





import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase=0.9_9_9			,      _UpperCAmelCase="cosine"			,      )						->  Optional[Any]:
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(_UpperCAmelCase				):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(_UpperCAmelCase				):
												return math.exp(t * -1_2.0				)

				else:
								raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""				)

				lowerCamelCase	 =[]
				for i in range(_UpperCAmelCase				):
								lowerCamelCase	 =i / num_diffusion_timesteps
								lowerCamelCase	 =(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(_UpperCAmelCase				) / alpha_bar_fn(_UpperCAmelCase				)			,      _UpperCAmelCase				)				)
				return torch.tensor(_UpperCAmelCase			,      dtype=torch.floataa				)




class       __A     (						a    ,					a    ):
				__A      =				[e.name for e in KarrasDiffusionSchedulers]
				__A      =				2


				@register_to_config
				def __init__(		self   , UpperCAmelCase_ = 1000   , UpperCAmelCase_ = 0.0_0_0_8_5   , UpperCAmelCase_ = 0.0_1_2   , UpperCAmelCase_ = "linear"   , UpperCAmelCase_ = None   , UpperCAmelCase_ = "epsilon"   , UpperCAmelCase_ = "linspace"   , UpperCAmelCase_ = 0   , ):
								if trained_betas is not None:
												lowerCamelCase	 =torch.tensor(UpperCAmelCase_   , dtype=torch.floataa							)
								elif beta_schedule == "linear":
												lowerCamelCase	 =torch.linspace(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , dtype=torch.floataa							)
								elif beta_schedule == "scaled_linear":
												# this schedule is very specific to the latent diffusion model.
												lowerCamelCase	 =(
												    torch.linspace(beta_start**0.5   , beta_end**0.5   , UpperCAmelCase_   , dtype=torch.floataa							) ** 2
												)
								elif beta_schedule == "squaredcos_cap_v2":
												# Glide cosine schedule
												lowerCamelCase	 =betas_for_alpha_bar(UpperCAmelCase_							)
								else:
												raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""							)

								lowerCamelCase	 =1.0 - self.betas
								lowerCamelCase	 =torch.cumprod(self.alphas   , dim=0							)

								#  set all values
								self.set_timesteps(UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_=None							):
								if schedule_timesteps is None:
												lowerCamelCase	 =self.timesteps

								lowerCamelCase	 =(schedule_timesteps == timestep).nonzero()

								# The sigma index that is taken for the **very** first `step`
								# is always the second index (or the last index if there is only 1)
								# This way we can ensure we don't accidentally skip a sigma in
								# case we start in the middle of the denoising schedule (e.g. for image-to-image)
								if len(self._index_counter							) == 0:
												lowerCamelCase	 =1 if len(UpperCAmelCase_							) > 1 else 0
								else:
												lowerCamelCase	 =timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_							) else timestep
												lowerCamelCase	 =self._index_counter[timestep_int]

								return indices[pos].item()


				@property
				def _snake_case       (		self							):
								# standard deviation of the initial noise distribution
								if self.config.timestep_spacing in ["linspace", "trailing"]:
												return self.sigmas.max()

								return (self.sigmas.max() ** 2 + 1) ** 0.5


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , ):
								lowerCamelCase	 =self.index_for_timestep(UpperCAmelCase_							)

								if self.state_in_first_order:
												lowerCamelCase	 =self.sigmas[step_index]
								else:
												lowerCamelCase	 =self.sigmas_interpol[step_index]

								lowerCamelCase	 =sample / ((sigma**2 + 1) ** 0.5)
								return sample


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_ = None   , UpperCAmelCase_ = None   , ):
								lowerCamelCase	 =num_inference_steps

								lowerCamelCase	 =num_train_timesteps or self.config.num_train_timesteps

								# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
								if self.config.timestep_spacing == "linspace":
												lowerCamelCase	 =np.linspace(0   , num_train_timesteps - 1   , UpperCAmelCase_   , dtype=UpperCAmelCase_							)[::-1].copy()
								elif self.config.timestep_spacing == "leading":
												lowerCamelCase	 =num_train_timesteps // self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												lowerCamelCase	 =(np.arange(0   , UpperCAmelCase_							) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_							)
												timesteps += self.config.steps_offset
								elif self.config.timestep_spacing == "trailing":
												lowerCamelCase	 =num_train_timesteps / self.num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# casting to int to avoid issues when num_inference_step is power of 3
												lowerCamelCase	 =(np.arange(UpperCAmelCase_   , 0   , -step_ratio							)).round().copy().astype(UpperCAmelCase_							)
												timesteps -= 1
								else:
												raise ValueError(
												    f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""							)

								lowerCamelCase	 =np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5							)
								lowerCamelCase	 =torch.from_numpy(np.log(UpperCAmelCase_							)							).to(UpperCAmelCase_							)

								lowerCamelCase	 =np.interp(UpperCAmelCase_   , np.arange(0   , len(UpperCAmelCase_							)							)   , UpperCAmelCase_							)
								lowerCamelCase	 =np.concatenate([sigmas, [0.0]]							).astype(np.floataa							)
								lowerCamelCase	 =torch.from_numpy(UpperCAmelCase_							).to(device=UpperCAmelCase_							)

								# interpolate sigmas
								lowerCamelCase	 =sigmas.log().lerp(sigmas.roll(1							).log()   , 0.5							).exp()

								lowerCamelCase	 =torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2							), sigmas[-1:]]							)
								lowerCamelCase	 =torch.cat(
								    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2							), sigmas_interpol[-1:]]							)

								if str(UpperCAmelCase_							).startswith("""mps"""							):
												# mps does not support float64
												lowerCamelCase	 =torch.from_numpy(UpperCAmelCase_							).to(UpperCAmelCase_   , dtype=torch.floataa							)
								else:
												lowerCamelCase	 =torch.from_numpy(UpperCAmelCase_							).to(UpperCAmelCase_							)

								# interpolate timesteps
								lowerCamelCase	 =self.sigma_to_t(UpperCAmelCase_							).to(UpperCAmelCase_   , dtype=timesteps.dtype							)
								lowerCamelCase	 =torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None])   , dim=-1							).flatten()

								lowerCamelCase	 =torch.cat([timesteps[:1], interleaved_timesteps]							)

								lowerCamelCase	 =None

								# for exp beta schedules, such as the one for `pipeline_shap_e.py`
								# we need an index counter
								lowerCamelCase	 =defaultdict(UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								# get log sigma
								lowerCamelCase	 =sigma.log()

								# get distribution
								lowerCamelCase	 =log_sigma - self.log_sigmas[:, None]

								# get sigmas range
								lowerCamelCase	 =dists.ge(0							).cumsum(dim=0							).argmax(dim=0							).clamp(max=self.log_sigmas.shape[0] - 2							)
								lowerCamelCase	 =low_idx + 1

								lowerCamelCase	 =self.log_sigmas[low_idx]
								lowerCamelCase	 =self.log_sigmas[high_idx]

								# interpolate sigmas
								lowerCamelCase	 =(low - log_sigma) / (low - high)
								lowerCamelCase	 =w.clamp(0   , 1							)

								# transform interpolation to time range
								lowerCamelCase	 =(1 - w) * low_idx + w * high_idx
								lowerCamelCase	 =t.view(sigma.shape							)
								return t


				@property
				def _snake_case       (		self							):
								return self.sample is None


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_ = True   , ):
								lowerCamelCase	 =self.index_for_timestep(UpperCAmelCase_							)

								# advance index counter by 1
								lowerCamelCase	 =timestep.cpu().item() if torch.is_tensor(UpperCAmelCase_							) else timestep
								self._index_counter[timestep_int] += 1

								if self.state_in_first_order:
												lowerCamelCase	 =self.sigmas[step_index]
												lowerCamelCase	 =self.sigmas_interpol[step_index + 1]
												lowerCamelCase	 =self.sigmas[step_index + 1]
								else:
												# 2nd order / KDPM2's method
												lowerCamelCase	 =self.sigmas[step_index - 1]
												lowerCamelCase	 =self.sigmas_interpol[step_index]
												lowerCamelCase	 =self.sigmas[step_index]

								# currently only gamma=0 is supported. This usually works best anyways.
								# We can support gamma in the future but then need to scale the timestep before
								# passing it to the model which requires a change in API
								lowerCamelCase	 =0
								lowerCamelCase	 =sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

								# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
								if self.config.prediction_type == "epsilon":
												lowerCamelCase	 =sigma_hat if self.state_in_first_order else sigma_interpol
												lowerCamelCase	 =sample - sigma_input * model_output
								elif self.config.prediction_type == "v_prediction":
												lowerCamelCase	 =sigma_hat if self.state_in_first_order else sigma_interpol
												lowerCamelCase	 =model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
												    sample / (sigma_input**2 + 1)
												)
								elif self.config.prediction_type == "sample":
												raise NotImplementedError("""prediction_type not implemented yet: sample"""							)
								else:
												raise ValueError(
												    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""							)

								if self.state_in_first_order:
												# 2. Convert to an ODE derivative for 1st order
												lowerCamelCase	 =(sample - pred_original_sample) / sigma_hat
												# 3. delta timestep
												lowerCamelCase	 =sigma_interpol - sigma_hat

												# store for 2nd order step
												lowerCamelCase	 =sample
								else:
												# DPM-Solver-2
												# 2. Convert to an ODE derivative for 2nd order
												lowerCamelCase	 =(sample - pred_original_sample) / sigma_interpol

												# 3. delta timestep
												lowerCamelCase	 =sigma_next - sigma_hat

												lowerCamelCase	 =self.sample
												lowerCamelCase	 =None

								lowerCamelCase	 =sample + derivative * dt

								if not return_dict:
												return (prev_sample,)

								return SchedulerOutput(prev_sample=UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_   , UpperCAmelCase_   , UpperCAmelCase_   , ):
								# Make sure sigmas and timesteps have the same device and dtype as original_samples
								lowerCamelCase	 =self.sigmas.to(device=original_samples.device   , dtype=original_samples.dtype							)
								if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_							):
												# mps does not support float64
												lowerCamelCase	 =self.timesteps.to(original_samples.device   , dtype=torch.floataa							)
												lowerCamelCase	 =timesteps.to(original_samples.device   , dtype=torch.floataa							)
								else:
												lowerCamelCase	 =self.timesteps.to(original_samples.device							)
												lowerCamelCase	 =timesteps.to(original_samples.device							)

								lowerCamelCase	 =[self.index_for_timestep(UpperCAmelCase_   , UpperCAmelCase_							) for t in timesteps]

								lowerCamelCase	 =sigmas[step_indices].flatten()
								while len(sigma.shape							) < len(original_samples.shape							):
												lowerCamelCase	 =sigma.unsqueeze(-1							)

								lowerCamelCase	 =original_samples + noise * sigma
								return noisy_samples


				def __len__(		self							):
								return self.config.num_train_timesteps












import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)
from transformers.utils import is_sentencepiece_available


if is_sentencepiece_available():
							from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


if is_sentencepiece_available():
							UpperCAmelCase__						:			Union[str, Any]									=get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

UpperCAmelCase__						:			Union[str, Any]									=12_80_22
UpperCAmelCase__						:			Optional[Any]									=12_80_28

@require_sentencepiece
class       __A     (						a    ,					unittest.TestCase    ):
				__A      =				MaMaaaTokenizer
				__A      =				False
				__A      =				False
				__A      =				True


				def _snake_case       (		self							):
								super().setUp()

								lowerCamelCase	 =["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
								lowerCamelCase	 =dict(zip(UpperCAmelCase_   , range(len(UpperCAmelCase_							)							)							)							)
								lowerCamelCase	 =Path(self.tmpdirname							)
								save_json(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""vocab_file"""]							)
								if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
												copyfile(UpperCAmelCase_   , save_dir / VOCAB_FILES_NAMES["""spm_file"""]							)

								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(self.tmpdirname							)
								tokenizer.save_pretrained(self.tmpdirname							)


				def _snake_case       (		self   , **UpperCAmelCase_							):
								return MaMaaaTokenizer.from_pretrained(self.tmpdirname   , **UpperCAmelCase_							)


				def _snake_case       (		self   , UpperCAmelCase_							):
								return (
								    "This is a test",
								    "This is a test",
								)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""</s>"""
								lowerCamelCase	 =0

								self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCAmelCase_							)   , UpperCAmelCase_							)
								self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCAmelCase_							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()
								lowerCamelCase	 =list(tokenizer.get_vocab().keys()							)

								self.assertEqual(vocab_keys[0]   , """</s>"""							)
								self.assertEqual(vocab_keys[1]   , """<unk>"""							)
								self.assertEqual(vocab_keys[-1]   , """<s>"""							)
								self.assertEqual(len(UpperCAmelCase_							)   , tokenizer.vocab_size + len(tokenizer.get_added_vocab()							)							)


				@unittest.skip("""Skip this test while all models are still to be uploaded."""							)
				def _snake_case       (		self							):
								pass


				def _snake_case       (		self							):
								lowerCamelCase	 =self.get_tokenizer()

								lowerCamelCase	 =tokenizer.tokenize("""This is a test"""							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(UpperCAmelCase_							)   , [2, 3, 4, 5, 6]   , )

								lowerCamelCase	 =tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6]							)
								self.assertListEqual(UpperCAmelCase_   , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

								lowerCamelCase	 =tokenizer.convert_tokens_to_string(UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , """This is a test"""							)


				@slow
				def _snake_case       (		self							):
								# fmt: off
								lowerCamelCase	 ={"""input_ids""": [[128022, 110108, 397, 11, 38272, 2247, 124811, 285, 18105, 1586, 207, 7, 39534, 4428, 397, 1019, 18105, 1586, 207, 7, 41337, 16786, 241, 7, 20214, 17, 125690, 10398, 7, 44378, 58069, 68342, 7798, 7343, 11, 299, 33310, 4, 158, 37350, 94077, 4569, 299, 33310, 90, 4, 52840, 290, 4, 31270, 112, 299, 682, 4, 52840, 39953, 14079, 193, 52519, 90894, 17894, 120697, 11, 40445, 551, 17, 1019, 52519, 90894, 17756, 963, 11, 40445, 480, 17, 9792, 1120, 5173, 1393, 6240, 16786, 241, 120996, 28, 1245, 1393, 118240, 11123, 1019, 93612, 2691, 10618, 98058, 120409, 1928, 279, 4, 40683, 367, 178, 207, 1019, 103, 103121, 506, 65296, 5, 2], [128022, 21217, 367, 117, 125450, 128, 719, 7, 7308, 40, 93612, 12669, 1116, 16704, 71, 17785, 3699, 15592, 35, 144, 9584, 241, 11943, 713, 950, 799, 2247, 88427, 150, 149, 118813, 120706, 1019, 106906, 81518, 28, 1224, 22799, 397, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [128022, 1658, 123311, 5155, 5578, 4722, 279, 14947, 2366, 1120, 1197, 14, 1348, 9232, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=UpperCAmelCase_   , model_name="""facebook/m2m100_418M"""   , revision="""c168bae485c864188cf9aa0e4108b0b6934dc91e"""   , )




@require_torch
@require_sentencepiece
@require_tokenizers
class       __A     (						unittest.TestCase    ):
				__A      =				"""facebook/m2m100_418M"""
				__A      =				[
				    """In my opinion, there are two levels of response from the French government.""",
				    """NSA Affair Emphasizes Complete Lack of Debate on Intelligence""",
				]
				__A      =				[
				    """Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
				    """L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
				]

				# fmt: off
				__A      =				[EN_CODE, 5_93, 19_49, 11_57_81, 4, 7_15_86, 42_34, 6_06_33, 12_62_33, 4_32, 12_38_08, 1_55_92, 11_97, 11_71_32, 12_06_18, 5, 2]


				@classmethod
				def _snake_case       (		cls							):
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(
								    cls.checkpoint_name   , src_lang="""en"""   , tgt_lang="""fr"""							)
								lowerCamelCase	 =1
								return cls


				def _snake_case       (		self							):
								self.assertEqual(self.tokenizer.get_lang_id("""ar"""							)   , 128006							)
								self.assertEqual(self.tokenizer.get_lang_id("""en"""							)   , 128022							)
								self.assertEqual(self.tokenizer.get_lang_id("""ro"""							)   , 128076							)
								self.assertEqual(self.tokenizer.get_lang_id("""mr"""							)   , 128063							)


				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer.get_vocab()
								self.assertEqual(len(UpperCAmelCase_							)   , self.tokenizer.vocab_size							)
								self.assertEqual(vocab["""<unk>"""]   , 3							)
								self.assertIn(self.tokenizer.get_lang_token("""en"""							)   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 =self.tokenizer.batch_encode_plus(self.src_text							).input_ids[0]
								self.assertListEqual(self.expected_src_tokens   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								self.assertIn(UpperCAmelCase_   , self.tokenizer.all_special_ids							)
								# fmt: off
								lowerCamelCase	 =[FR_CODE, 5364, 82, 8642, 4, 294, 47, 8, 14028, 136, 3286, 9706, 6, 90797, 6, 144012, 162, 88128, 30061, 5, 2]
								# fmt: on
								lowerCamelCase	 =self.tokenizer.decode(UpperCAmelCase_   , skip_special_tokens=UpperCAmelCase_							)
								lowerCamelCase	 =self.tokenizer.decode(generated_ids[1:]   , skip_special_tokens=UpperCAmelCase_							)
								self.assertEqual(UpperCAmelCase_   , UpperCAmelCase_							)
								self.assertNotIn(self.tokenizer.eos_token   , UpperCAmelCase_							)


				def _snake_case       (		self							):
								lowerCamelCase	 =tempfile.mkdtemp()
								lowerCamelCase	 =self.tokenizer.lang_token_to_id
								self.tokenizer.save_pretrained(UpperCAmelCase_							)
								lowerCamelCase	 =MaMaaaTokenizer.from_pretrained(UpperCAmelCase_							)
								self.assertDictEqual(new_tok.lang_token_to_id   , UpperCAmelCase_							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""en"""
								lowerCamelCase	 ="""fr"""

								lowerCamelCase	 =self.tokenizer(self.src_text   , text_target=self.tgt_text   , padding=UpperCAmelCase_   , return_tensors="""pt"""							)

								lowerCamelCase	 =shift_tokens_right(
								    batch["""labels"""]   , self.tokenizer.pad_token_id   , self.tokenizer.eos_token_id							)

								for k in batch:
												lowerCamelCase	 =batch[k].tolist()
								# batch = {k: v.tolist() for k,v in batch.items()}
								# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
								# batch.decoder_inputs_ids[0][0] ==
								assert batch.input_ids[1][0] == EN_CODE
								assert batch.input_ids[1][-1] == 2
								assert batch.labels[1][0] == FR_CODE
								assert batch.labels[1][-1] == 2
								assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)

								lowerCamelCase	 ="""zh"""
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 ="""mr"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""mr"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)

								lowerCamelCase	 ="""zh"""
								self.tokenizer._switch_to_target_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id("""zh"""							)]							)
								self.assertListEqual(self.tokenizer.suffix_tokens   , [self.tokenizer.eos_token_id]							)
								self.tokenizer._switch_to_input_mode()
								self.assertListEqual(self.tokenizer.prefix_tokens   , [self.tokenizer.get_lang_id(self.tokenizer.src_lang							)]							)


				@require_torch
				def _snake_case       (		self							):
								lowerCamelCase	 =self.tokenizer._build_translation_inputs("""A test"""   , return_tensors="""pt"""   , src_lang="""en"""   , tgt_lang="""ar"""							)

								self.assertEqual(
								    nested_simplify(UpperCAmelCase_							)   , {
								        # en_XX, A, test, EOS
								        """input_ids""": [[128022, 58, 4183, 2]],
								        """attention_mask""": [[1, 1, 1, 1]],
								        # ar_AR
								        """forced_bos_token_id""": 128006,
								    }   , )





