





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__     ( __snake_case ,     unittest.TestCase    ):
      _UpperCAmelCase			:Union[str, Any]							=     LongformerTokenizer
      _UpperCAmelCase			:Optional[Any]							=     True
      _UpperCAmelCase			:Any							=     LongformerTokenizerFast
      _UpperCAmelCase			:List[str]							=     True





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       super().setUp()

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

       UpperCamelCase			:      Dict 			=     os.path.join(self.tmpdirname ,							VOCAB_FILES_NAMES["vocab_file"]     )
       UpperCamelCase			:      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  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




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





      def  __UpperCamelCase(				self ,							**A_     ):
       '''simple docstring'''




       kwargs.update(self.special_tokens_map     )
       return self.rust_tokenizer_class.from_pretrained(self.tmpdirname ,							**A_     )





      def  __UpperCamelCase(				self ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Any 			=     "lower newer"
       UpperCamelCase			:      str 			=     "lower newer"
       return input_text, output_text





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Union[str, Any] 			=     self.tokenizer_class(self.vocab_file ,							self.merges_file ,							**self.special_tokens_map     )
       UpperCamelCase			:      str 			=     "lower newer"
       UpperCamelCase			:      Tuple 			=     ["l", "o", "w", "er", "\u0120", "n", "e", "w", "er"]
       UpperCamelCase			:      int 			=     tokenizer.tokenize(A_     )  # , add_prefix_space=True)
       self.assertListEqual(A_ ,							A_     )

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





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[Any] 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.tokenizer_class.from_pretrained("allenai/longformer-base-4096"     )

       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode("sequence builders" ,							add_special_tokens=A_     )
       UpperCamelCase			:      Dict 			=     tokenizer.encode("multi-sequence build" ,							add_special_tokens=A_     )

       UpperCamelCase			:      Optional[Any] 			=     tokenizer.encode(
           "sequence builders" ,							add_special_tokens=A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode(
           "sequence builders" ,							"multi-sequence build" ,							add_special_tokens=A_ ,							add_prefix_space=A_     )

       UpperCamelCase			:      List[str] 			=     tokenizer.build_inputs_with_special_tokens(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.build_inputs_with_special_tokens(A_ ,							A_     )

       assert encoded_sentence == encoded_text_from_decode
       assert encoded_pair == encoded_pair_from_decode





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

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

       # Testing encoder arguments
       UpperCamelCase			:      Optional[int] 			=     tokenizer.encode(A_ ,							add_special_tokens=A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      str 			=     tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
       self.assertNotEqual(A_ ,							A_     )

       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.encode(A_ ,							add_special_tokens=A_ ,							add_prefix_space=A_     )
       UpperCamelCase			:      Tuple 			=     tokenizer.convert_ids_to_tokens(encoded[0]     )[0]
       self.assertEqual(A_ ,							A_     )

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

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

       UpperCamelCase			:      Any 			=     "Encode <mask> sequence"
       UpperCamelCase			:      Optional[int] 			=     "Encode <mask>sequence"

       UpperCamelCase			:      List[Any] 			=     tokenizer.encode(A_     )
       UpperCamelCase			:      Optional[int] 			=     encoded.index(A_     )
       UpperCamelCase			:      Optional[Any] 			=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
       self.assertEqual(A_ ,							A_     )

       UpperCamelCase			:      Dict 			=     tokenizer.encode(A_     )
       UpperCamelCase			:      List[str] 			=     encoded.index(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]     )[0]
       self.assertNotEqual(A_ ,							A_     )





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       pass





      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

         UpperCamelCase			:      Optional[Any] 			=     tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]     )
         UpperCamelCase			:      int 			=     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  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

        UpperCamelCase			:      Optional[Any] 			=     json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()     )
        UpperCamelCase			:      Union[str, 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  __UpperCamelCase(				self     ):
       '''simple docstring'''




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

         UpperCamelCase			:      List[Any] 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      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_     )) ,							)

         UpperCamelCase			:      Any 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      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_     )) ,							)

         UpperCamelCase			:      int 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Union[str, 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_     )) ,							)

         UpperCamelCase			:      Any 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      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_     ), len(A_     ) + 1 + len(A_     )) ,							)

         UpperCamelCase			:      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)),
         # )

         UpperCamelCase			:      str 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Dict 			=     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_     )) ,							)

         UpperCamelCase			:      Dict 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Union[str, Any] 			=     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_     )) ,							)

         UpperCamelCase			:      Dict 			=     self.rust_tokenizer_class.from_pretrained(
             A_ ,							use_fast=A_ ,							add_prefix_space=A_ ,							trim_offsets=A_     )
         UpperCamelCase			:      Optional[Any] 			=     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_     )) ,							)







import math

import tensorflow as tf
from packaging import version



def 	A_       (  _lowerCAmelCase  )		->				Any:
 UpperCamelCase			:      List[Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      Any 			=     0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0  )							,     x.dtype  )  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				Dict:
 UpperCamelCase			:      Union[str, Any] 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(math.pi							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      int 			=     0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi  ) * (x + coeff * tf.pow(_lowerCAmelCase							,     3  ))  ))

 return x * cdf



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      str 			=     tf.convert_to_tensor(_lowerCAmelCase  )

 return x * tf.tanh(tf.math.softplus(_lowerCAmelCase  )  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 UpperCamelCase			:      Tuple 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(0.044_715							,     x.dtype  )
 UpperCamelCase			:      Optional[Any] 			=     tf.cast(0.7_978_845_608							,     x.dtype  )

 return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)  ))



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 UpperCamelCase			:      Any 			=     tf.convert_to_tensor(_lowerCAmelCase  )
 UpperCamelCase			:      List[Any] 			=     tf.cast(1.702							,     x.dtype  )
 return x * tf.math.sigmoid(coeff * x  )



def 	A_       (  _lowerCAmelCase  )		->				List[Any]:
 return tf.clip_by_value(_gelu(_lowerCAmelCase  )							,     -10							,     10  )



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase=-1  )		->				str:
 UpperCamelCase			,					UpperCamelCase			:      List[Any] 			=     tf.split(_lowerCAmelCase							,     2							,     axis=_lowerCAmelCase  )
 return a * tf.math.sigmoid(_lowerCAmelCase  )


if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):



    def 	A_       (  _lowerCAmelCase  )		->				Any:
     return tf.keras.activations.gelu(_lowerCAmelCase							,     approximate=_lowerCAmelCase  )

    __lowerCamelCase :  Optional[int]										=  tf.keras.activations.gelu


    __lowerCamelCase :  int										=  approximate_gelu_wrap
else:
    __lowerCamelCase :  List[Any]										=  _gelu
    __lowerCamelCase :  Optional[Any]										=  _gelu_new


__lowerCamelCase :  Any										=  {
    """gelu""": gelu,
    """gelu_10""": gelu_aa,
    """gelu_fast""": gelu_fast,
    """gelu_new""": gelu_new,
    """glu""": glu,
    """mish""": mish,
    """quick_gelu""": quick_gelu,
    """relu""": tf.keras.activations.relu,
    """sigmoid""": tf.keras.activations.sigmoid,
    """silu""": tf.keras.activations.swish,
    """swish""": tf.keras.activations.swish,
    """tanh""": tf.keras.activations.tanh,
}



def 	A_       (  _lowerCAmelCase  )		->				Optional[Any]:
 if activation_string in ACTaFN:
  return ACTaFN[activation_string]
 else:
  raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()  )}"""  )
