



import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   UpperCAmelCase_    (       _lowerCAmelCase   ,					unittest.TestCase):
							lowerCamelCase__   			=		RobertaTokenizer
							lowerCamelCase__   			=		RobertaTokenizerFast
							lowerCamelCase__   			=		True
							lowerCamelCase__   			=		{'cls_token': '<s>'}







							def    snake_case__      (   self):

														'''simple docstring'''

														super().setUp()

														# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
														_lowerCAmelCase     :							List[Any]    =    [
														    "l",
														    "o",
														    "w",
														    "e",
														    "r",
														    "s",
														    "t",
														    "i",
														    "d",
														    "n",
														    "\u0120",
														    "\u0120l",
														    "\u0120n",
														    "\u0120lo",
														    "\u0120low",
														    "er",
														    "\u0120lowest",
														    "\u0120newer",
														    "\u0120wider",
														    "<unk>",
														]
														_lowerCAmelCase     :							List[Any]    =    dict(zip(__a,			range(len(__a))))
														_lowerCAmelCase     :							Optional[Any]    =    ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
														_lowerCAmelCase     :							Union[str, Any]    =    {"unk_token": "<unk>"}

														_lowerCAmelCase     :							Tuple    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["vocab_file"])
														_lowerCAmelCase     :							Union[str, Any]    =    os.path.join(self.tmpdirname,			VOCAB_FILES_NAMES["merges_file"])
														with open(self.vocab_file,			"w",			encoding="utf-8") as fp:
																					fp.write(json.dumps(__a) + "\n")
														with open(self.merges_file,			"w",			encoding="utf-8") as fp:
																					fp.write("\n".join(__a))







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														kwargs.update(self.special_tokens_map)
														return self.tokenizer_class.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														kwargs.update(self.special_tokens_map)
														return RobertaTokenizerFast.from_pretrained(self.tmpdirname,			**__a)







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    "lower newer"
														_lowerCAmelCase     :							Any    =    "lower newer"
														return input_text, output_text







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    self.tokenizer_class(self.vocab_file,			self.merges_file,			**self.special_tokens_map)
														_lowerCAmelCase     :							Dict    =    "lower newer"
														_lowerCAmelCase     :							Dict    =    ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
														_lowerCAmelCase     :							int    =    tokenizer.tokenize(__a)  # , add_prefix_space=True)
														self.assertListEqual(__a,			__a)

														_lowerCAmelCase     :							Optional[Any]    =    tokens + [tokenizer.unk_token]
														_lowerCAmelCase     :							Any    =    [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
														self.assertListEqual(tokenizer.convert_tokens_to_ids(__a),			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    self.get_tokenizer()

														self.assertListEqual(tokenizer.encode("Hello world!",			add_special_tokens=__a),			[0, 3_1414, 232, 328, 2])
														self.assertListEqual(
														    tokenizer.encode("Hello world! cécé herlolip 418",			add_special_tokens=__a),			[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2],			)







							@slow
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    self.tokenizer_class.from_pretrained("roberta-base")

														_lowerCAmelCase     :							Any    =    tokenizer.encode("sequence builders",			add_special_tokens=__a)
														_lowerCAmelCase     :							str    =    tokenizer.encode("multi-sequence build",			add_special_tokens=__a)

														_lowerCAmelCase     :							int    =    tokenizer.encode(
														    "sequence builders",			add_special_tokens=__a,			add_prefix_space=__a)
														_lowerCAmelCase     :							Optional[int]    =    tokenizer.encode(
														    "sequence builders",			"multi-sequence build",			add_special_tokens=__a,			add_prefix_space=__a)

														_lowerCAmelCase     :							str    =    tokenizer.build_inputs_with_special_tokens(__a)
														_lowerCAmelCase     :							Optional[int]    =    tokenizer.build_inputs_with_special_tokens(__a,			__a)

														assert encoded_sentence == encoded_text_from_decode
														assert encoded_pair == encoded_pair_from_decode







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.get_tokenizer()

														_lowerCAmelCase     :							str    =    "Encode this sequence."
														_lowerCAmelCase     :							Tuple    =    tokenizer.byte_encoder[" ".encode("utf-8")[0]]

														# Testing encoder arguments
														_lowerCAmelCase     :							List[str]    =    tokenizer.encode(__a,			add_special_tokens=__a,			add_prefix_space=__a)
														_lowerCAmelCase     :							List[Any]    =    tokenizer.convert_ids_to_tokens(encoded[0])[0]
														self.assertNotEqual(__a,			__a)

														_lowerCAmelCase     :							List[str]    =    tokenizer.encode(__a,			add_special_tokens=__a,			add_prefix_space=__a)
														_lowerCAmelCase     :							List[Any]    =    tokenizer.convert_ids_to_tokens(encoded[0])[0]
														self.assertEqual(__a,			__a)

														tokenizer.add_special_tokens({"bos_token": "<s>"})
														_lowerCAmelCase     :							List[str]    =    tokenizer.encode(__a,			add_special_tokens=__a)
														_lowerCAmelCase     :							Optional[int]    =    tokenizer.convert_ids_to_tokens(encoded[1])[0]
														self.assertNotEqual(__a,			__a)

														# Testing spaces after special tokens
														_lowerCAmelCase     :							Union[str, Any]    =    "<mask>"
														tokenizer.add_special_tokens(
														    {"mask_token": AddedToken(__a,			lstrip=__a,			rstrip=__a)})  # mask token has a left space
														_lowerCAmelCase     :							Any    =    tokenizer.convert_tokens_to_ids(__a)

														_lowerCAmelCase     :							Tuple    =    "Encode <mask> sequence"
														_lowerCAmelCase     :							int    =    "Encode <mask>sequence"

														_lowerCAmelCase     :							str    =    tokenizer.encode(__a)
														_lowerCAmelCase     :							Any    =    encoded.index(__a)
														_lowerCAmelCase     :							Dict    =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
														self.assertEqual(__a,			__a)

														_lowerCAmelCase     :							str    =    tokenizer.encode(__a)
														_lowerCAmelCase     :							int    =    encoded.index(__a)
														_lowerCAmelCase     :							Optional[int]    =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
														self.assertNotEqual(__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self):

														'''simple docstring'''

														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
																												_lowerCAmelCase     :							List[Any]    =    self.rust_tokenizer_class.from_pretrained(__a,			**__a)
																												_lowerCAmelCase     :							Dict    =    self.tokenizer_class.from_pretrained(__a,			**__a)
																												_lowerCAmelCase     :							str    =    "A, <mask> AllenNLP sentence."
																												_lowerCAmelCase     :							Tuple    =    tokenizer_r.encode_plus(__a,			add_special_tokens=__a,			return_token_type_ids=__a)
																												_lowerCAmelCase     :							str    =    tokenizer_p.encode_plus(__a,			add_special_tokens=__a,			return_token_type_ids=__a)

																												# token_type_ids should put 0 everywhere
																												self.assertEqual(sum(tokens_r["token_type_ids"]),			sum(tokens_p["token_type_ids"]))

																												# attention_mask should put 1 everywhere, so sum over length should be 1
																												self.assertEqual(
																												    sum(tokens_r["attention_mask"]) / len(tokens_r["attention_mask"]),			sum(tokens_p["attention_mask"]) / len(tokens_p["attention_mask"]),			)

																												_lowerCAmelCase     :							Any    =    tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"])
																												_lowerCAmelCase     :							Union[str, Any]    =    tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"])

																												# Rust correctly handles the space before the mask while python doesnt
																												self.assertSequenceEqual(tokens_p["input_ids"],			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])
																												self.assertSequenceEqual(tokens_r["input_ids"],			[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2])

																												self.assertSequenceEqual(
																												    __a,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])
																												self.assertSequenceEqual(
																												    __a,			["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"])







							def    snake_case__      (   self):

														'''simple docstring'''

														for trim_offsets, add_prefix_space in itertools.product([True, False],			repeat=2):
																					_lowerCAmelCase     :							List[Any]    =    self.rust_tokenizer_class.from_pretrained(
																					    self.tmpdirname,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)

																					_lowerCAmelCase     :							int    =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
																					_lowerCAmelCase     :							Any    =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

																					self.assertEqual(pre_tokenizer_state["add_prefix_space"],			__a)

																					self.assertEqual(post_processor_state["add_prefix_space"],			__a)
																					self.assertEqual(post_processor_state["trim_offsets"],			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																					with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"):
																												_lowerCAmelCase     :							Tuple    =    "hello"  # `hello` is a token in the vocabulary of `pretrained_name`
																												_lowerCAmelCase     :							Optional[Any]    =    f"{text_of_1_token} {text_of_1_token}"

																												_lowerCAmelCase     :							int    =    self.rust_tokenizer_class.from_pretrained(
																												    __a,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)
																												_lowerCAmelCase     :							Optional[int]    =    tokenizer_r(__a,			return_offsets_mapping=__a,			add_special_tokens=__a)
																												self.assertEqual(encoding.offset_mapping[0],			(0, len(__a)))
																												self.assertEqual(
																												    encoding.offset_mapping[1],			(len(__a) + 1, len(__a) + 1 + len(__a)),			)

																												_lowerCAmelCase     :							Dict    =    self.rust_tokenizer_class.from_pretrained(
																												    __a,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)
																												_lowerCAmelCase     :							List[Any]    =    tokenizer_r(__a,			return_offsets_mapping=__a,			add_special_tokens=__a)
																												self.assertEqual(encoding.offset_mapping[0],			(0, len(__a)))
																												self.assertEqual(
																												    encoding.offset_mapping[1],			(len(__a) + 1, len(__a) + 1 + len(__a)),			)

																												_lowerCAmelCase     :							Any    =    self.rust_tokenizer_class.from_pretrained(
																												    __a,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)
																												_lowerCAmelCase     :							Optional[Any]    =    tokenizer_r(__a,			return_offsets_mapping=__a,			add_special_tokens=__a)
																												self.assertEqual(encoding.offset_mapping[0],			(0, len(__a)))
																												self.assertEqual(
																												    encoding.offset_mapping[1],			(len(__a), len(__a) + 1 + len(__a)),			)

																												_lowerCAmelCase     :							List[str]    =    self.rust_tokenizer_class.from_pretrained(
																												    __a,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)
																												_lowerCAmelCase     :							List[Any]    =    tokenizer_r(__a,			return_offsets_mapping=__a,			add_special_tokens=__a)
																												self.assertEqual(encoding.offset_mapping[0],			(0, len(__a)))
																												self.assertEqual(
																												    encoding.offset_mapping[1],			(len(__a), len(__a) + 1 + len(__a)),			)

																												_lowerCAmelCase     :							Dict    =    f" {text}"

																												# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																												#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																												# )
																												# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																												# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																												# self.assertEqual(
																												#     encoding.offset_mapping[1],
																												#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																												# )

																												_lowerCAmelCase     :							Optional[Any]    =    self.rust_tokenizer_class.from_pretrained(
																												    __a,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)
																												_lowerCAmelCase     :							Optional[Any]    =    tokenizer_r(__a,			return_offsets_mapping=__a,			add_special_tokens=__a)
																												self.assertEqual(encoding.offset_mapping[0],			(1, 1 + len(__a)))
																												self.assertEqual(
																												    encoding.offset_mapping[1],			(1 + len(__a) + 1, 1 + len(__a) + 1 + len(__a)),			)

																												_lowerCAmelCase     :							Union[str, Any]    =    self.rust_tokenizer_class.from_pretrained(
																												    __a,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)
																												_lowerCAmelCase     :							Dict    =    tokenizer_r(__a,			return_offsets_mapping=__a,			add_special_tokens=__a)
																												self.assertEqual(encoding.offset_mapping[0],			(0, 1 + len(__a)))
																												self.assertEqual(
																												    encoding.offset_mapping[1],			(1 + len(__a), 1 + len(__a) + 1 + len(__a)),			)

																												_lowerCAmelCase     :							Optional[int]    =    self.rust_tokenizer_class.from_pretrained(
																												    __a,			use_fast=__a,			add_prefix_space=__a,			trim_offsets=__a)
																												_lowerCAmelCase     :							Dict    =    tokenizer_r(__a,			return_offsets_mapping=__a,			add_special_tokens=__a)
																												self.assertEqual(encoding.offset_mapping[0],			(0, 1 + len(__a)))
																												self.assertEqual(
																												    encoding.offset_mapping[1],			(1 + len(__a), 1 + len(__a) + 1 + len(__a)),			)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase					:     Optional[Any]												={
    '''configuration_nllb_moe''': [
        '''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''NllbMoeConfig''',
    ]
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase					:     Tuple												=[
     '''NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''NllbMoeForConditionalGeneration''',
     '''NllbMoeModel''',
     '''NllbMoePreTrainedModel''',
     '''NllbMoeTop2Router''',
     '''NllbMoeSparseMLP''',
 ]


if TYPE_CHECKING:
 from .configuration_nllb_moe import (
     NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
     NllbMoeConfig,
 )

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_nllb_moe import (
      NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
      NllbMoeForConditionalGeneration,
      NllbMoeModel,
      NllbMoePreTrainedModel,
      NllbMoeSparseMLP,
      NllbMoeTopaRouter,
  )


else:
 import sys

 lowerCAmelCase					:     Tuple												=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


