"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()







class        UpperCamelCase		(			lowercase_						,      unittest.TestCase  ):
   lowercase							=  UnCLIPImageVariationPipeline
   lowercase							=  IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
   lowercase							=  IMAGE_VARIATION_BATCH_PARAMS

   lowercase							=  [
       'generator',
       'return_dict',
       'decoder_num_inference_steps',
       'super_res_num_inference_steps',
   ]
   lowercase							=  False
   @property
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return 32
   @property
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return 32
   @property
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        return self.time_input_dim
   @property
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        return self.time_input_dim * 4
   @property
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return 100
   @property
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
        return tokenizer
   @property
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        torch.manual_seed(0			)
        lowercase_	:				int													=	CLIPTextConfig(
            bos_token_id=0     ,eos_token_id=2     ,hidden_size=self.text_embedder_hidden_size     ,projection_dim=self.text_embedder_hidden_size     ,intermediate_size=37     ,layer_norm_eps=1e-05     ,num_attention_heads=4     ,num_hidden_layers=5     ,pad_token_id=1     ,vocab_size=1000     ,)
        return CLIPTextModelWithProjection(__UpperCamelCase			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        torch.manual_seed(0			)
        lowercase_	:				List[Any]													=	CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size     ,projection_dim=self.text_embedder_hidden_size     ,num_hidden_layers=5     ,num_attention_heads=4     ,image_size=32     ,intermediate_size=37     ,patch_size=1     ,)
        return CLIPVisionModelWithProjection(__UpperCamelCase			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        torch.manual_seed(0			)

        lowercase_	:				Tuple													=	{
            'clip_embeddings_dim': self.text_embedder_hidden_size,
            'time_embed_dim': self.time_embed_dim,
            'cross_attention_dim': self.cross_attention_dim,
        }

        lowercase_	:				int													=	UnCLIPTextProjModel(**__UpperCamelCase			)
        return model
   @property
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        torch.manual_seed(0			)

        lowercase_	:				Optional[int]													=	{
            'sample_size': 32,
            # RGB in channels
            'in_channels': 3,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 6,
            'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
            'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
            'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
            'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
            'layers_per_block': 1,
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': 'identity',
        }

        lowercase_	:				Dict													=	UNetaDConditionModel(**__UpperCamelCase			)
        return model
   @property
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        return {
            "sample_size": 64,
            "layers_per_block": 1,
            "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
            "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
            "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
            "in_channels": 6,
            "out_channels": 3,
        }
   @property
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        torch.manual_seed(0			)

        lowercase_	:				Tuple													=	UNetaDModel(**self.dummy_super_res_kwargs			)
        return model
   @property
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        torch.manual_seed(1			)

        lowercase_	:				Dict													=	UNetaDModel(**self.dummy_super_res_kwargs			)
        return model
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.dummy_decoder
        lowercase_	:				Optional[int]													=	self.dummy_text_proj
        lowercase_	:				Union[str, Any]													=	self.dummy_text_encoder
        lowercase_	:				Optional[Any]													=	self.dummy_tokenizer
        lowercase_	:				Optional[Any]													=	self.dummy_super_res_first
        lowercase_	:				List[Any]													=	self.dummy_super_res_last

        lowercase_	:				str													=	UnCLIPScheduler(
            variance_type='learned_range'     ,prediction_type='epsilon'     ,num_train_timesteps=1000     ,)

        lowercase_	:				List[Any]													=	UnCLIPScheduler(
            variance_type='fixed_small_log'     ,prediction_type='epsilon'     ,num_train_timesteps=1000     ,)

        lowercase_	:				Optional[Any]													=	CLIPImageProcessor(crop_size=32     ,size=32			)

        lowercase_	:				List[Any]													=	self.dummy_image_encoder

        return {
            "decoder": decoder,
            "text_encoder": text_encoder,
            "tokenizer": tokenizer,
            "text_proj": text_proj,
            "feature_extractor": feature_extractor,
            "image_encoder": image_encoder,
            "super_res_first": super_res_first,
            "super_res_last": super_res_last,
            "decoder_scheduler": decoder_scheduler,
            "super_res_scheduler": super_res_scheduler,
        }
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase=0     ,__UpperCamelCase=True			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				int													=	floats_tensor((1, 3, 32, 32)     ,rng=random.Random(__UpperCamelCase			)			).to(__UpperCamelCase			)
        if str(__UpperCamelCase			).startswith('mps'			):
             lowercase_	:				Any													=	torch.manual_seed(__UpperCamelCase			)
        else:
             lowercase_	:				Optional[Any]													=	torch.Generator(device=__UpperCamelCase			).manual_seed(__UpperCamelCase			)

        if pil_image:
             lowercase_	:				Dict													=	input_image * 0.5 + 0.5
             lowercase_	:				int													=	input_image.clamp(0     ,1			)
             lowercase_	:				str													=	input_image.cpu().permute(0     ,2     ,3     ,1			).float().numpy()
             lowercase_	:				Union[str, Any]													=	DiffusionPipeline.numpy_to_pil(__UpperCamelCase			)[0]

        return {
            "image": input_image,
            "generator": generator,
            "decoder_num_inference_steps": 2,
            "super_res_num_inference_steps": 2,
            "output_type": "np",
        }
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				List[str]													=	'cpu'

        lowercase_	:				Dict													=	self.get_dummy_components()

        lowercase_	:				Union[str, Any]													=	self.pipeline_class(**__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	pipe.to(__UpperCamelCase			)

        pipe.set_progress_bar_config(disable=__UpperCamelCase			)

        lowercase_	:				List[Any]													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	pipe(**__UpperCamelCase			)
        lowercase_	:				List[Any]													=	output.images

        lowercase_	:				int													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)

        lowercase_	:				str													=	pipe(
            **__UpperCamelCase     ,return_dict=__UpperCamelCase     ,)[0]

        lowercase_	:				List[str]													=	image[0, -3:, -3:, -1]
        lowercase_	:				int													=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        lowercase_	:				Any													=	np.array(
            [
                0.9997,
                0.0002,
                0.9997,
                0.9997,
                0.9969,
                0.0023,
                0.9997,
                0.9969,
                0.9970,
            ]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	'cpu'

        lowercase_	:				Any													=	self.get_dummy_components()

        lowercase_	:				Any													=	self.pipeline_class(**__UpperCamelCase			)
        lowercase_	:				str													=	pipe.to(__UpperCamelCase			)

        pipe.set_progress_bar_config(disable=__UpperCamelCase			)

        lowercase_	:				str													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)

        lowercase_	:				Any													=	pipe(**__UpperCamelCase			)
        lowercase_	:				List[str]													=	output.images

        lowercase_	:				Union[str, Any]													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)

        lowercase_	:				Tuple													=	pipe(
            **__UpperCamelCase     ,return_dict=__UpperCamelCase     ,)[0]

        lowercase_	:				Optional[int]													=	image[0, -3:, -3:, -1]
        lowercase_	:				Optional[Any]													=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        lowercase_	:				Any													=	np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	'cpu'

        lowercase_	:				Optional[Any]													=	self.get_dummy_components()

        lowercase_	:				Optional[int]													=	self.pipeline_class(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	pipe.to(__UpperCamelCase			)

        pipe.set_progress_bar_config(disable=__UpperCamelCase			)

        lowercase_	:				str													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)
        lowercase_	:				Any													=	[
            pipeline_inputs['image'],
            pipeline_inputs['image'],
        ]

        lowercase_	:				Optional[Any]													=	pipe(**__UpperCamelCase			)
        lowercase_	:				Tuple													=	output.images

        lowercase_	:				Dict													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)
        lowercase_	:				Any													=	[
            tuple_pipeline_inputs['image'],
            tuple_pipeline_inputs['image'],
        ]

        lowercase_	:				List[Any]													=	pipe(
            **__UpperCamelCase     ,return_dict=__UpperCamelCase     ,)[0]

        lowercase_	:				Optional[Any]													=	image[0, -3:, -3:, -1]
        lowercase_	:				Optional[Any]													=	image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (2, 64, 64, 3)

        lowercase_	:				Optional[Any]													=	np.array(
            [
                0.9997,
                0.9989,
                0.0008,
                0.0021,
                0.9960,
                0.0018,
                0.0014,
                0.0002,
                0.9933,
            ]			)

        assert np.abs(image_slice.flatten() - expected_slice			).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1e-2
   def 			_UpperCAmelCase   (			self			)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	torch.device('cpu'			)







        class        UpperCamelCase		:
           lowercase							=  1






        lowercase_	:				Any													=	self.get_dummy_components()

        lowercase_	:				Optional[int]													=	self.pipeline_class(**__UpperCamelCase			)
        lowercase_	:				Optional[int]													=	pipe.to(__UpperCamelCase			)

        pipe.set_progress_bar_config(disable=__UpperCamelCase			)

        lowercase_	:				Tuple													=	torch.Generator(device=__UpperCamelCase			).manual_seed(0			)
        lowercase_	:				Optional[Any]													=	pipe.decoder.dtype
        lowercase_	:				Tuple													=	1

        lowercase_	:				Dict													=	(
            batch_size,
            pipe.decoder.config.in_channels,
            pipe.decoder.config.sample_size,
            pipe.decoder.config.sample_size,
        )
        lowercase_	:				str													=	pipe.prepare_latents(
            __UpperCamelCase     ,dtype=__UpperCamelCase     ,device=__UpperCamelCase     ,generator=__UpperCamelCase     ,latents=__UpperCamelCase     ,scheduler=DummyScheduler()			)

        lowercase_	:				Any													=	(
            batch_size,
            pipe.super_res_first.config.in_channels // 2,
            pipe.super_res_first.config.sample_size,
            pipe.super_res_first.config.sample_size,
        )
        lowercase_	:				List[str]													=	pipe.prepare_latents(
            __UpperCamelCase     ,dtype=__UpperCamelCase     ,device=__UpperCamelCase     ,generator=__UpperCamelCase     ,latents=__UpperCamelCase     ,scheduler=DummyScheduler()			)

        lowercase_	:				Union[str, Any]													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)

        lowercase_	:				int													=	pipe(
            **__UpperCamelCase     ,decoder_latents=__UpperCamelCase     ,super_res_latents=__UpperCamelCase			).images

        lowercase_	:				Optional[Any]													=	self.get_dummy_inputs(__UpperCamelCase     ,pil_image=__UpperCamelCase			)
        # Don't pass image, instead pass embedding
        lowercase_	:				Union[str, Any]													=	pipeline_inputs.pop('image'			)
        lowercase_	:				Union[str, Any]													=	pipe.image_encoder(__UpperCamelCase			).image_embeds

        lowercase_	:				Tuple													=	pipe(
            **__UpperCamelCase     ,decoder_latents=__UpperCamelCase     ,super_res_latents=__UpperCamelCase     ,image_embeddings=__UpperCamelCase     ,).images

        # make sure passing text embeddings manually is identical
        assert np.abs(img_out_a - img_out_a			).max() < 1e-4
   @skip_mps
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	torch_device == 'cpu'

        # Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
        lowercase_	:				Any													=	1e-2

        self._test_attention_slicing_forward_pass(
            test_max_difference=__UpperCamelCase     ,expected_max_diff=__UpperCamelCase			)
   @skip_mps
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	torch_device == 'cpu'
        lowercase_	:				List[Any]													=	True
        lowercase_	:				str													=	[
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        self._test_inference_batch_single_identical(
            test_max_difference=__UpperCamelCase     ,relax_max_difference=__UpperCamelCase     ,additional_params_copy_to_batched_inputs=__UpperCamelCase     ,)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Tuple													=	[
            'decoder_num_inference_steps',
            'super_res_num_inference_steps',
        ]

        if torch_device == "mps":
             # TODO: MPS errors with larger batch sizes
             lowercase_	:				List[str]													=	[2, 3]
             self._test_inference_batch_consistent(
                 batch_sizes=__UpperCamelCase     ,additional_params_copy_to_batched_inputs=__UpperCamelCase     ,)
        else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=__UpperCamelCase			)
   @skip_mps
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return super().test_dict_tuple_outputs_equivalent()
   @skip_mps
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        return super().test_save_load_local()







   @skip_mps
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return super().test_save_load_optional_components()







@slow
@require_torch_gpu
class        UpperCamelCase		(			unittest.TestCase  ):
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'			)
        lowercase_	:				Any													=	load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'			)

        lowercase_	:				List[str]													=	UnCLIPImageVariationPipeline.from_pretrained(
            'kakaobrain/karlo-v1-alpha-image-variations'     ,torch_dtype=torch.floataa			)
        lowercase_	:				Optional[Any]													=	pipeline.to(__UpperCamelCase			)
        pipeline.set_progress_bar_config(disable=__UpperCamelCase			)

        lowercase_	:				Dict													=	torch.Generator(device='cpu'			).manual_seed(0			)
        lowercase_	:				Union[str, Any]													=	pipeline(
            __UpperCamelCase     ,generator=__UpperCamelCase     ,output_type='np'     ,)

        lowercase_	:				Any													=	output.images[0]

        assert image.shape == (256, 256, 3)

        assert_mean_pixel_difference(__UpperCamelCase     ,__UpperCamelCase     ,15			)


"""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:
       __SCREAMING_SNAKE_CASE    	=None


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)


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

__SCREAMING_SNAKE_CASE    	={
    "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",
    },
}

__SCREAMING_SNAKE_CASE    	={
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__SCREAMING_SNAKE_CASE    	=["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		(			lowercase_  ):
   lowercase							=  VOCAB_FILES_NAMES
   lowercase							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=  PRETRAINED_VOCAB_FILES_MAP
   lowercase							=  ['input_ids', 'attention_mask']
   lowercase							=  MBartTokenizer

   lowercase							=  []
   lowercase							=  []
   def __init__(			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="<unk>"     ,__UpperCamelCase="<pad>"     ,__UpperCamelCase="<mask>"     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				str													=	AddedToken(__UpperCamelCase     ,lstrip=__UpperCamelCase     ,rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else mask_token

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

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

        lowercase_	:				List[str]													=	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_	:				List[Any]													=	{
            lang_code: self.convert_tokens_to_ids(__UpperCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_	:				Dict													=	src_lang if src_lang is not None else 'en_XX'
        lowercase_	:				Union[str, Any]													=	self.convert_tokens_to_ids(self._src_lang			)
        lowercase_	:				int													=	tgt_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return self._src_lang
   @src_lang.setter
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	new_src_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''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 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	[self.sep_token_id]
        lowercase_	:				str													=	[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     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''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_	:				Dict													=	src_lang
        lowercase_	:				List[Any]													=	self(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,return_tensors=__UpperCamelCase     ,**__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Dict													=	tgt_lang_id
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = "en_XX"     ,__UpperCamelCase = None     ,__UpperCamelCase = "ro_RO"     ,**__UpperCamelCase     ,)	->      BatchEncoding:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	src_lang
        lowercase_	:				List[Any]													=	tgt_lang
        return super().prepare_seqaseq_batch(__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.set_src_lang_special_tokens(self.src_lang			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return self.set_tgt_lang_special_tokens(self.tgt_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Any													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	[]
        lowercase_	:				int													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				Any													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				int													=	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 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				str													=	[]
        lowercase_	:				Dict													=	[self.eos_token_id, self.cur_lang_code]

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

        lowercase_	:				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     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      Tuple[str]:


        '''simple docstring'''


        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(__UpperCamelCase			):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''			)
             return
        lowercase_	:				Tuple													=	os.path.join(
            __UpperCamelCase     ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

        return (out_vocab_file,)

