

"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin





@skip_mps
class    lowerCamelCase						(A__			,A__			,unittest.TestCase			):
			lowerCamelCase__   :		Optional[Any]							=							IFPipeline
			lowerCamelCase__   :		Dict							=							TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
			lowerCamelCase__   :		List[str]							=							TEXT_TO_IMAGE_BATCH_PARAMS
			lowerCamelCase__   :		int							=							PipelineTesterMixin.required_optional_params - {'latents'}

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Dict:
						return self._get_dummy_components()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						Optional[int]=0       )   ->    Optional[int]:
						if str(__UpperCAmelCase       ).startswith("""mps"""       ):
									SCREAMING_SNAKE_CASE__								=      torch.manual_seed(__UpperCAmelCase       )
						else:
									SCREAMING_SNAKE_CASE__								=      torch.Generator(device=__UpperCAmelCase       ).manual_seed(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      {
						    """prompt""": """A painting of a squirrel eating a burger""",
						    """generator""": generator,
						    """num_inference_steps""": 2,
						    """output_type""": """numpy""",
						}

						return inputs

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Any:
						self._test_save_load_optional_components()

			@unittest.skipIf(torch_device != """cuda"""    ,				reason="""float16 requires CUDA"""       )
			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[int]       )   ->    Union[str, Any]:
						# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
						super().test_save_load_floataa(expected_max_diff=1e-1       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    Union[str, Any]:
						self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						self._test_save_load_local()

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Union[str, Any]:
						self._test_inference_batch_single_identical(
						    expected_max_diff=1e-2    ,				)





			@unittest.skipIf(
			    torch_device != """cuda""" or not is_xformers_available()    ,				reason="""XFormers attention is only available with CUDA and `xformers` installed"""    ,				)
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Tuple:
						self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )





@slow
@require_torch_gpu
class    lowerCamelCase						(unittest.TestCase			):

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Dict:
						# clean up the VRAM after each test
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[Any]:
						# if

						SCREAMING_SNAKE_CASE__								=      IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0"""    ,				variant="""fp16"""    ,				torch_dtype=torch.floataa       )

						SCREAMING_SNAKE_CASE__								=      IFSuperResolutionPipeline.from_pretrained(
						    """DeepFloyd/IF-II-L-v1.0"""    ,				variant="""fp16"""    ,				torch_dtype=torch.floataa    ,				text_encoder=__UpperCAmelCase    ,				tokenizer=__UpperCAmelCase       )

						# pre compute text embeddings and remove T5 to save memory

						pipe_a.text_encoder.to("""cuda"""       )

						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      pipe_a.encode_prompt("""anime turtle"""    ,				device="""cuda"""       )

						del pipe_a.tokenizer
						del pipe_a.text_encoder
						gc.collect()

						SCREAMING_SNAKE_CASE__								=      None
						SCREAMING_SNAKE_CASE__								=      None

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )

						self._test_if(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# img2img

						SCREAMING_SNAKE_CASE__								=      IFImgaImgPipeline(**pipe_a.components       )
						SCREAMING_SNAKE_CASE__								=      IFImgaImgSuperResolutionPipeline(**pipe_a.components       )

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )

						self._test_if_imgaimg(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

						pipe_a.remove_all_hooks()
						pipe_a.remove_all_hooks()

						# inpainting

						SCREAMING_SNAKE_CASE__								=      IFInpaintingPipeline(**pipe_a.components       )
						SCREAMING_SNAKE_CASE__								=      IFInpaintingSuperResolutionPipeline(**pipe_a.components       )

						pipe_a.enable_model_cpu_offload()
						pipe_a.enable_model_cpu_offload()

						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )
						pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()       )

						self._test_if_inpainting(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int       )   ->    Union[str, Any]:
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__								=      torch.Generator(device="""cpu"""       ).manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe_a(
						    prompt_embeds=__UpperCAmelCase    ,				negative_prompt_embeds=__UpperCAmelCase    ,				num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__								=      torch.cuda.max_memory_allocated()
						assert mem_bytes < 1_3 * 1_0**9

						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"""       )
						assert_mean_pixel_difference(__UpperCAmelCase    ,				__UpperCAmelCase       )

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__								=      torch.Generator(device="""cpu"""       ).manual_seed(0       )

						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(0       )       ).to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      pipe_a(
						    prompt_embeds=__UpperCAmelCase    ,				negative_prompt_embeds=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				num_inference_steps=2    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)

						SCREAMING_SNAKE_CASE__								=      torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 1_0**9

						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"""       )
						assert_mean_pixel_difference(__UpperCAmelCase    ,				__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						Tuple    ,				__UpperCAmelCase				:						Any    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						List[Any]       )   ->    List[Any]:
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(0       )       ).to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.Generator(device="""cpu"""       ).manual_seed(0       )

						SCREAMING_SNAKE_CASE__								=      pipe_a(
						    prompt_embeds=__UpperCAmelCase    ,				negative_prompt_embeds=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__								=      torch.cuda.max_memory_allocated()
						assert mem_bytes < 1_0 * 1_0**9

						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"""       )
						assert_mean_pixel_difference(__UpperCAmelCase    ,				__UpperCAmelCase       )

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__								=      torch.Generator(device="""cpu"""       ).manual_seed(0       )

						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 2_5_6, 2_5_6)    ,				rng=random.Random(0       )       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(0       )       ).to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      pipe_a(
						    prompt_embeds=__UpperCAmelCase    ,				negative_prompt_embeds=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				original_image=__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				num_inference_steps=2    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)

						SCREAMING_SNAKE_CASE__								=      torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 1_0**9

						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"""       )
						assert_mean_pixel_difference(__UpperCAmelCase    ,				__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						List[Any]       )   ->    int:
						# pipeline 1

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(0       )       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(1       )       ).to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      torch.Generator(device="""cpu"""       ).manual_seed(0       )
						SCREAMING_SNAKE_CASE__								=      pipe_a(
						    prompt_embeds=__UpperCAmelCase    ,				negative_prompt_embeds=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				num_inference_steps=2    ,				generator=__UpperCAmelCase    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (6_4, 6_4, 3)

						SCREAMING_SNAKE_CASE__								=      torch.cuda.max_memory_allocated()
						assert mem_bytes < 1_0 * 1_0**9

						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"""       )
						assert_mean_pixel_difference(__UpperCAmelCase    ,				__UpperCAmelCase       )

						# pipeline 2

						_start_torch_memory_measurement()

						SCREAMING_SNAKE_CASE__								=      torch.Generator(device="""cpu"""       ).manual_seed(0       )

						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 6_4, 6_4)    ,				rng=random.Random(0       )       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 2_5_6, 2_5_6)    ,				rng=random.Random(0       )       ).to(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      floats_tensor((1, 3, 2_5_6, 2_5_6)    ,				rng=random.Random(1       )       ).to(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      pipe_a(
						    prompt_embeds=__UpperCAmelCase    ,				negative_prompt_embeds=__UpperCAmelCase    ,				image=__UpperCAmelCase    ,				mask_image=__UpperCAmelCase    ,				original_image=__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				num_inference_steps=2    ,				output_type="""np"""    ,				)

						SCREAMING_SNAKE_CASE__								=      output.images[0]

						assert image.shape == (2_5_6, 2_5_6, 3)

						SCREAMING_SNAKE_CASE__								=      torch.cuda.max_memory_allocated()
						assert mem_bytes < 4 * 1_0**9

						SCREAMING_SNAKE_CASE__								=      load_numpy(
						    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"""       )
						assert_mean_pixel_difference(__UpperCAmelCase    ,				__UpperCAmelCase       )

def 	A       (     ):




			'''simple docstring'''


			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()





"""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


A_							:			Union[str, Any]											= logging.get_logger(__name__)

A_							:			Tuple											= "▁"

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

A_							:			Dict											= {
    "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",
    },
}

A_							:			Dict											= {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
A_							:			Any											= {
    "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						(A__			):
			lowerCamelCase__   :		Optional[int]							=							VOCAB_FILES_NAMES
			lowerCamelCase__   :		Any							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase__   :		List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase__   :		Tuple							=							['input_ids', 'attention_mask']

			lowerCamelCase__   :		List[int]							=							[]
			lowerCamelCase__   :		List[int]							=							[]

			def __init__(					self				:						List[str]    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict=None    ,				__UpperCAmelCase				:						Optional[Any]=None    ,				__UpperCAmelCase				:						int="<s>"    ,				__UpperCAmelCase				:						Optional[int]="</s>"    ,				__UpperCAmelCase				:						Any="</s>"    ,				__UpperCAmelCase				:						Any="<pad>"    ,				__UpperCAmelCase				:						str="<unk>"    ,				__UpperCAmelCase				:						Dict="m2m100"    ,				__UpperCAmelCase				:						Optional[Dict[str, Any]] = None    ,				__UpperCAmelCase				:						str=8    ,				**__UpperCAmelCase				:						str    ,				)   ->    None:
						SCREAMING_SNAKE_CASE__								=      {} if sp_model_kwargs is None else sp_model_kwargs

						SCREAMING_SNAKE_CASE__								=      language_codes
						SCREAMING_SNAKE_CASE__								=      FAIRSEQ_LANGUAGE_CODES[language_codes]
						SCREAMING_SNAKE_CASE__								=      {lang_code: F"""__{lang_code}__""" for lang_code in fairseq_language_code}

						SCREAMING_SNAKE_CASE__								=      kwargs.get("""additional_special_tokens"""    ,				[]       )
						kwargs["additional_special_tokens"] += [
						    self.get_lang_token(__UpperCAmelCase       )
						    for lang_code in fairseq_language_code
						    if self.get_lang_token(__UpperCAmelCase       ) not in kwargs["additional_special_tokens"]
						]

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

						SCREAMING_SNAKE_CASE__								=      vocab_file
						SCREAMING_SNAKE_CASE__								=      load_json(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.encoder.items()}
						SCREAMING_SNAKE_CASE__								=      spm_file
						SCREAMING_SNAKE_CASE__								=      load_spm(__UpperCAmelCase    ,				self.sp_model_kwargs       )

						SCREAMING_SNAKE_CASE__								=      len(self.encoder       )

						SCREAMING_SNAKE_CASE__								=      {
						    self.get_lang_token(__UpperCAmelCase       ): self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase       )
						}
						SCREAMING_SNAKE_CASE__								=      {lang_code: self.encoder_size + i for i, lang_code in enumerate(__UpperCAmelCase       )}
						SCREAMING_SNAKE_CASE__								=      {v: k for k, v in self.lang_token_to_id.items()}

						SCREAMING_SNAKE_CASE__								=      src_lang if src_lang is not None else """en"""
						SCREAMING_SNAKE_CASE__								=      tgt_lang
						SCREAMING_SNAKE_CASE__								=      self.get_lang_id(self._src_lang       )
						self.set_src_lang_special_tokens(self._src_lang       )

						SCREAMING_SNAKE_CASE__								=      num_madeup_words

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    int:
						return len(self.encoder       ) + len(self.lang_token_to_id       )

			@property
			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    str:
						return self._src_lang

			@src_lang.setter
			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						str       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      new_src_lang
						self.set_src_lang_special_tokens(self._src_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						str       )   ->    List[str]:
						return self.sp_model.encode(__UpperCAmelCase    ,				out_type=__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						Tuple       )   ->    Tuple:
						if token in self.lang_token_to_id:
									return self.lang_token_to_id[token]
						return self.encoder.get(__UpperCAmelCase    ,				self.encoder[self.unk_token]       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    str:
						if index in self.id_to_lang_token:
									return self.id_to_lang_token[index]
						return self.decoder.get(__UpperCAmelCase    ,				self.unk_token       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Optional[Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      []
						SCREAMING_SNAKE_CASE__								=      """"""
						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(__UpperCAmelCase       ) + token
												SCREAMING_SNAKE_CASE__								=      []
									else:
												current_sub_tokens.append(__UpperCAmelCase       )
						out_string += self.sp_model.decode(__UpperCAmelCase       )
						return out_string.strip()

			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = None    ,				__UpperCAmelCase				:						bool = False       )   ->    List[int]:

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

						SCREAMING_SNAKE_CASE__								=      [1] * len(self.prefix_tokens       )
						SCREAMING_SNAKE_CASE__								=      [1] * len(self.suffix_tokens       )
						if token_ids_a is None:
									return prefix_ones + ([0] * len(__UpperCAmelCase       )) + suffix_ones
						return prefix_ones + ([0] * len(__UpperCAmelCase       )) + ([0] * len(__UpperCAmelCase       )) + suffix_ones

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[int]    ,				__UpperCAmelCase				:						Optional[List[int]] = 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 					SCREAMING_SNAKE_CASE				(					self				:						Dict       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      {self.convert_ids_to_tokens(__UpperCAmelCase       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab

			def __getstate__(					self				:						Union[str, Any]       )   ->    Dict:
						SCREAMING_SNAKE_CASE__								=      self.__dict__.copy()
						SCREAMING_SNAKE_CASE__								=      None
						return state

			def __setstate__(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Dict       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      d

						# for backward compatibility
						if not hasattr(self    ,				"""sp_model_kwargs"""       ):
									SCREAMING_SNAKE_CASE__								=      {}

						SCREAMING_SNAKE_CASE__								=      load_spm(self.spm_file    ,				self.sp_model_kwargs       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple    ,				__UpperCAmelCase				:						str    ,				__UpperCAmelCase				:						Optional[str] = None       )   ->    Tuple[str]:
						SCREAMING_SNAKE_CASE__								=      Path(__UpperCAmelCase       )
						if not save_dir.is_dir():
									raise OSError(F"""{save_directory} should be a directory"""       )
						SCREAMING_SNAKE_CASE__								=      save_dir / (
						    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
						)
						SCREAMING_SNAKE_CASE__								=      save_dir / (
						    (filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
						)

						save_json(self.encoder    ,				__UpperCAmelCase       )

						if os.path.abspath(self.spm_file       ) != os.path.abspath(__UpperCAmelCase       ) and os.path.isfile(self.spm_file       ):
									copyfile(self.spm_file    ,				__UpperCAmelCase       )
						elif not os.path.isfile(self.spm_file       ):
									with open(__UpperCAmelCase    ,				"""wb"""       ) as fi:
												SCREAMING_SNAKE_CASE__								=      self.sp_model.serialized_model_proto()
												fi.write(__UpperCAmelCase       )

						return (str(__UpperCAmelCase       ), str(__UpperCAmelCase       ))

			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						str = "en"    ,				__UpperCAmelCase				:						Optional[List[str]] = None    ,				__UpperCAmelCase				:						str = "ro"    ,				**__UpperCAmelCase				:						str    ,				)   ->    BatchEncoding:
						SCREAMING_SNAKE_CASE__								=      src_lang
						SCREAMING_SNAKE_CASE__								=      tgt_lang
						self.set_src_lang_special_tokens(self.src_lang       )
						return super().prepare_seqaseq_batch(__UpperCAmelCase    ,				__UpperCAmelCase    ,				**__UpperCAmelCase       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						Optional[str]    ,				__UpperCAmelCase				:						Optional[str]    ,				**__UpperCAmelCase				:						Tuple       )   ->    str:
						if src_lang is None or tgt_lang is None:
									raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""       )
						SCREAMING_SNAKE_CASE__								=      src_lang
						SCREAMING_SNAKE_CASE__								=      self(__UpperCAmelCase    ,				add_special_tokens=__UpperCAmelCase    ,				**__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.get_lang_id(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      tgt_lang_id
						return inputs

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    int:
						self.set_src_lang_special_tokens(self.src_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						str       )   ->    str:
						self.set_tgt_lang_special_tokens(self.tgt_lang       )

			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						str       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      self.get_lang_token(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.lang_token_to_id[lang_token]
						SCREAMING_SNAKE_CASE__								=      [self.cur_lang_id]
						SCREAMING_SNAKE_CASE__								=      [self.eos_token_id]

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						str       )   ->    None:
						SCREAMING_SNAKE_CASE__								=      self.get_lang_token(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.lang_token_to_id[lang_token]
						SCREAMING_SNAKE_CASE__								=      [self.cur_lang_id]
						SCREAMING_SNAKE_CASE__								=      [self.eos_token_id]

			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						str       )   ->    str:
						return self.lang_code_to_token[lang]





			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						str       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.get_lang_token(__UpperCAmelCase       )
						return self.lang_token_to_id[lang_token]

def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      sentencepiece.SentencePieceProcessor(**snake_case__    )
			spm.Load(str(snake_case__    )    )
			return spm

def 	A       (     snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				"""r"""    ) as f:
						return json.load(snake_case__    )




def 	A       (     snake_case__		,				snake_case__    ):




			'''simple docstring'''


			with open(snake_case__		,				"""w"""    ) as f:
						json.dump(snake_case__		,				snake_case__		,				indent=2    )


