lang						)





					def    _UpperCAmelCase    (				self						):
						return self.set_tgt_lang_special_tokens(self.tgt_lang						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Union[str, Any]									= self.convert_tokens_to_ids(lowerCamelCase__						)
						UpperCAmelCase__:	str									= []
						UpperCAmelCase__:	str									= [self.eos_token_id, self.cur_lang_code]

						UpperCAmelCase__:	int									= self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCAmelCase__:	Tuple									= self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCAmelCase__:	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    _UpperCAmelCase    (				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Dict									= self.convert_tokens_to_ids(lowerCamelCase__						)
						UpperCAmelCase__:	Union[str, Any]									= []
						UpperCAmelCase__:	str									= [self.eos_token_id, self.cur_lang_code]

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

						UpperCAmelCase__:	int									= 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    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = None						):
						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(lowerCamelCase__						):
							logger.error(F"Vocabulary path ({save_directory}) should be a directory."						)
							return
						UpperCAmelCase__:	List[str]									= os.path.join(
						    lowerCamelCase__				,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

						return (out_vocab_file,)


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,
)


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

_lowerCAmelCase  :      Any			 ="""
    Examples:
        ```py
        >>> import torch
        >>> import numpy as np

        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
        >>> from transformers import pipeline
        >>> from diffusers.utils import load_image


        >>> def make_hint(image, depth_estimator):
        ...     image = depth_estimator(image)[\"depth\"]
        ...     image = np.array(image)
        ...     image = image[:, :, None]
        ...     image = np.concatenate([image, image, image], axis=2)
        ...     detected_map = torch.from_numpy(image).float() / 255.0
        ...     hint = detected_map.permute(2, 0, 1)
        ...     return hint


        >>> depth_estimator = pipeline(\"depth-estimation\")

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
        ... )
        >>> pipe_prior = pipe_prior.to(\"cuda\")

        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
        ... )
        >>> pipe = pipe.to(\"cuda\")


        >>> img = load_image(
        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
        ...     \"/kandinsky/cat.png\"
        ... ).resize((768, 768))

        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")

        >>> prompt = \"A robot, 4k photo\"
        >>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"

        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)

        >>> image_emb, zero_image_emb = pipe_prior(
        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
        ... ).to_tuple()

        >>> images = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     hint=hint,
        ...     num_inference_steps=50,
        ...     generator=generator,
        ...     height=768,
        ...     width=768,
        ... ).images

        >>> images[0].save(\"robot_cat.png\")
        ```
"""



def 			_A	(      SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE	,SCREAMING_SNAKE_CASE=8      ):
	UpperCAmelCase__:	int									= height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	UpperCAmelCase__:	Optional[Any]									= width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



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

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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						if latents is None:
							UpperCAmelCase__:	Any									= randn_tensor(lowerCamelCase__				,      generator=lowerCamelCase__				,      device=lowerCamelCase__				,      dtype=lowerCamelCase__						)
						else:
							if latents.shape != shape:
								raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"						)
							UpperCAmelCase__:	Tuple									= latents.to(lowerCamelCase__						)

						UpperCAmelCase__:	Union[str, Any]									= latents * scheduler.init_noise_sigma
						return latents





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=0						):
						if is_accelerate_available():
							from accelerate import cpu_offload
						else:
							raise ImportError("Please install accelerate via `pip install accelerate`"						)

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

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





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__=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__:	Union[str, Any]									= torch.device(F"cuda:{gpu_id}"						)

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

						UpperCAmelCase__:	int									= None
						for cpu_offloaded_model in [self.unet, self.movq]:
							UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cpu_offload_with_hook(lowerCamelCase__				,      lowerCamelCase__				,      prev_module_hook=lowerCamelCase__						)

						# 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    _UpperCAmelCase    (				self						):
						if not hasattr(self.unet				,      "_hf_hook"						):
							return self.device
						for module in self.unet.modules():
							if (
							    hasattr(lowerCamelCase__				,      "_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(lowerCamelCase__						)
					def __call__(				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__ = 5_1_2				,      lowerCamelCase__ = 5_1_2				,      lowerCamelCase__ = 1_0_0				,      lowerCamelCase__ = 4.0				,      lowerCamelCase__ = 1				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__ = "pil"				,      lowerCamelCase__ = True				,      ):
						UpperCAmelCase__:	str									= self._execution_device

						UpperCAmelCase__:	List[Any]									= guidance_scale > 1.0

						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	List[Any]									= torch.cat(lowerCamelCase__				,      dim=0						)
						if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
							UpperCAmelCase__:	Any									= torch.cat(lowerCamelCase__				,      dim=0						)

						UpperCAmelCase__:	Dict									= image_embeds.shape[0] * num_images_per_prompt

						if do_classifier_free_guidance:
							UpperCAmelCase__:	Tuple									= image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	Union[str, Any]									= negative_image_embeds.repeat_interleave(lowerCamelCase__				,      dim=0						)
							UpperCAmelCase__:	Any									= hint.repeat_interleave(lowerCamelCase__				,      dim=0						)

						UpperCAmelCase__:	Union[str, Any]									= torch.cat([negative_image_embeds, image_embeds]				,      dim=0						).to(dtype=self.unet.dtype				,      device=lowerCamelCase__						)
						UpperCAmelCase__:	Dict									= torch.cat([hint, hint]				,      dim=0						).to(dtype=self.unet.dtype				,      device=lowerCamelCase__						)
						self.scheduler.set_timesteps(lowerCamelCase__				,      device=lowerCamelCase__						)
						UpperCAmelCase__:	Optional[Any]									= self.scheduler.timesteps

						UpperCAmelCase__:	Optional[int]									= self.movq.config.latent_channels

						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= downscale_height_and_width(lowerCamelCase__				,      lowerCamelCase__				,      self.movq_scale_factor						)

						# create initial latent
						UpperCAmelCase__:	Any									= self.prepare_latents(
						    (batch_size, num_channels_latents, height, width)				,      image_embeds.dtype				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      self.scheduler				,      )

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

							UpperCAmelCase__:	List[str]									= {"image_embeds": image_embeds, "hint": hint}
							UpperCAmelCase__:	Any									= self.unet(
							    sample=lowerCamelCase__				,      timestep=lowerCamelCase__				,      encoder_hidden_states=lowerCamelCase__				,      added_cond_kwargs=lowerCamelCase__				,      return_dict=lowerCamelCase__				,      )[0]

							if do_classifier_free_guidance:
								UpperCAmelCase__							,						UpperCAmelCase__:	str									= noise_pred.split(latents.shape[1]				,      dim=1						)
								UpperCAmelCase__							,						UpperCAmelCase__:	Any									= noise_pred.chunk(2						)
								UpperCAmelCase__							,						UpperCAmelCase__:	Any									= variance_pred.chunk(2						)
								UpperCAmelCase__:	List[str]									= noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
								UpperCAmelCase__:	Dict									= 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__:	List[Any]									= self.scheduler.step(
							    lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__				,      generator=lowerCamelCase__				,      )[0]
						# post-processing
						UpperCAmelCase__:	Union[str, Any]									= self.movq.decode(lowerCamelCase__				,      force_not_quantize=lowerCamelCase__						)["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__:	List[str]									= image * 0.5 + 0.5
							UpperCAmelCase__:	int									= image.clamp(0				,      1						)
							UpperCAmelCase__:	Tuple									= image.cpu().permute(0				,      2				,      3				,      1						).float().numpy()

						if output_type == "pil":
							UpperCAmelCase__:	List[str]									= self.numpy_to_pil(lowerCamelCase__						)

						if not return_dict:
							return (image,)

						return ImagePipelineOutput(images=lowerCamelCase__						)