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 					_lowercase    (     UpperCAmelCase__ ,  unittest.TestCase			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				List[Any]          =    LongformerTokenizer
    SCREAMING_SNAKE_CASE:				List[Any]          =    True
    SCREAMING_SNAKE_CASE:				Optional[int]          =    LongformerTokenizerFast
    SCREAMING_SNAKE_CASE:				Optional[int]          =    True




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

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

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




    def      _a    (    self       ,						**lowerCamelCase__ ):
       kwargs.update(self.special_tokens_map )
       return self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						**lowerCamelCase__ ):
       kwargs.update(self.special_tokens_map )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Dict       =	"lower newer"
       lowerCAmelCase_:							Tuple       =	"lower newer"
       return input_text, output_text




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

       lowerCAmelCase_:							Tuple       =	tokens + [tokenizer.unk_token]
       lowerCAmelCase_:							int       =	[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ )       ,						lowerCamelCase__ )




    def      _a    (    self ):
       lowerCAmelCase_:							Dict       =	self.get_tokenizer()

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




    @slow
    def      _a    (    self ):
       lowerCAmelCase_:							Dict       =	self.tokenizer_class.from_pretrained("allenai/longformer-base-4096" )

       lowerCAmelCase_:							Any       =	tokenizer.encode("sequence builders"       ,						add_special_tokens=lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	tokenizer.encode("multi-sequence build"       ,						add_special_tokens=lowerCamelCase__ )

       lowerCAmelCase_:							Optional[Any]       =	tokenizer.encode(
           "sequence builders"       ,						add_special_tokens=lowerCamelCase__       ,						add_prefix_space=lowerCamelCase__ )
       lowerCAmelCase_:							List[str]       =	tokenizer.encode(
           "sequence builders"       ,						"multi-sequence build"       ,						add_special_tokens=lowerCamelCase__       ,						add_prefix_space=lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	tokenizer.build_inputs_with_special_tokens(lowerCamelCase__       ,						lowerCamelCase__ )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	self.get_tokenizer()

       lowerCAmelCase_:							Any       =	"Encode this sequence."
       lowerCAmelCase_:							Dict       =	tokenizer.byte_encoder[" ".encode("utf-8" )[0]]

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

       lowerCAmelCase_:							Any       =	tokenizer.encode(lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__       ,						add_prefix_space=lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	tokenizer.convert_ids_to_tokens(encoded[0] )[0]
       self.assertEqual(lowerCamelCase__       ,						lowerCamelCase__ )

       tokenizer.add_special_tokens({"bos_token": "<s>"} )
       lowerCAmelCase_:							List[str]       =	tokenizer.encode(lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	tokenizer.convert_ids_to_tokens(encoded[1] )[0]
       self.assertNotEqual(lowerCamelCase__       ,						lowerCamelCase__ )

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

       lowerCAmelCase_:							Tuple       =	"Encode <mask> sequence"
       lowerCAmelCase_:							Dict       =	"Encode <mask>sequence"

       lowerCAmelCase_:							List[str]       =	tokenizer.encode(lowerCamelCase__ )
       lowerCAmelCase_:							Union[str, Any]       =	encoded.index(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
       self.assertEqual(lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	tokenizer.encode(lowerCamelCase__ )
       lowerCAmelCase_:							str       =	encoded.index(lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
       self.assertNotEqual(lowerCamelCase__       ,						lowerCamelCase__ )




    def      _a    (    self ):
       pass




    def      _a    (    self ):
       for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
             lowerCAmelCase_:							Tuple       =	self.rust_tokenizer_class.from_pretrained(lowerCamelCase__       ,						**lowerCamelCase__ )
             lowerCAmelCase_:							int       =	self.tokenizer_class.from_pretrained(lowerCamelCase__       ,						**lowerCamelCase__ )
             lowerCAmelCase_:							List[Any]       =	"A, <mask> AllenNLP sentence."
             lowerCAmelCase_:							Optional[int]       =	tokenizer_r.encode_plus(lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__       ,						return_token_type_ids=lowerCamelCase__ )
             lowerCAmelCase_:							Union[str, Any]       =	tokenizer_p.encode_plus(lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__       ,						return_token_type_ids=lowerCamelCase__ )

             # 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_:							str       =	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, 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(
                 lowerCamelCase__       ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )
             self.assertSequenceEqual(
                 lowerCamelCase__       ,						["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"] )




    def      _a    (    self ):
       for trim_offsets, add_prefix_space in itertools.product([True, False]       ,						repeat=2 ):
          lowerCAmelCase_:							Union[str, Any]       =	self.rust_tokenizer_class.from_pretrained(
              self.tmpdirname       ,						use_fast=lowerCamelCase__       ,						add_prefix_space=lowerCamelCase__       ,						trim_offsets=lowerCamelCase__ )

          lowerCAmelCase_:							List[Any]       =	json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
          lowerCAmelCase_:							Optional[int]       =	json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )

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

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




    def      _a    (    self ):
       # 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})''' ):
             lowerCAmelCase_:							Union[str, Any]       =	"hello"  # `hello` is a token in the vocabulary of `pretrained_name`
             lowerCAmelCase_:							Tuple       =	F'''{text_of_1_token} {text_of_1_token}'''

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

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

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

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

             lowerCAmelCase_:							List[Any]       =	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_:							int       =	self.rust_tokenizer_class.from_pretrained(
                 lowerCamelCase__       ,						use_fast=lowerCamelCase__       ,						add_prefix_space=lowerCamelCase__       ,						trim_offsets=lowerCamelCase__ )
             lowerCAmelCase_:							int       =	tokenizer_r(lowerCamelCase__       ,						return_offsets_mapping=lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__ )
             self.assertEqual(encoding.offset_mapping[0]       ,						(1, 1 + len(lowerCamelCase__ )) )
             self.assertEqual(
                 encoding.offset_mapping[1]       ,						(1 + len(lowerCamelCase__ ) + 1, 1 + len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))       ,						)

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

             lowerCAmelCase_:							List[Any]       =	self.rust_tokenizer_class.from_pretrained(
                 lowerCamelCase__       ,						use_fast=lowerCamelCase__       ,						add_prefix_space=lowerCamelCase__       ,						trim_offsets=lowerCamelCase__ )
             lowerCAmelCase_:							Any       =	tokenizer_r(lowerCamelCase__       ,						return_offsets_mapping=lowerCamelCase__       ,						add_special_tokens=lowerCamelCase__ )
             self.assertEqual(encoding.offset_mapping[0]       ,						(0, 1 + len(lowerCamelCase__ )) )
             self.assertEqual(
                 encoding.offset_mapping[1]       ,						(1 + len(lowerCamelCase__ ), 1 + len(lowerCamelCase__ ) + 1 + len(lowerCamelCase__ ))       ,						)
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


a  :      List[str]            =    {
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      str            =    ["""EfficientFormerImageProcessor"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[str]            =    [
     """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """EfficientFormerForImageClassification""",
     """EfficientFormerForImageClassificationWithTeacher""",
     """EfficientFormerModel""",
     """EfficientFormerPreTrainedModel""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a  :      List[Any]            =    [
     """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFEfficientFormerForImageClassification""",
     """TFEfficientFormerForImageClassificationWithTeacher""",
     """TFEfficientFormerModel""",
     """TFEfficientFormerPreTrainedModel""",
 ]

if TYPE_CHECKING:
 from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .image_processing_efficientformer import EfficientFormerImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_efficientformer import (
      EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      EfficientFormerForImageClassification,
      EfficientFormerForImageClassificationWithTeacher,
      EfficientFormerModel,
      EfficientFormerPreTrainedModel,
  )
 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_efficientformer import (
      TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFEfficientFormerForImageClassification,
      TFEfficientFormerForImageClassificationWithTeacher,
      TFEfficientFormerModel,
      TFEfficientFormerPreTrainedModel,
  )

else:
 import sys

 a  :      Tuple            =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)