

"""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,
)


_lowercase					:     Optional[int]				 =		logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase					:     str				 =		"\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)[\"depth\"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline(\"depth-estimation\")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to(\"cuda\")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to(\"cuda\")\n\n\n        >>> img = load_image(\n        ...     \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"\n        ...     \"/kandinsky/cat.png\"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")\n\n        >>> prompt = \"A robot, 4k photo\"\n        >>> 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\"\n\n        >>> generator = torch.Generator(device=\"cuda\").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save(\"robot_cat.png\")\n        ```\n"





def 	lowercase__		(    snake_case_					:Any       ,						snake_case_					:str       ,						snake_case_					:Union[str, Any]=8  ):
	__UpperCAmelCase							=      height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	__UpperCAmelCase							=      width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor




class 			_UpperCAmelCase					(				lowerCamelCase_    ):



	def __init__(   self			:    str       ,  _lowercase			:    Optional[int]       ,  _lowercase			:    int       ,  _lowercase			:    Any       ,  ):
		super().__init__()

		self.register_modules(
		    unet=__SCREAMING_SNAKE_CASE       ,  scheduler=__SCREAMING_SNAKE_CASE       ,  movq=__SCREAMING_SNAKE_CASE       ,  )
		__UpperCAmelCase							=      2 ** (len(self.movq.config.block_out_channels		) - 1)



	def     a   (   self			:    Dict       ,  _lowercase			:    List[Any]       ,  _lowercase			:    Tuple       ,  _lowercase			:    Dict       ,  _lowercase			:    Optional[Any]       ,  _lowercase			:    Tuple       ,  _lowercase			:    List[str]		):





		if latents is None:
			__UpperCAmelCase							=      randn_tensor(__SCREAMING_SNAKE_CASE       ,  generator=__SCREAMING_SNAKE_CASE       ,  device=__SCREAMING_SNAKE_CASE       ,  dtype=__SCREAMING_SNAKE_CASE		)
		else:
			if latents.shape != shape:
				raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}'''		)
			__UpperCAmelCase							=      latents.to(__SCREAMING_SNAKE_CASE		)

		__UpperCAmelCase							=      latents * scheduler.init_noise_sigma
		return latents



	def     a   (   self			:    int       ,  _lowercase			:    Optional[Any]=0		):





		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.movq,
		]
		for cpu_offloaded_model in models:
			if cpu_offloaded_model is not None:
				cpu_offload(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE		)



	def     a   (   self			:    Optional[Any]       ,  _lowercase			:    int=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							=      torch.device(F'''cuda:{gpu_id}'''		)

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

		__UpperCAmelCase							=      None
		for cpu_offloaded_model in [self.unet, self.movq]:
			__UpperCAmelCase							=      cpu_offload_with_hook(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  prev_module_hook=__SCREAMING_SNAKE_CASE		)

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



	@property
	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
	def     a   (   self			:    int		):





		if not hasattr(self.unet       ,  '''_hf_hook'''		):
			return self.device
		for module in self.unet.modules():
			if (
			    hasattr(__SCREAMING_SNAKE_CASE       ,  '''_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(__SCREAMING_SNAKE_CASE		)
	def __call__(   self			:    str       ,  _lowercase			:    Union[str, Any]       ,  _lowercase			:    List[str]       ,  _lowercase			:    Optional[int]       ,  _lowercase			:    int = 5_12       ,  _lowercase			:    Any = 5_12       ,  _lowercase			:    List[Any] = 1_00       ,  _lowercase			:    Optional[Any] = 4.0       ,  _lowercase			:    Optional[int] = 1       ,  _lowercase			:    int = None       ,  _lowercase			:    Union[str, Any] = None       ,  _lowercase			:    Optional[int] = "pil"       ,  _lowercase			:    int = True       ,  ):
		__UpperCAmelCase							=      self._execution_device

		__UpperCAmelCase							=      guidance_scale > 1.0

		if isinstance(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE		):
			__UpperCAmelCase							=      torch.cat(__SCREAMING_SNAKE_CASE       ,  dim=0		)
		if isinstance(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE		):
			__UpperCAmelCase							=      torch.cat(__SCREAMING_SNAKE_CASE       ,  dim=0		)
		if isinstance(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE		):
			__UpperCAmelCase							=      torch.cat(__SCREAMING_SNAKE_CASE       ,  dim=0		)

		__UpperCAmelCase							=      image_embeds.shape[0] * num_images_per_prompt

		if do_classifier_free_guidance:
			__UpperCAmelCase							=      image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE       ,  dim=0		)
			__UpperCAmelCase							=      negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE       ,  dim=0		)
			__UpperCAmelCase							=      hint.repeat_interleave(__SCREAMING_SNAKE_CASE       ,  dim=0		)

		__UpperCAmelCase							=      torch.cat([negative_image_embeds, image_embeds]       ,  dim=0		).to(dtype=self.unet.dtype       ,  device=__SCREAMING_SNAKE_CASE		)
		__UpperCAmelCase							=      torch.cat([hint, hint]       ,  dim=0		).to(dtype=self.unet.dtype       ,  device=__SCREAMING_SNAKE_CASE		)
		self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE       ,  device=__SCREAMING_SNAKE_CASE		)
		__UpperCAmelCase							=      self.scheduler.timesteps

		__UpperCAmelCase							=      self.movq.config.latent_channels

		__UpperCAmelCase							=      downscale_height_and_width(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  self.movq_scale_factor		)

		# create initial latent
		__UpperCAmelCase							=      self.prepare_latents(
		    (batch_size, num_channels_latents, height, width)       ,  image_embeds.dtype       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  self.scheduler       ,  )

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

			__UpperCAmelCase							=      {'''image_embeds''': image_embeds, '''hint''': hint}
			__UpperCAmelCase							=      self.unet(
			    sample=__SCREAMING_SNAKE_CASE       ,  timestep=__SCREAMING_SNAKE_CASE       ,  encoder_hidden_states=__SCREAMING_SNAKE_CASE       ,  added_cond_kwargs=__SCREAMING_SNAKE_CASE       ,  return_dict=__SCREAMING_SNAKE_CASE       ,  )[0]

			if do_classifier_free_guidance:
				__UpperCAmelCase							=      noise_pred.split(latents.shape[1]       ,  dim=1		)
				__UpperCAmelCase							=      noise_pred.chunk(2		)
				__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							=      noise_pred.split(latents.shape[1]       ,  dim=1		)

			# compute the previous noisy sample x_t -> x_t-1
			__UpperCAmelCase							=      self.scheduler.step(
			    __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE       ,  generator=__SCREAMING_SNAKE_CASE       ,  )[0]
		# post-processing
		__UpperCAmelCase							=      self.movq.decode(__SCREAMING_SNAKE_CASE       ,  force_not_quantize=__SCREAMING_SNAKE_CASE		)['''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(__SCREAMING_SNAKE_CASE		)

		if not return_dict:
			return (image,)

		return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE		)


'''simple docstring'''







import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowercase :  List[Any]    							=       logging.get_logger(__name__)

_lowercase :  List[Any]    							=       "▁"

_lowercase :  Tuple    							=       {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

_lowercase :  List[str]    							=       {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

_lowercase :  List[str]    							=       {
    "facebook/m2m100_418M": 1_0_2_4,
}

# fmt: off
_lowercase :  Tuple    							=       {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     VOCAB_FILES_NAMES
					lowerCAmelCase_        =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_        =     PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_        =     ['''input_ids''', '''attention_mask''']

					lowerCAmelCase_        =     []
					lowerCAmelCase_        =     []






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE="<s>"				,							__SCREAMING_SNAKE_CASE="</s>"				,							__SCREAMING_SNAKE_CASE="</s>"				,							__SCREAMING_SNAKE_CASE="<pad>"				,							__SCREAMING_SNAKE_CASE="<unk>"				,							__SCREAMING_SNAKE_CASE="m2m100"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE=8				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       {} if sp_model_kwargs is None else sp_model_kwargs

											lowercase_	:     List[Any]													=       language_codes
											lowercase_	:     Optional[int]													=       FAIRSEQ_LANGUAGE_CODES[language_codes]
											lowercase_	:     List[Any]													=       {lang_code: F'''__{lang_code}__''' for lang_code in fairseq_language_code}

											lowercase_	:     Union[str, Any]													=       kwargs.get('''additional_special_tokens'''				,							[]		)
											kwargs["additional_special_tokens"] += [
											    self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											    for lang_code in fairseq_language_code
											    if self.get_lang_token(__SCREAMING_SNAKE_CASE		) not in kwargs["additional_special_tokens"]
											]

											super().__init__(
											    src_lang=__SCREAMING_SNAKE_CASE				,							tgt_lang=__SCREAMING_SNAKE_CASE				,							bos_token=__SCREAMING_SNAKE_CASE				,							eos_token=__SCREAMING_SNAKE_CASE				,							sep_token=__SCREAMING_SNAKE_CASE				,							unk_token=__SCREAMING_SNAKE_CASE				,							pad_token=__SCREAMING_SNAKE_CASE				,							language_codes=__SCREAMING_SNAKE_CASE				,							sp_model_kwargs=self.sp_model_kwargs				,							num_madeup_words=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE				,							)

											lowercase_	:     int													=       vocab_file
											lowercase_	:     Any													=       load_json(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       {v: k for k, v in self.encoder.items()}
											lowercase_	:     Optional[int]													=       spm_file
											lowercase_	:     Any													=       load_spm(__SCREAMING_SNAKE_CASE				,							self.sp_model_kwargs		)

											lowercase_	:     List[Any]													=       len(self.encoder		)

											lowercase_	:     Dict													=       {
											    self.get_lang_token(__SCREAMING_SNAKE_CASE		): self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE		)
											}
											lowercase_	:     Optional[int]													=       {lang_code: self.encoder_size + i for i, lang_code in enumerate(__SCREAMING_SNAKE_CASE		)}
											lowercase_	:     Union[str, Any]													=       {v: k for k, v in self.lang_token_to_id.items()}

											lowercase_	:     Tuple													=       src_lang if src_lang is not None else '''en'''
											lowercase_	:     Optional[int]													=       tgt_lang
											lowercase_	:     Any													=       self.get_lang_id(self._src_lang		)
											self.set_src_lang_special_tokens(self._src_lang		)

											lowercase_	:     Dict													=       num_madeup_words






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return len(self.encoder		) + len(self.lang_token_to_id		)






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self._src_lang






					@src_lang.setter
					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     str													=       new_src_lang
											self.set_src_lang_special_tokens(self._src_lang		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.sp_model.encode(__SCREAMING_SNAKE_CASE				,							out_type=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if token in self.lang_token_to_id:
																	return self.lang_token_to_id[token]
											return self.encoder.get(__SCREAMING_SNAKE_CASE				,							self.encoder[self.unk_token]		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											if index in self.id_to_lang_token:
																	return self.id_to_lang_token[index]
											return self.decoder.get(__SCREAMING_SNAKE_CASE				,							self.unk_token		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       []
											lowercase_	:     List[str]													=       ''''''
											for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																							out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE		) + token
																							lowercase_	:     Optional[Any]													=       []
																	else:
																							current_sub_tokens.append(__SCREAMING_SNAKE_CASE		)
											out_string += self.sp_model.decode(__SCREAMING_SNAKE_CASE		)
											return out_string.strip()






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											if already_has_special_tokens:
																	return super().get_special_tokens_mask(
																	    token_ids_a=__SCREAMING_SNAKE_CASE				,							token_ids_a=__SCREAMING_SNAKE_CASE				,							already_has_special_tokens=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       [1] * len(self.prefix_tokens		)
											lowercase_	:     Any													=       [1] * len(self.suffix_tokens		)
											if token_ids_a is None:
																	return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones
											return prefix_ones + ([0] * len(__SCREAMING_SNAKE_CASE		)) + ([0] * len(__SCREAMING_SNAKE_CASE		)) + suffix_ones






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											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 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       {self.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE		): i for i in range(self.vocab_size		)}
											vocab.update(self.added_tokens_encoder		)
											return vocab






					def __getstate__(   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.__dict__.copy()
											lowercase_	:     List[Any]													=       None
											return state






					def __setstate__(   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Dict													=       d

											# for backward compatibility
											if not hasattr(self				,							'''sp_model_kwargs'''		):
																	lowercase_	:     List[Any]													=       {}

											lowercase_	:     Union[str, Any]													=       load_spm(self.spm_file				,							self.sp_model_kwargs		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       Path(__SCREAMING_SNAKE_CASE		)
											if not save_dir.is_dir():
																	raise OSError(F'''{save_directory} should be a directory'''		)
											lowercase_	:     Dict													=       save_dir / (
											    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
											)
											lowercase_	:     Dict													=       save_dir / (
											    (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
											)

											save_json(self.encoder				,							__SCREAMING_SNAKE_CASE		)

											if os.path.abspath(self.spm_file		) != os.path.abspath(__SCREAMING_SNAKE_CASE		) and os.path.isfile(self.spm_file		):
																	copyfile(self.spm_file				,							__SCREAMING_SNAKE_CASE		)
											elif not os.path.isfile(self.spm_file		):
																	with open(__SCREAMING_SNAKE_CASE				,							'''wb'''		) as fi:
																							lowercase_	:     int													=       self.sp_model.serialized_model_proto()
																							fi.write(__SCREAMING_SNAKE_CASE		)

											return (str(__SCREAMING_SNAKE_CASE		), str(__SCREAMING_SNAKE_CASE		))






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = "en"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "ro"				,							**__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       src_lang
											lowercase_	:     List[str]													=       tgt_lang
											self.set_src_lang_special_tokens(self.src_lang		)
											return super().prepare_seqaseq_batch(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											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_	:     Tuple													=       src_lang
											lowercase_	:     Any													=       self(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       self.get_lang_id(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Union[str, Any]													=       tgt_lang_id
											return inputs






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.set_src_lang_special_tokens(self.src_lang		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											self.set_tgt_lang_special_tokens(self.tgt_lang		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       self.lang_token_to_id[lang_token]
											lowercase_	:     Optional[Any]													=       [self.cur_lang_id]
											lowercase_	:     Union[str, Any]													=       [self.eos_token_id]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Any													=       self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       self.lang_token_to_id[lang_token]
											lowercase_	:     str													=       [self.cur_lang_id]
											lowercase_	:     List[str]													=       [self.eos_token_id]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.lang_code_to_token[lang]






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.get_lang_token(__SCREAMING_SNAKE_CASE		)
											return self.lang_token_to_id[lang_token]







def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: Dict[str, Any]							):



						"""simple docstring"""





						lowercase_	:     Optional[int]													=       sentencepiece.SentencePieceProcessor(**__SCREAMING_SNAKE_CASE							)
						spm.Load(str(__SCREAMING_SNAKE_CASE							)							)
						return spm
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						with open(__SCREAMING_SNAKE_CASE      ,      '''r'''							) as f:
												return json.load(__SCREAMING_SNAKE_CASE							)




def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						with open(__SCREAMING_SNAKE_CASE      ,      '''w'''							) as f:
												json.dump(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      indent=2							)



