
"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					int   =			LongformerTokenizer
       UpperCamelCase_		:					Tuple   =			True
       UpperCamelCase_		:					int   =			LongformerTokenizerFast
       UpperCamelCase_		:					Dict   =			True






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Optional[int]:







              """simple docstring"""
              super().setUp()

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

              UpperCamelCase					 =		os.path.join(self.tmpdirname       ,      VOCAB_FILES_NAMES["vocab_file"]			)
              UpperCamelCase					 =		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(UpperCAmelCase_			) + "\n"			)
              with open(self.merges_file       ,      "w"       ,      encoding="utf-8"			) as fp:
                     fp.write("\n".join(UpperCAmelCase_			)			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							str       ,      **UpperCAmelCase_ :							Tuple			)->  str:







              """simple docstring"""
              kwargs.update(self.special_tokens_map			)
              return self.tokenizer_class.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]       ,      **UpperCAmelCase_ :							Dict			)->  List[Any]:







              """simple docstring"""
              kwargs.update(self.special_tokens_map			)
              return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,      **UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple       ,      UpperCAmelCase_ :							int			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		"lower newer"
              UpperCamelCase					 =		"lower newer"
              return input_text, output_text






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class(self.vocab_file       ,      self.merges_file       ,      **self.special_tokens_map			)
              UpperCamelCase					 =		"lower newer"
              UpperCamelCase					 =		["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
              UpperCamelCase					 =		tokenizer.tokenize(UpperCAmelCase_			)  # , add_prefix_space=True)
              self.assertListEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokens + [tokenizer.unk_token]
              UpperCamelCase					 =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
              self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  str:







              """simple docstring"""
              UpperCamelCase					 =		self.get_tokenizer()

              self.assertListEqual(tokenizer.encode("Hello world!"       ,      add_special_tokens=UpperCAmelCase_			)       ,      [0, 31_414, 232, 328, 2]			)
              self.assertListEqual(
                  tokenizer.encode("Hello world! cécé herlolip 418"       ,      add_special_tokens=UpperCAmelCase_			)       ,      [0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]       ,      )






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Optional[Any]			)->  List[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"			)

              UpperCamelCase					 =		tokenizer.encode("sequence builders"       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.encode("multi-sequence build"       ,      add_special_tokens=UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(
                  "sequence builders"       ,      add_special_tokens=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.encode(
                  "sequence builders"       ,      "multi-sequence build"       ,      add_special_tokens=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.build_inputs_with_special_tokens(UpperCAmelCase_       ,      UpperCAmelCase_			)

              assert encoded_sentence == encoded_text_from_decode
              assert encoded_pair == encoded_pair_from_decode






       def 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[Any]:







              """simple docstring"""
              UpperCamelCase					 =		self.get_tokenizer()

              UpperCamelCase					 =		"Encode this sequence."
              UpperCamelCase					 =		tokenizer.byte_encoder[" ".encode("utf-8"			)[0]]

              # Testing encoder arguments
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
              self.assertNotEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.convert_ids_to_tokens(encoded[0]			)[0]
              self.assertEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              tokenizer.add_special_tokens({"bos_token": "<s>"}			)
              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.convert_ids_to_tokens(encoded[1]			)[0]
              self.assertNotEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              # Testing spaces after special tokens
              UpperCamelCase					 =		"<mask>"
              tokenizer.add_special_tokens(
                  {"mask_token": AddedToken(UpperCAmelCase_       ,      lstrip=UpperCAmelCase_       ,      rstrip=UpperCAmelCase_			)}			)  # mask token has a left space
              UpperCamelCase					 =		tokenizer.convert_tokens_to_ids(UpperCAmelCase_			)

              UpperCamelCase					 =		"Encode <mask> sequence"
              UpperCamelCase					 =		"Encode <mask>sequence"

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		encoded.index(UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
              self.assertEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)

              UpperCamelCase					 =		tokenizer.encode(UpperCAmelCase_			)
              UpperCamelCase					 =		encoded.index(UpperCAmelCase_			)
              UpperCamelCase					 =		tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]			)[0]
              self.assertNotEqual(UpperCAmelCase_       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[Any]:







              """simple docstring"""
              pass






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  Optional[int]:







              """simple docstring"""
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                            UpperCamelCase					 =		self.tokenizer_class.from_pretrained(UpperCAmelCase_       ,      **UpperCAmelCase_			)
                            UpperCamelCase					 =		"A, <mask> AllenNLP sentence."
                            UpperCamelCase					 =		tokenizer_r.encode_plus(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      return_token_type_ids=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_p.encode_plus(UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_       ,      return_token_type_ids=UpperCAmelCase_			)

                            # 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"]			)       ,      )

                            UpperCamelCase					 =		tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]			)
                            UpperCamelCase					 =		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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)
                            self.assertSequenceEqual(tokens_r["input_ids"]       ,      [0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2]			)

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Optional[Any]:







              """simple docstring"""
              for trim_offsets, add_prefix_space in itertools.product([True, False]       ,      repeat=2			):
                     UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                         self.tmpdirname       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)

                     UpperCamelCase					 =		json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()			)
                     UpperCamelCase					 =		json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()			)

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

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






       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Optional[int]:







              """simple docstring"""
              # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
              # `trim_offsets`
              for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                     with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"			):
                            UpperCamelCase					 =		"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
                            UpperCamelCase					 =		f"{text_of_1_token} {text_of_1_token}"

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r(UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            self.assertEqual(encoding.offset_mapping[0]       ,      (0, len(UpperCAmelCase_			))			)
                            self.assertEqual(
                                encoding.offset_mapping[1]       ,      (len(UpperCAmelCase_			) + 1, len(UpperCAmelCase_			) + 1 + len(UpperCAmelCase_			))       ,      )

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r(UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            self.assertEqual(encoding.offset_mapping[0]       ,      (0, len(UpperCAmelCase_			))			)
                            self.assertEqual(
                                encoding.offset_mapping[1]       ,      (len(UpperCAmelCase_			) + 1, len(UpperCAmelCase_			) + 1 + len(UpperCAmelCase_			))       ,      )

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r(UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            self.assertEqual(encoding.offset_mapping[0]       ,      (0, len(UpperCAmelCase_			))			)
                            self.assertEqual(
                                encoding.offset_mapping[1]       ,      (len(UpperCAmelCase_			), len(UpperCAmelCase_			) + 1 + len(UpperCAmelCase_			))       ,      )

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r(UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            self.assertEqual(encoding.offset_mapping[0]       ,      (0, len(UpperCAmelCase_			))			)
                            self.assertEqual(
                                encoding.offset_mapping[1]       ,      (len(UpperCAmelCase_			), len(UpperCAmelCase_			) + 1 + len(UpperCAmelCase_			))       ,      )

                            UpperCamelCase					 =		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)),
                            # )

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r(UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            self.assertEqual(encoding.offset_mapping[0]       ,      (1, 1 + len(UpperCAmelCase_			))			)
                            self.assertEqual(
                                encoding.offset_mapping[1]       ,      (1 + len(UpperCAmelCase_			) + 1, 1 + len(UpperCAmelCase_			) + 1 + len(UpperCAmelCase_			))       ,      )

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r(UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            self.assertEqual(encoding.offset_mapping[0]       ,      (0, 1 + len(UpperCAmelCase_			))			)
                            self.assertEqual(
                                encoding.offset_mapping[1]       ,      (1 + len(UpperCAmelCase_			), 1 + len(UpperCAmelCase_			) + 1 + len(UpperCAmelCase_			))       ,      )

                            UpperCamelCase					 =		self.rust_tokenizer_class.from_pretrained(
                                UpperCAmelCase_       ,      use_fast=UpperCAmelCase_       ,      add_prefix_space=UpperCAmelCase_       ,      trim_offsets=UpperCAmelCase_			)
                            UpperCamelCase					 =		tokenizer_r(UpperCAmelCase_       ,      return_offsets_mapping=UpperCAmelCase_       ,      add_special_tokens=UpperCAmelCase_			)
                            self.assertEqual(encoding.offset_mapping[0]       ,      (0, 1 + len(UpperCAmelCase_			))			)
                            self.assertEqual(
                                encoding.offset_mapping[1]       ,      (1 + len(UpperCAmelCase_			), 1 + len(UpperCAmelCase_			) + 1 + len(UpperCAmelCase_			))       ,      )







"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow


if is_torch_available():
      import torch

      from transformers import XLMRobertaModel



@require_sentencepiece
@require_tokenizers
@require_torch
class   __a  (      unittest.TestCase	):






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-base"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 768)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		torch.tensor(
                  [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]]			)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							Union[str, Any]			)->  Optional[int]:







              """simple docstring"""
              UpperCamelCase					 =		XLMRobertaModel.from_pretrained("xlm-roberta-large"			)
              UpperCamelCase					 =		torch.tensor([[0, 581, 10_269, 83, 99_942, 136, 60_742, 23, 70, 80_583, 18_276, 2]]			)
              # The dog is cute and lives in the garden house

              UpperCamelCase					 =		torch.Size((1, 12, 1_024)			)  # batch_size, sequence_length, embedding_vector_dim
              UpperCamelCase					 =		torch.tensor(
                  [[-0.0699, -0.0318, 0.0705, -0.1241, 0.0999, -0.0520, 0.1004, -0.1838, -0.4704, 0.1437, 0.0821, 0.0126]]			)
              #  xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.large')
              #  xlmr.eval()
              #  expected_output_values_last_dim = xlmr.extract_features(input_ids[0])[:, :, -1]
              with torch.no_grad():
                     UpperCamelCase					 =		model(UpperCAmelCase_			)["last_hidden_state"].detach()
              self.assertEqual(output.shape       ,      UpperCAmelCase_			)
              # compare the actual values for a slice of last dim
              self.assertTrue(torch.allclose(output[:, :, -1]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)





