





import gc
import unittest

import torch
from parameterized import parameterized

from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 					__A    (    lowerCAmelCase			,				lowerCAmelCase			,				unittest.TestCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						AutoencoderKL
			lowerCAmelCase_							     =						"""sample"""
			lowerCAmelCase_							     =						1e-2





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		4
				lowerCamelCase__								=		3
				lowerCamelCase__								=		(3_2, 3_2)

				lowerCamelCase__								=		floats_tensor((batch_size, num_channels) + sizes							).to(__lowerCAmelCase							)

				return {"sample": image}





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return (3, 3_2, 3_2)





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return (3, 3_2, 3_2)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		{
				    '''block_out_channels''': [3_2, 6_4],
				    '''in_channels''': 3,
				    '''out_channels''': 3,
				    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
				    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
				    '''latent_channels''': 4,
				}
				lowerCamelCase__								=		self.dummy_input
				return init_dict, inputs_dict





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				pass





			@unittest.skipIf(torch_device == '''mps'''       ,  '''Gradient checkpointing skipped on MPS'''							)
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		self.prepare_init_args_and_inputs_for_common()
				lowerCamelCase__								=		self.model_class(**__lowerCAmelCase							)
				model.to(__lowerCAmelCase							)

				assert not model.is_gradient_checkpointing and model.training

				lowerCamelCase__								=		model(**__lowerCAmelCase							).sample
				# run the backwards pass on the model. For backwards pass, for simplicity purpose,
				# we won't calculate the loss and rather backprop on out.sum()
				model.zero_grad()

				lowerCamelCase__								=		torch.randn_like(__lowerCAmelCase							)
				lowerCamelCase__								=		(out - labels).mean()
				loss.backward()

				# re-instantiate the model now enabling gradient checkpointing
				lowerCamelCase__								=		self.model_class(**__lowerCAmelCase							)
				# clone model
				model_a.load_state_dict(model.state_dict()							)
				model_a.to(__lowerCAmelCase							)
				model_a.enable_gradient_checkpointing()

				assert model_a.is_gradient_checkpointing and model_a.training

				lowerCamelCase__								=		model_a(**__lowerCAmelCase							).sample
				# run the backwards pass on the model. For backwards pass, for simplicity purpose,
				# we won't calculate the loss and rather backprop on out.sum()
				model_a.zero_grad()
				lowerCamelCase__								=		(out_a - labels).mean()
				loss_a.backward()

				# compare the output and parameters gradients
				self.assertTrue((loss - loss_a).abs() < 1E-5							)
				lowerCamelCase__								=		dict(model.named_parameters()							)
				lowerCamelCase__								=		dict(model_a.named_parameters()							)
				for name, param in named_params.items():
					self.assertTrue(torch_all_close(param.grad.data       ,  named_params_a[name].grad.data       ,  atol=5E-5							)							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__				,	lowerCamelCase__								=		AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''       ,  output_loading_info=__lowerCAmelCase							)
				self.assertIsNotNone(__lowerCAmelCase							)
				self.assertEqual(len(loading_info['''missing_keys''']							)       ,  0							)

				model.to(__lowerCAmelCase							)
				lowerCamelCase__								=		model(**self.dummy_input							)

				assert image is not None, "Make sure output is not None"





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		AutoencoderKL.from_pretrained('''fusing/autoencoder-kl-dummy'''							)
				lowerCamelCase__								=		model.to(__lowerCAmelCase							)
				model.eval()

				if torch_device == "mps":
					lowerCamelCase__								=		torch.manual_seed(0							)
				else:
					lowerCamelCase__								=		torch.Generator(device=__lowerCAmelCase							).manual_seed(0							)

				lowerCamelCase__								=		torch.randn(
				    1       ,  model.config.in_channels       ,  model.config.sample_size       ,  model.config.sample_size       ,  generator=torch.manual_seed(0							)       ,  )
				lowerCamelCase__								=		image.to(__lowerCAmelCase							)
				with torch.no_grad():
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  sample_posterior=__lowerCAmelCase       ,  generator=__lowerCAmelCase							).sample

				lowerCamelCase__								=		output[0, -1, -3:, -3:].flatten().cpu()

				# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
				# the expected output slices are not the same for CPU and GPU.
				if torch_device == "mps":
					lowerCamelCase__								=		torch.tensor(
					    [
					        -4.0078E-01,
					        -3.8323E-04,
					        -1.2681E-01,
					        -1.1462E-01,
					        2.0095E-01,
					        1.0893E-01,
					        -8.8247E-02,
					        -3.0361E-01,
					        -9.8644E-03,
					    ]							)
				elif torch_device == "cpu":
					lowerCamelCase__								=		torch.tensor(
					    [-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]							)
				else:
					lowerCamelCase__								=		torch.tensor(
					    [-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]							)

				self.assertTrue(torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  rtol=1E-2							)							)






@slow
class 					__A    (    unittest.TestCase ):

			'''simple docstring'''







			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return F'gaussian_noise_s={seed}_shape={"_".join([str(__lowerCAmelCase							) for s in shape]							)}.npy'





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase=0       ,  __lowerCAmelCase=(4, 3, 5_1_2, 5_1_2)       ,  __lowerCAmelCase=False							):



				'''simple docstring'''







				lowerCamelCase__								=		torch.floataa if fpaa else torch.floataa
				lowerCamelCase__								=		torch.from_numpy(load_hf_numpy(self.get_file_format(__lowerCAmelCase       ,  __lowerCAmelCase							)							)							).to(__lowerCAmelCase							).to(__lowerCAmelCase							)
				return image





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase="CompVis/stable-diffusion-v1-4"       ,  __lowerCAmelCase=False							):



				'''simple docstring'''







				lowerCamelCase__								=		'''fp16''' if fpaa else None
				lowerCamelCase__								=		torch.floataa if fpaa else torch.floataa

				lowerCamelCase__								=		AutoencoderKL.from_pretrained(
				    __lowerCAmelCase       ,  subfolder='''vae'''       ,  torch_dtype=__lowerCAmelCase       ,  revision=__lowerCAmelCase       ,  )
				model.to(__lowerCAmelCase							).eval()

				return model





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if torch_device == "mps":
					return torch.manual_seed(__lowerCAmelCase							)
				return torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)





			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
			        [4_7, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
			        # fmt: on
			    ]							)
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model()
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_generator(__lowerCAmelCase							)

				with torch.no_grad():
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  generator=__lowerCAmelCase       ,  sample_posterior=__lowerCAmelCase							).sample

				assert sample.shape == image.shape

				lowerCamelCase__								=		sample[-1, -2:, -2:, :2].flatten().float().cpu()
				lowerCamelCase__								=		torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice							)

				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=3E-3							)





			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
			        [4_7, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
			        # fmt: on
			    ]							)
			@require_torch_gpu
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model(fpaa=__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase       ,  fpaa=__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_generator(__lowerCAmelCase							)

				with torch.no_grad():
					lowerCamelCase__								=		model(__lowerCAmelCase       ,  generator=__lowerCAmelCase       ,  sample_posterior=__lowerCAmelCase							).sample

				assert sample.shape == image.shape

				lowerCamelCase__								=		sample[-1, -2:, :2, -2:].flatten().float().cpu()
				lowerCamelCase__								=		torch.tensor(__lowerCAmelCase							)

				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=1E-2							)





			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
			        [4_7, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
			        # fmt: on
			    ]							)
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model()
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase							)

				with torch.no_grad():
					lowerCamelCase__								=		model(__lowerCAmelCase							).sample

				assert sample.shape == image.shape

				lowerCamelCase__								=		sample[-1, -2:, -2:, :2].flatten().float().cpu()
				lowerCamelCase__								=		torch.tensor(expected_slice_mps if torch_device == '''mps''' else expected_slice							)

				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=3E-3							)





			@parameterized.expand(
			    [
			        # fmt: off
			        [1_3, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
			        [3_7, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
			        # fmt: on
			    ]							)
			@require_torch_gpu
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model()
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase       ,  shape=(3, 4, 6_4, 6_4)							)

				with torch.no_grad():
					lowerCamelCase__								=		model.decode(__lowerCAmelCase							).sample

				assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

				lowerCamelCase__								=		sample[-1, -2:, :2, -2:].flatten().cpu()
				lowerCamelCase__								=		torch.tensor(__lowerCAmelCase							)

				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=1E-3							)





			@parameterized.expand(
			    [
			        # fmt: off
			        [2_7, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
			        [1_6, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
			        # fmt: on
			    ]							)
			@require_torch_gpu
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model(fpaa=__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase       ,  shape=(3, 4, 6_4, 6_4)       ,  fpaa=__lowerCAmelCase							)

				with torch.no_grad():
					lowerCamelCase__								=		model.decode(__lowerCAmelCase							).sample

				assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

				lowerCamelCase__								=		sample[-1, -2:, :2, -2:].flatten().float().cpu()
				lowerCamelCase__								=		torch.tensor(__lowerCAmelCase							)

				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=5E-3							)





			@parameterized.expand([(1_3,), (1_6,), (2_7,)]							)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()       ,  reason='''xformers is not required when using PyTorch 2.0.'''							)
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model(fpaa=__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase       ,  shape=(3, 4, 6_4, 6_4)       ,  fpaa=__lowerCAmelCase							)

				with torch.no_grad():
					lowerCamelCase__								=		model.decode(__lowerCAmelCase							).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
					lowerCamelCase__								=		model.decode(__lowerCAmelCase							).sample

				assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=1E-1							)





			@parameterized.expand([(1_3,), (1_6,), (3_7,)]							)
			@require_torch_gpu
			@unittest.skipIf(not is_xformers_available()       ,  reason='''xformers is not required when using PyTorch 2.0.'''							)
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model()
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase       ,  shape=(3, 4, 6_4, 6_4)							)

				with torch.no_grad():
					lowerCamelCase__								=		model.decode(__lowerCAmelCase							).sample

				model.enable_xformers_memory_efficient_attention()
				with torch.no_grad():
					lowerCamelCase__								=		model.decode(__lowerCAmelCase							).sample

				assert list(sample.shape							) == [3, 3, 5_1_2, 5_1_2]

				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=1E-2							)





			@parameterized.expand(
			    [
			        # fmt: off
			        [3_3, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
			        [4_7, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
			        # fmt: on
			    ]							)
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_sd_vae_model()
				lowerCamelCase__								=		self.get_sd_image(__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_generator(__lowerCAmelCase							)

				with torch.no_grad():
					lowerCamelCase__								=		model.encode(__lowerCAmelCase							).latent_dist
					lowerCamelCase__								=		dist.sample(generator=__lowerCAmelCase							)

				assert list(sample.shape							) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]

				lowerCamelCase__								=		sample[0, -1, -3:, -3:].flatten().cpu()
				lowerCamelCase__								=		torch.tensor(__lowerCAmelCase							)

				lowerCamelCase__								=		3E-3 if torch_device != '''mps''' else 1E-2
				assert torch_all_close(__lowerCAmelCase       ,  __lowerCAmelCase       ,  atol=__lowerCAmelCase							)










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 						=      logging.get_logger(__name__)

_a 						=      "▁"

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

_a 						=      {
    "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 						=      {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
_a 						=      {
    "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 					__A    (    lowerCAmelCase ):

			'''simple docstring'''







			lowerCAmelCase_							     =						VOCAB_FILES_NAMES
			lowerCAmelCase_							     =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCAmelCase_							     =						PRETRAINED_VOCAB_FILES_MAP
			lowerCAmelCase_							     =						["""input_ids""", """attention_mask"""]

			lowerCAmelCase_							     =						[]
			lowerCAmelCase_							     =						[]





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



				'''simple docstring'''







				lowerCamelCase__								=		{} if sp_model_kwargs is None else sp_model_kwargs

				lowerCamelCase__								=		language_codes
				lowerCamelCase__								=		FAIRSEQ_LANGUAGE_CODES[language_codes]
				lowerCamelCase__								=		{lang_code: F'__{lang_code}__' for lang_code in fairseq_language_code}

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

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

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

				lowerCamelCase__								=		len(self.encoder							)

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

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

				lowerCamelCase__								=		num_madeup_words





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







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





			@property
			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				return self._src_lang





			@src_lang.setter
			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		new_src_lang
				self.set_src_lang_special_tokens(self._src_lang							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		[]
				lowerCamelCase__								=		''''''
				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(__lowerCAmelCase							) + token
						lowerCamelCase__								=		[]
					else:
						current_sub_tokens.append(__lowerCAmelCase							)
				out_string += self.sp_model.decode(__lowerCAmelCase							)
				return out_string.strip()





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = False							):



				'''simple docstring'''







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

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = 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 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		{self.convert_ids_to_tokens(__lowerCAmelCase							): i for i in range(self.vocab_size							)}
				vocab.update(self.added_tokens_encoder							)
				return vocab





			def __getstate__(    self							):



				'''simple docstring'''







				lowerCamelCase__								=		self.__dict__.copy()
				lowerCamelCase__								=		None
				return state





			def __setstate__(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		d

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

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





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = None							):



				'''simple docstring'''







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

				save_json(self.encoder       ,  __lowerCAmelCase							)

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

				return (str(__lowerCAmelCase							), str(__lowerCAmelCase							))





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase = "en"       ,  __lowerCAmelCase = None       ,  __lowerCAmelCase = "ro"       ,  **__lowerCAmelCase       ,  ):



				'''simple docstring'''







				lowerCamelCase__								=		src_lang
				lowerCamelCase__								=		tgt_lang
				self.set_src_lang_special_tokens(self.src_lang							)
				return super().prepare_seqaseq_batch(__lowerCAmelCase       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  __lowerCAmelCase       ,  **__lowerCAmelCase							):



				'''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'''							)
				lowerCamelCase__								=		src_lang
				lowerCamelCase__								=		self(__lowerCAmelCase       ,  add_special_tokens=__lowerCAmelCase       ,  **__lowerCAmelCase							)
				lowerCamelCase__								=		self.get_lang_id(__lowerCAmelCase							)
				lowerCamelCase__								=		tgt_lang_id
				return inputs





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.set_src_lang_special_tokens(self.src_lang							)





			def 		__lowerCamelCase				(    self							):



				'''simple docstring'''







				self.set_tgt_lang_special_tokens(self.tgt_lang							)





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_lang_token(__lowerCAmelCase							)
				lowerCamelCase__								=		self.lang_token_to_id[lang_token]
				lowerCamelCase__								=		[self.cur_lang_id]
				lowerCamelCase__								=		[self.eos_token_id]





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_lang_token(__lowerCAmelCase							)
				lowerCamelCase__								=		self.lang_token_to_id[lang_token]
				lowerCamelCase__								=		[self.cur_lang_id]
				lowerCamelCase__								=		[self.eos_token_id]





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				return self.lang_code_to_token[lang]





			def 		__lowerCamelCase				(    self       ,  __lowerCAmelCase							):



				'''simple docstring'''







				lowerCamelCase__								=		self.get_lang_token(__lowerCAmelCase							)
				return self.lang_token_to_id[lang_token]





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      sentencepiece.SentencePieceProcessor:







	'''simple docstring'''







	lowerCamelCase__								=		sentencepiece.SentencePieceProcessor(**__snake_case							)
	spm.Load(str(__snake_case							)							)
	return spm





def lowerCAmelCase__(__snake_case							)				->      Union[Dict, List]:







	'''simple docstring'''







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





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      None:







	'''simple docstring'''







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



