






'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
   import tensorflow as tf

   from transformers import TFLEDForConditionalGeneration, TFLEDModel







@require_tf
class 	SCREAMING_SNAKE_CASE	:
       snake_case__    :     Union[str, Any]   		=			LEDConfig
       snake_case__    :     Tuple   		=			{}
       snake_case__    :     int   		=			'gelu'




       def __init__(					self      : int	,    A__      : Any	,    A__      : Any=13	,    A__      : Optional[int]=7	,    A__      : List[Any]=True	,    A__      : Any=False	,    A__      : Optional[int]=99	,    A__      : Optional[Any]=32	,    A__      : Union[str, Any]=2	,    A__      : List[str]=4	,    A__      : List[Any]=37	,    A__      : List[Any]=0.1	,    A__      : str=0.1	,    A__      : Union[str, Any]=20	,    A__      : Tuple=2	,    A__      : int=1	,    A__      : Tuple=0	,    A__      : Union[str, Any]=4	,    ):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				parent
            __lowerCamelCase						:       Dict				   =				batch_size
            __lowerCamelCase						:       List[str]				   =				seq_length
            __lowerCamelCase						:       str				   =				is_training
            __lowerCamelCase						:       Optional[int]				   =				use_labels
            __lowerCamelCase						:       str				   =				vocab_size
            __lowerCamelCase						:       str				   =				hidden_size
            __lowerCamelCase						:       List[str]				   =				num_hidden_layers
            __lowerCamelCase						:       Optional[Any]				   =				num_attention_heads
            __lowerCamelCase						:       List[Any]				   =				intermediate_size

            __lowerCamelCase						:       Dict				   =				hidden_dropout_prob
            __lowerCamelCase						:       Dict				   =				attention_probs_dropout_prob
            __lowerCamelCase						:       List[Any]				   =				max_position_embeddings
            __lowerCamelCase						:       Optional[Any]				   =				eos_token_id
            __lowerCamelCase						:       Tuple				   =				pad_token_id
            __lowerCamelCase						:       str				   =				bos_token_id
            __lowerCamelCase						:       Union[str, Any]				   =				attention_window

            # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
            # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
            # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
            # because its local attention only attends to `self.attention_window` and one before and one after
            __lowerCamelCase						:       int				   =				self.attention_window + 2

            # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
            # the `test_attention_outputs` and `test_hidden_states_output` tests
            __lowerCamelCase						:       Any				   =				(
                self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
            )




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				ids_tensor([self.batch_size, self.seq_length - 1]	,    self.vocab_size					)
            __lowerCamelCase						:       Union[str, Any]				   =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					)	,    1					)
            __lowerCamelCase						:       int				   =				tf.concat([input_ids, eos_tensor]	,    axis=1					)

            __lowerCamelCase						:       Tuple				   =				ids_tensor([self.batch_size, self.seq_length]	,    self.vocab_size					)

            __lowerCamelCase						:       Optional[int]				   =				self.config_cls(
                vocab_size=self.vocab_size	,    d_model=self.hidden_size	,    encoder_layers=self.num_hidden_layers	,    decoder_layers=self.num_hidden_layers	,    encoder_attention_heads=self.num_attention_heads	,    decoder_attention_heads=self.num_attention_heads	,    encoder_ffn_dim=self.intermediate_size	,    decoder_ffn_dim=self.intermediate_size	,    dropout=self.hidden_dropout_prob	,    attention_dropout=self.attention_probs_dropout_prob	,    max_position_embeddings=self.max_position_embeddings	,    eos_token_ids=[2]	,    bos_token_id=self.bos_token_id	,    pad_token_id=self.pad_token_id	,    decoder_start_token_id=self.pad_token_id	,    attention_window=self.attention_window	,    **self.config_updates	,    )
            __lowerCamelCase						:       List[Any]				   =				prepare_led_inputs_dict(A__	,    A__	,    A__					)
            __lowerCamelCase						:       Any				   =				tf.concat(
                [tf.zeros_like(A__					)[:, :-1], tf.ones_like(A__					)[:, -1:]]	,    axis=-1	,    )
            __lowerCamelCase						:       Dict				   =				global_attention_mask
            return config, inputs_dict




       def 					a_					(					self      : Optional[int]	,    A__      : int	,    A__      : List[Any]					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				TFLEDModel(config=A__					).get_decoder()
            __lowerCamelCase						:       Union[str, Any]				   =				inputs_dict["""input_ids"""]

            __lowerCamelCase						:       List[Any]				   =				input_ids[:1, :]
            __lowerCamelCase						:       Optional[Any]				   =				inputs_dict["""attention_mask"""][:1, :]
            __lowerCamelCase						:       Optional[int]				   =				1

            # first forward pass
            __lowerCamelCase						:       Union[str, Any]				   =				model(A__	,    attention_mask=A__	,    use_cache=A__					)

            __lowerCamelCase   ,      __lowerCamelCase						:       str				   =				outputs.to_tuple()

            # create hypothetical next token and extent to next_input_ids
            __lowerCamelCase						:       Union[str, Any]				   =				ids_tensor((self.batch_size, 3)	,    config.vocab_size					)
            __lowerCamelCase						:       Dict				   =				tf.cast(ids_tensor((self.batch_size, 3)	,    2					)	,    tf.inta					)

            # append to next input_ids and
            __lowerCamelCase						:       Dict				   =				tf.concat([input_ids, next_tokens]	,    axis=-1					)
            __lowerCamelCase						:       Optional[Any]				   =				tf.concat([attention_mask, next_attn_mask]	,    axis=-1					)

            __lowerCamelCase						:       List[str]				   =				model(A__	,    attention_mask=A__					)[0]
            __lowerCamelCase						:       Dict				   =				model(A__	,    attention_mask=A__	,    past_key_values=A__					)[0]

            self.parent.assertEqual(next_tokens.shape[1]	,    output_from_past.shape[1]					)

            # select random slice
            __lowerCamelCase						:       Optional[Any]				   =				int(ids_tensor((1,)	,    output_from_past.shape[-1]					)					)
            __lowerCamelCase						:       Optional[Any]				   =				output_from_no_past[:, -3:, random_slice_idx]
            __lowerCamelCase						:       int				   =				output_from_past[:, :, random_slice_idx]

            # test that outputs are equal for slice
            tf.debugging.assert_near(A__	,    A__	,    rtol=1e-3					)

def     __lowercase  (_lowercase,						_lowercase,						_lowercase,						_lowercase=None,						_lowercase=None,						_lowercase=None,						_lowercase=None,						)   ->     List[str]:


     """simple docstring"""
     if attention_mask is None:
          __lowerCamelCase						:       Union[str, Any]				   =				tf.cast(tf.math.not_equal(_lowercase,						config.pad_token_id     ),						tf.inta     )
     if decoder_attention_mask is None:
          __lowerCamelCase						:       List[Any]				   =				tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape,						dtype=tf.inta     ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:],						config.pad_token_id     ),						tf.inta     ),
              ],						axis=-1,						)
     if head_mask is None:
          __lowerCamelCase						:       Optional[int]				   =				tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
     if decoder_head_mask is None:
          __lowerCamelCase						:       str				   =				tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
     return {
         "input_ids": input_ids,
         "attention_mask": attention_mask,
         "decoder_input_ids": decoder_input_ids,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
     }







@require_tf
class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     int   		=			(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
       snake_case__    :     Union[str, Any]   		=			(TFLEDForConditionalGeneration,) if is_tf_available() else ()
       snake_case__    :     Optional[Any]   		=			(
           {
               'conversational': TFLEDForConditionalGeneration,
               'feature-extraction': TFLEDModel,
               'summarization': TFLEDForConditionalGeneration,
               'text2text-generation': TFLEDForConditionalGeneration,
               'translation': TFLEDForConditionalGeneration,
           }
           if is_tf_available()
           else {}
       )
       snake_case__    :     Union[str, Any]   		=			True
       snake_case__    :     List[str]   		=			False
       snake_case__    :     Optional[int]   		=			False
       snake_case__    :     Optional[int]   		=			False




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase						:       Any				   =				TFLEDModelTester(self					)
            __lowerCamelCase						:       Any				   =				ConfigTester(self	,    config_class=A__					)




       def 					a_					(					self      : Any					):


            """simple docstring"""







            self.config_tester.run_common_tests()




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            __lowerCamelCase						:       Union[str, Any]				   =				self.model_tester.prepare_config_and_inputs_for_common()
            self.model_tester.check_decoder_model_past_large_inputs(*A__					)




       def 					a_					(					self      : str					):


            """simple docstring"""







            __lowerCamelCase   ,      __lowerCamelCase						:       List[Any]				   =				self.model_tester.prepare_config_and_inputs_for_common()
            __lowerCamelCase						:       Optional[Any]				   =				tf.zeros_like(inputs_dict["""attention_mask"""]					)
            __lowerCamelCase						:       List[str]				   =				2
            __lowerCamelCase						:       Dict				   =				tf.where(
                tf.range(self.model_tester.seq_length					)[None, :] < num_global_attn_indices	,    1	,    inputs_dict["""global_attention_mask"""]	,    )

            __lowerCamelCase						:       str				   =				True
            __lowerCamelCase						:       Dict				   =				self.model_tester.seq_length
            __lowerCamelCase						:       Union[str, Any]				   =				self.model_tester.encoder_seq_length

            def check_decoder_attentions_output(A__      : List[Any]					):
                 __lowerCamelCase						:       List[str]				   =				outputs.decoder_attentions
                 self.assertEqual(len(A__					)	,    self.model_tester.num_hidden_layers					)
                 self.assertListEqual(
                     list(decoder_attentions[0].shape[-3:]					)	,    [self.model_tester.num_attention_heads, seq_length, seq_length]	,    )

            def check_encoder_attentions_output(A__      : Any					):
                 __lowerCamelCase						:       str				   =				[t.numpy() for t in outputs.encoder_attentions]
                 __lowerCamelCase						:       Tuple				   =				[t.numpy() for t in outputs.encoder_global_attentions]
                 self.assertEqual(len(A__					)	,    self.model_tester.num_hidden_layers					)
                 self.assertEqual(len(A__					)	,    self.model_tester.num_hidden_layers					)
                 self.assertListEqual(
                     list(attentions[0].shape[-3:]					)	,    [self.model_tester.num_attention_heads, seq_length, seq_length]	,    )
                 self.assertListEqual(
                     list(global_attentions[0].shape[-3:]					)	,    [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]	,    )

            for model_class in self.all_model_classes:
                 __lowerCamelCase						:       str				   =				True
                 __lowerCamelCase						:       Tuple				   =				False
                 __lowerCamelCase						:       Tuple				   =				False
                 __lowerCamelCase						:       Union[str, Any]				   =				model_class(A__					)
                 __lowerCamelCase						:       List[str]				   =				model(self._prepare_for_class(A__	,    A__					)					)
                 __lowerCamelCase						:       str				   =				len(A__					)
                 self.assertEqual(config.output_hidden_states	,    A__					)
                 check_encoder_attentions_output(A__					)

                 if self.is_encoder_decoder:
                      __lowerCamelCase						:       Optional[int]				   =				model_class(A__					)
                      __lowerCamelCase						:       Dict				   =				model(self._prepare_for_class(A__	,    A__					)					)
                      self.assertEqual(config.output_hidden_states	,    A__					)
                      check_decoder_attentions_output(A__					)

                 # Check that output attentions can also be changed via the config
                 del inputs_dict["output_attentions"]
                 __lowerCamelCase						:       Tuple				   =				True
                 __lowerCamelCase						:       int				   =				model_class(A__					)
                 __lowerCamelCase						:       Optional[Any]				   =				model(self._prepare_for_class(A__	,    A__					)					)
                 self.assertEqual(config.output_hidden_states	,    A__					)
                 check_encoder_attentions_output(A__					)

                 # Check attention is always last and order is fine
                 __lowerCamelCase						:       Optional[int]				   =				True
                 __lowerCamelCase						:       Optional[int]				   =				True
                 __lowerCamelCase						:       Dict				   =				model_class(A__					)
                 __lowerCamelCase						:       List[Any]				   =				model(self._prepare_for_class(A__	,    A__					)					)

                 self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)	,    len(A__					)					)
                 self.assertEqual(model.config.output_hidden_states	,    A__					)
                 check_encoder_attentions_output(A__					)




       @unittest.skip("""LED keeps using potentially symbolic tensors in conditionals and breaks tracing."""					)
       def 					a_					(					self      : int					):


            """simple docstring"""







            pass




       def 					a_					(					self      : Tuple					):


            """simple docstring"""







            pass

def     __lowercase  (_lowercase     )   ->     Optional[Any]:


     """simple docstring"""
     return tf.constant(_lowercase,						dtype=tf.intaa     )


UpperCAmelCase__						:int          =			1e-4







@slow
@require_tf
class 	SCREAMING_SNAKE_CASE	(      unittest.TestCase  ):




       def 					a_					(					self      : Optional[int]					):


            """simple docstring"""







            __lowerCamelCase						:       str				   =				TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""					).led

            # change to intended input here
            __lowerCamelCase						:       str				   =				_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
            __lowerCamelCase						:       Union[str, Any]				   =				_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
            __lowerCamelCase						:       List[Any]				   =				prepare_led_inputs_dict(model.config	,    A__	,    A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				model(**A__					)[0]
            __lowerCamelCase						:       Optional[Any]				   =				(1, 1024, 768)
            self.assertEqual(output.shape	,    A__					)
            # change to expected output here
            __lowerCamelCase						:       Optional[Any]				   =				tf.convert_to_tensor(
                [[2.3050, 2.8279, 0.6531], [-1.8457, -0.1455, -3.5661], [-1.0186, 0.4586, -2.2043]]	,    )
            tf.debugging.assert_near(output[:, :3, :3]	,    A__	,    atol=1e-3					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            __lowerCamelCase						:       Tuple				   =				TFLEDForConditionalGeneration.from_pretrained("""allenai/led-base-16384"""					)

            # change to intended input here
            __lowerCamelCase						:       List[str]				   =				_long_tensor([512 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
            __lowerCamelCase						:       List[Any]				   =				_long_tensor([128 * [0, 31414, 232, 328, 740, 1140, 12695, 69]]					)
            __lowerCamelCase						:       Any				   =				prepare_led_inputs_dict(model.config	,    A__	,    A__					)
            __lowerCamelCase						:       Any				   =				model(**A__					)[0]
            __lowerCamelCase						:       Any				   =				(1, 1024, model.config.vocab_size)
            self.assertEqual(output.shape	,    A__					)
            # change to expected output here
            __lowerCamelCase						:       List[Any]				   =				tf.convert_to_tensor(
                [[33.6507, 6.4572, 16.8089], [5.8739, -2.4238, 11.2902], [-3.2139, -4.3149, 4.2783]]	,    )
            tf.debugging.assert_near(output[:, :3, :3]	,    A__	,    atol=1e-3	,    rtol=1e-3					)











'''simple docstring'''

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 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_    ,   unittest.TestCase  ):
       snake_case__    :     str   		=			RobertaTokenizer
       snake_case__    :     Optional[int]   		=			RobertaTokenizerFast
       snake_case__    :     Union[str, Any]   		=			True
       snake_case__    :     Tuple   		=			{'cls_token': '<s>'}




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            super().setUp()

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

            __lowerCamelCase						:       Optional[int]				   =				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 					a_					(					self      : Tuple	,    **A__      : Dict					):


            """simple docstring"""







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




       def 					a_					(					self      : str	,    **A__      : List[Any]					):


            """simple docstring"""







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




       def 					a_					(					self      : Tuple	,    A__      : Any					):


            """simple docstring"""







            __lowerCamelCase						:       int				   =				"""lower newer"""
            __lowerCamelCase						:       int				   =				"""lower newer"""
            return input_text, output_text




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







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

            __lowerCamelCase						:       List[str]				   =				tokens + [tokenizer.unk_token]
            __lowerCamelCase						:       str				   =				[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(A__					)	,    A__					)




       def 					a_					(					self      : Dict					):


            """simple docstring"""







            __lowerCamelCase						:       List[Any]				   =				self.get_tokenizer()

            self.assertListEqual(tokenizer.encode("""Hello world!"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 2]					)
            self.assertListEqual(
                tokenizer.encode("""Hello world! cécé herlolip 418"""	,    add_special_tokens=A__					)	,    [0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]	,    )




       @slow
       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Dict				   =				self.tokenizer_class.from_pretrained("""roberta-base"""					)

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

            __lowerCamelCase						:       Dict				   =				tokenizer.encode(
                """sequence builders"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       List[Any]				   =				tokenizer.encode(
                """sequence builders"""	,    """multi-sequence build"""	,    add_special_tokens=A__	,    add_prefix_space=A__					)

            __lowerCamelCase						:       List[str]				   =				tokenizer.build_inputs_with_special_tokens(A__					)
            __lowerCamelCase						:       Tuple				   =				tokenizer.build_inputs_with_special_tokens(A__	,    A__					)

            assert encoded_sentence == encoded_text_from_decode
            assert encoded_pair == encoded_pair_from_decode




       def 					a_					(					self      : List[str]					):


            """simple docstring"""







            __lowerCamelCase						:       Optional[Any]				   =				self.get_tokenizer()

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

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

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__	,    add_special_tokens=A__	,    add_prefix_space=A__					)
            __lowerCamelCase						:       int				   =				tokenizer.convert_ids_to_tokens(encoded[0]					)[0]
            self.assertEqual(A__	,    A__					)

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

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

            __lowerCamelCase						:       str				   =				"""Encode <mask> sequence"""
            __lowerCamelCase						:       Optional[int]				   =				"""Encode <mask>sequence"""

            __lowerCamelCase						:       Optional[Any]				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       str				   =				encoded.index(A__					)
            __lowerCamelCase						:       Any				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertEqual(A__	,    A__					)

            __lowerCamelCase						:       Any				   =				tokenizer.encode(A__					)
            __lowerCamelCase						:       Union[str, Any]				   =				encoded.index(A__					)
            __lowerCamelCase						:       Optional[int]				   =				tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]					)[0]
            self.assertNotEqual(A__	,    A__					)




       def 					a_					(					self      : Union[str, Any]					):


            """simple docstring"""







            pass




       def 					a_					(					self      : str					):


            """simple docstring"""







            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(A__	,    **A__					)
                      __lowerCamelCase						:       List[Any]				   =				self.tokenizer_class.from_pretrained(A__	,    **A__					)
                      __lowerCamelCase						:       Tuple				   =				"""A, <mask> AllenNLP sentence."""
                      __lowerCamelCase						:       int				   =				tokenizer_r.encode_plus(A__	,    add_special_tokens=A__	,    return_token_type_ids=A__					)
                      __lowerCamelCase						:       List[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						:       Optional[int]				   =				tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""]					)
                      __lowerCamelCase						:       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, 50264, 3823, 487, 21992, 3645, 4, 2]					)
                      self.assertSequenceEqual(tokens_r["""input_ids"""]	,    [0, 250, 6, 50264, 3823, 487, 21992, 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 					a_					(					self      : str					):


            """simple docstring"""







            for trim_offsets, add_prefix_space in itertools.product([True, False]	,    repeat=2					):
                 __lowerCamelCase						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                     self.tmpdirname	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)

                 __lowerCamelCase						:       Union[str, Any]				   =				json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()					)
                 __lowerCamelCase						:       Dict				   =				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 					a_					(					self      : List[Any]					):


            """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						:       List[Any]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       str				   =				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						:       Tuple				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       List[str]				   =				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						:       int				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       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__					))	,    )

                      __lowerCamelCase						:       Optional[int]				   =				self.rust_tokenizer_class.from_pretrained(
                          A__	,    use_fast=A__	,    add_prefix_space=A__	,    trim_offsets=A__					)
                      __lowerCamelCase						:       Tuple				   =				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						:       str				   =				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						:       Tuple				   =				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]	,    (1, 1 + len(A__					))					)
                      self.assertEqual(
                          encoding.offset_mapping[1]	,    (1 + len(A__					) + 1, 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						:       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						:       Optional[int]				   =				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__					))	,    )



