
import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
		import jax.numpy as jnp

if is_tf_available():
		import tensorflow as tf

if is_torch_available():
		import torch




class  lowerCamelCase__							(				unittest.TestCase):




	'''simple docstring'''




	def    _lowerCamelCase				(					self		:Tuple						)		-> List[str]:
								__UpperCamelCase	:   str			   =					{
								    "task_specific_params": {
								        "summarization": {"length_penalty": 1.0, "max_length": 1_2_8, "min_length": 1_2, "num_beams": 4},
								        "summarization_cnn": {"length_penalty": 2.0, "max_length": 1_4_2, "min_length": 5_6, "num_beams": 4},
								        "summarization_xsum": {"length_penalty": 1.0, "max_length": 6_2, "min_length": 1_1, "num_beams": 6},
								    }
								}
								__UpperCamelCase	:   List[Any]			   =					{
								    "task_specific_params.summarization.length_penalty": 1.0,
								    "task_specific_params.summarization.max_length": 1_2_8,
								    "task_specific_params.summarization.min_length": 1_2,
								    "task_specific_params.summarization.num_beams": 4,
								    "task_specific_params.summarization_cnn.length_penalty": 2.0,
								    "task_specific_params.summarization_cnn.max_length": 1_4_2,
								    "task_specific_params.summarization_cnn.min_length": 5_6,
								    "task_specific_params.summarization_cnn.num_beams": 4,
								    "task_specific_params.summarization_xsum.length_penalty": 1.0,
								    "task_specific_params.summarization_xsum.max_length": 6_2,
								    "task_specific_params.summarization_xsum.min_length": 1_1,
								    "task_specific_params.summarization_xsum.num_beams": 6,
								}

								self.assertEqual(flatten_dict(a						)     ,   a						)







	def    _lowerCamelCase				(					self		:int						)		-> Any:
								__UpperCamelCase	:   Tuple			   =					np.random.randn(3     ,   4						)
								self.assertTrue(np.allclose(transpose(a						)     ,   x.transpose()						)						)

								__UpperCamelCase	:   List[str]			   =					np.random.randn(3     ,   4     ,   5						)
								self.assertTrue(np.allclose(transpose(a     ,   axes=(1, 2, 0)						)     ,   x.transpose((1, 2, 0)						)						)						)







	@require_torch
	def    _lowerCamelCase				(					self		:List[str]						)		-> str:
								__UpperCamelCase	:   Union[str, Any]			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   Optional[Any]			   =					torch.tensor(a						)
								self.assertTrue(np.allclose(transpose(a						)     ,   transpose(a						).numpy()						)						)

								__UpperCamelCase	:   Dict			   =					np.random.randn(3     ,   4     ,   5						)
								__UpperCamelCase	:   Tuple			   =					torch.tensor(a						)
								self.assertTrue(np.allclose(transpose(a     ,   axes=(1, 2, 0)						)     ,   transpose(a     ,   axes=(1, 2, 0)						).numpy()						)						)







	@require_tf
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> List[Any]:
								__UpperCamelCase	:   Optional[int]			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   Dict			   =					tf.constant(a						)
								self.assertTrue(np.allclose(transpose(a						)     ,   transpose(a						).numpy()						)						)

								__UpperCamelCase	:   List[Any]			   =					np.random.randn(3     ,   4     ,   5						)
								__UpperCamelCase	:   List[str]			   =					tf.constant(a						)
								self.assertTrue(np.allclose(transpose(a     ,   axes=(1, 2, 0)						)     ,   transpose(a     ,   axes=(1, 2, 0)						).numpy()						)						)







	@require_flax
	def    _lowerCamelCase				(					self		:int						)		-> Tuple:
								__UpperCamelCase	:   Optional[Any]			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   Dict			   =					jnp.array(a						)
								self.assertTrue(np.allclose(transpose(a						)     ,   np.asarray(transpose(a						)						)						)						)

								__UpperCamelCase	:   List[str]			   =					np.random.randn(3     ,   4     ,   5						)
								__UpperCamelCase	:   Any			   =					jnp.array(a						)
								self.assertTrue(np.allclose(transpose(a     ,   axes=(1, 2, 0)						)     ,   np.asarray(transpose(a     ,   axes=(1, 2, 0)						)						)						)						)







	def    _lowerCamelCase				(					self		:Any						)		-> Dict:
								__UpperCamelCase	:   Optional[Any]			   =					np.random.randn(3     ,   4						)
								self.assertTrue(np.allclose(reshape(a     ,   (4, 3)						)     ,   np.reshape(a     ,   (4, 3)						)						)						)

								__UpperCamelCase	:   Optional[Any]			   =					np.random.randn(3     ,   4     ,   5						)
								self.assertTrue(np.allclose(reshape(a     ,   (1_2, 5)						)     ,   np.reshape(a     ,   (1_2, 5)						)						)						)







	@require_torch
	def    _lowerCamelCase				(					self		:Any						)		-> Optional[Any]:
								__UpperCamelCase	:   Optional[Any]			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   str			   =					torch.tensor(a						)
								self.assertTrue(np.allclose(reshape(a     ,   (4, 3)						)     ,   reshape(a     ,   (4, 3)						).numpy()						)						)

								__UpperCamelCase	:   Any			   =					np.random.randn(3     ,   4     ,   5						)
								__UpperCamelCase	:   Optional[Any]			   =					torch.tensor(a						)
								self.assertTrue(np.allclose(reshape(a     ,   (1_2, 5)						)     ,   reshape(a     ,   (1_2, 5)						).numpy()						)						)







	@require_tf
	def    _lowerCamelCase				(					self		:Optional[int]						)		-> int:
								__UpperCamelCase	:   List[Any]			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   str			   =					tf.constant(a						)
								self.assertTrue(np.allclose(reshape(a     ,   (4, 3)						)     ,   reshape(a     ,   (4, 3)						).numpy()						)						)

								__UpperCamelCase	:   int			   =					np.random.randn(3     ,   4     ,   5						)
								__UpperCamelCase	:   Optional[int]			   =					tf.constant(a						)
								self.assertTrue(np.allclose(reshape(a     ,   (1_2, 5)						)     ,   reshape(a     ,   (1_2, 5)						).numpy()						)						)







	@require_flax
	def    _lowerCamelCase				(					self		:List[Any]						)		-> str:
								__UpperCamelCase	:   List[str]			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   Any			   =					jnp.array(a						)
								self.assertTrue(np.allclose(reshape(a     ,   (4, 3)						)     ,   np.asarray(reshape(a     ,   (4, 3)						)						)						)						)

								__UpperCamelCase	:   Optional[int]			   =					np.random.randn(3     ,   4     ,   5						)
								__UpperCamelCase	:   List[Any]			   =					jnp.array(a						)
								self.assertTrue(np.allclose(reshape(a     ,   (1_2, 5)						)     ,   np.asarray(reshape(a     ,   (1_2, 5)						)						)						)						)







	def    _lowerCamelCase				(					self		:Dict						)		-> List[str]:
								__UpperCamelCase	:   List[Any]			   =					np.random.randn(1     ,   3     ,   4						)
								self.assertTrue(np.allclose(squeeze(a						)     ,   np.squeeze(a						)						)						)

								__UpperCamelCase	:   int			   =					np.random.randn(1     ,   4     ,   1     ,   5						)
								self.assertTrue(np.allclose(squeeze(a     ,   axis=2						)     ,   np.squeeze(a     ,   axis=2						)						)						)







	@require_torch
	def    _lowerCamelCase				(					self		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					np.random.randn(1     ,   3     ,   4						)
								__UpperCamelCase	:   Any			   =					torch.tensor(a						)
								self.assertTrue(np.allclose(squeeze(a						)     ,   squeeze(a						).numpy()						)						)

								__UpperCamelCase	:   List[Any]			   =					np.random.randn(1     ,   4     ,   1     ,   5						)
								__UpperCamelCase	:   str			   =					torch.tensor(a						)
								self.assertTrue(np.allclose(squeeze(a     ,   axis=2						)     ,   squeeze(a     ,   axis=2						).numpy()						)						)







	@require_tf
	def    _lowerCamelCase				(					self		:Union[str, Any]						)		-> Tuple:
								__UpperCamelCase	:   str			   =					np.random.randn(1     ,   3     ,   4						)
								__UpperCamelCase	:   List[str]			   =					tf.constant(a						)
								self.assertTrue(np.allclose(squeeze(a						)     ,   squeeze(a						).numpy()						)						)

								__UpperCamelCase	:   Tuple			   =					np.random.randn(1     ,   4     ,   1     ,   5						)
								__UpperCamelCase	:   Union[str, Any]			   =					tf.constant(a						)
								self.assertTrue(np.allclose(squeeze(a     ,   axis=2						)     ,   squeeze(a     ,   axis=2						).numpy()						)						)







	@require_flax
	def    _lowerCamelCase				(					self		:Dict						)		-> int:
								__UpperCamelCase	:   int			   =					np.random.randn(1     ,   3     ,   4						)
								__UpperCamelCase	:   Tuple			   =					jnp.array(a						)
								self.assertTrue(np.allclose(squeeze(a						)     ,   np.asarray(squeeze(a						)						)						)						)

								__UpperCamelCase	:   int			   =					np.random.randn(1     ,   4     ,   1     ,   5						)
								__UpperCamelCase	:   Optional[Any]			   =					jnp.array(a						)
								self.assertTrue(np.allclose(squeeze(a     ,   axis=2						)     ,   np.asarray(squeeze(a     ,   axis=2						)						)						)						)







	def    _lowerCamelCase				(					self		:Any						)		-> List[str]:
								__UpperCamelCase	:   Tuple			   =					np.random.randn(3     ,   4						)
								self.assertTrue(np.allclose(expand_dims(a     ,   axis=1						)     ,   np.expand_dims(a     ,   axis=1						)						)						)







	@require_torch
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Optional[int]:
								__UpperCamelCase	:   Any			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   Dict			   =					torch.tensor(a						)
								self.assertTrue(np.allclose(expand_dims(a     ,   axis=1						)     ,   expand_dims(a     ,   axis=1						).numpy()						)						)







	@require_tf
	def    _lowerCamelCase				(					self		:List[str]						)		-> List[Any]:
								__UpperCamelCase	:   str			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   Dict			   =					tf.constant(a						)
								self.assertTrue(np.allclose(expand_dims(a     ,   axis=1						)     ,   expand_dims(a     ,   axis=1						).numpy()						)						)







	@require_flax
	def    _lowerCamelCase				(					self		:Any						)		-> Any:
								__UpperCamelCase	:   Any			   =					np.random.randn(3     ,   4						)
								__UpperCamelCase	:   Optional[int]			   =					jnp.array(a						)
								self.assertTrue(np.allclose(expand_dims(a     ,   axis=1						)     ,   np.asarray(expand_dims(a     ,   axis=1						)						)						)						)

import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase  :					int													=       logging.get_logger(__name__)


lowercase  :					Union[str, Any]													=       '▁'

lowercase  :					Tuple													=       {'vocab_file': 'spiece.model'}

lowercase  :					Dict													=       {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

lowercase  :					Any													=       {
    'google/reformer-crime-and-punishment': 524288,
}




class  lowerCamelCase__							(				__lowercase):




	'''simple docstring'''




	_A								=     VOCAB_FILES_NAMES
	_A								=     PRETRAINED_VOCAB_FILES_MAP
	_A								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_A								=     ['input_ids', 'attention_mask']







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

								super().__init__(
								    eos_token=a     ,   unk_token=a     ,   additional_special_tokens=a     ,   sp_model_kwargs=self.sp_model_kwargs     ,   **a     ,   )

								__UpperCamelCase	:   Optional[Any]			   =					vocab_file
								__UpperCamelCase	:   Dict			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(a						)







	@property
	def    _lowerCamelCase				(					self		:Optional[Any]						)		-> Any:
								return self.sp_model.get_piece_size()







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







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







	def __setstate__(					self		:int     ,   a		:List[str]						)		-> int:
								__UpperCamelCase	:   Union[str, Any]			   =					d

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

								__UpperCamelCase	:   Any			   =					spm.SentencePieceProcessor(**self.sp_model_kwargs						)
								self.sp_model.Load(self.vocab_file						)







	def    _lowerCamelCase				(					self		:List[Any]     ,   a		:str						)		-> List[str]:
								return self.sp_model.encode(a     ,   out_type=a						)







	def    _lowerCamelCase				(					self		:Optional[int]     ,   a		:Optional[Any]						)		-> str:
								return self.sp_model.piece_to_id(a						)







	def    _lowerCamelCase				(					self		:Dict     ,   a		:Union[str, Any]						)		-> Optional[int]:
								if index < self.sp_model.get_piece_size():
															__UpperCamelCase	:   Optional[int]			   =					self.sp_model.IdToPiece(a						)
								return token







	def    _lowerCamelCase				(					self		:Dict     ,   a		:List[Any]						)		-> Dict:
								__UpperCamelCase	:   Optional[int]			   =					[]
								__UpperCamelCase	:   str			   =					""
								for token in tokens:
															# make sure that special tokens are not decoded using sentencepiece model
															if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(a						) + token
																						__UpperCamelCase	:   List[Any]			   =					[]
															else:
																						current_sub_tokens.append(a						)
								out_string += self.sp_model.decode(a						)
								return out_string.strip()







	def    _lowerCamelCase				(					self		:Optional[Any]     ,   a		:str     ,   a		:Optional[str] = None						)		-> Tuple[str]:
								if not os.path.isdir(a						):
															logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
															return
								__UpperCamelCase	:   List[Any]			   =					os.path.join(
								    a     ,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

								if os.path.abspath(self.vocab_file						) != os.path.abspath(a						) and os.path.isfile(self.vocab_file						):
															copyfile(self.vocab_file     ,   a						)
								elif not os.path.isfile(self.vocab_file						):
															with open(a     ,   "wb"						) as fi:
																						__UpperCamelCase	:   int			   =					self.sp_model.serialized_model_proto()
																						fi.write(a						)

								return (out_vocab_file,)