


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


__lowerCamelCase   	=  logging.get_logger(__name__)

__lowerCamelCase   	=  {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__lowerCamelCase   	=  {
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''allenai/longformer-base-4096''': (
            '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json'''
        ),
    },
}

__lowerCamelCase   	=  {
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}


class 	a__						(    lowerCAmelCase_     ):
       lowerCamelCase__:  List[Any]				     =  VOCAB_FILES_NAMES
       lowerCamelCase__:  Any				     =  PRETRAINED_VOCAB_FILES_MAP
       lowerCamelCase__:  List[Any]				     =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       lowerCamelCase__:  List[Any]				     =  ["""input_ids""", """attention_mask"""]
       lowerCamelCase__:  int				     =  LongformerTokenizer

       def __init__( self   :  Union[str, Any]   ,				lowerCamelCase_   :  str=None   ,				lowerCamelCase_   :  List[Any]=None   ,				lowerCamelCase_   :  Any=None   ,				lowerCamelCase_   :  Any="replace"   ,				lowerCamelCase_   :  str="<s>"   ,				lowerCamelCase_   :  Dict="</s>"   ,				lowerCamelCase_   :  Optional[int]="</s>"   ,				lowerCamelCase_   :  Optional[int]="<s>"   ,				lowerCamelCase_   :  Dict="<unk>"   ,				lowerCamelCase_   :  Any="<pad>"   ,				lowerCamelCase_   :  List[str]="<mask>"   ,				lowerCamelCase_   :  Dict=False   ,				lowerCamelCase_   :  Optional[int]=True   ,				**lowerCamelCase_   :  Optional[Any]   ,				):
            super().__init__(
                lowerCamelCase_   ,				lowerCamelCase_   ,				tokenizer_file=lowerCamelCase_   ,				errors=lowerCamelCase_   ,				bos_token=lowerCamelCase_   ,				eos_token=lowerCamelCase_   ,				sep_token=lowerCamelCase_   ,				cls_token=lowerCamelCase_   ,				unk_token=lowerCamelCase_   ,				pad_token=lowerCamelCase_   ,				mask_token=lowerCamelCase_   ,				add_prefix_space=lowerCamelCase_   ,				trim_offsets=lowerCamelCase_   ,				**lowerCamelCase_   ,				)

            a_     :  Optional[Any]				      =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
            if pre_tok_state.get("""add_prefix_space"""   ,				lowerCamelCase_     ) != add_prefix_space:
                 a_     :  int				      =     getattr(lowerCamelCase_   ,				pre_tok_state.pop("""type"""     )     )
                 a_     :  str				      =     add_prefix_space
                 a_     :  str				      =     pre_tok_class(**lowerCamelCase_     )

            a_     :  Optional[Any]				      =     add_prefix_space

            a_     :  str				      =     """post_processor"""
            a_     :  str				      =     getattr(self.backend_tokenizer   ,				lowerCamelCase_   ,				lowerCamelCase_     )
            if tokenizer_component_instance:
                 a_     :  Any				      =     json.loads(tokenizer_component_instance.__getstate__()     )

                 # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                 if "sep" in state:
                      a_     :  Optional[Any]				      =     tuple(state["""sep"""]     )
                 if "cls" in state:
                      a_     :  str				      =     tuple(state["""cls"""]     )

                 a_     :  Optional[Any]				      =     False

                 if state.get("""add_prefix_space"""   ,				lowerCamelCase_     ) != add_prefix_space:
                      a_     :  List[Any]				      =     add_prefix_space
                      a_     :  Any				      =     True

                 if state.get("""trim_offsets"""   ,				lowerCamelCase_     ) != trim_offsets:
                      a_     :  int				      =     trim_offsets
                      a_     :  str				      =     True

                 if changes_to_apply:
                      a_     :  Any				      =     getattr(lowerCamelCase_   ,				state.pop("""type"""     )     )
                      a_     :  Union[str, Any]				      =     component_class(**lowerCamelCase_     )
                      setattr(self.backend_tokenizer   ,				lowerCamelCase_   ,				lowerCamelCase_     )

       @property
       def     UpperCAmelCase( self   :  Dict     ):
            if self._mask_token is None:
                 if self.verbose:
                      logger.error("""Using mask_token, but it is not set yet."""     )
                 return None
            return str(self._mask_token     )

       @mask_token.setter
       def     UpperCAmelCase( self   :  Optional[int]   ,				lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     AddedToken(lowerCamelCase_   ,				lstrip=lowerCamelCase_   ,				rstrip=lowerCamelCase_     ) if isinstance(lowerCamelCase_   ,				lowerCamelCase_     ) else value
            a_     :  Optional[int]				      =     value

       def     UpperCAmelCase( self   :  str   ,				*lowerCamelCase_   :  int   ,				**lowerCamelCase_   :  List[Any]     ):
            a_     :  Any				      =     kwargs.get("""is_split_into_words"""   ,				lowerCamelCase_     )
            assert self.add_prefix_space or not is_split_into_words, (
                F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._batch_encode_plus(*lowerCamelCase_   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Union[str, Any]   ,				*lowerCamelCase_   :  Dict   ,				**lowerCamelCase_   :  int     ):
            a_     :  List[str]				      =     kwargs.get("""is_split_into_words"""   ,				lowerCamelCase_     )

            assert self.add_prefix_space or not is_split_into_words, (
                F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
                "to use it with pretokenized inputs."
            )

            return super()._encode_plus(*lowerCamelCase_   ,				**lowerCamelCase_     )

       def     UpperCAmelCase( self   :  int   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[str] = None     ):
            a_     :  List[Any]				      =     self._tokenizer.model.save(lowerCamelCase_   ,				name=lowerCamelCase_     )
            return tuple(lowerCamelCase_     )

       def     UpperCAmelCase( self   :  str   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Dict=None     ):
            a_     :  List[Any]				      =     [self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                 return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]

       def     UpperCAmelCase( self   :  Optional[Any]   ,				lowerCamelCase_   :  List[int]   ,				lowerCamelCase_   :  Optional[List[int]] = None     ):
            a_     :  Any				      =     [self.sep_token_id]
            a_     :  Optional[int]				      =     [self.cls_token_id]

            if token_ids_a is None:
                 return len(cls + token_ids_a + sep     ) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]







import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
           SpeechaTextaDecoder,
           SpeechaTextaForCausalLM,
       )


@require_torch
class 	a__						:

       def __init__( self   :  Dict   ,				lowerCamelCase_   :  Any   ,				lowerCamelCase_   :  Dict=9_9   ,				lowerCamelCase_   :  List[Any]=1_3   ,				lowerCamelCase_   :  Dict=1_6   ,				lowerCamelCase_   :  Any=7   ,				lowerCamelCase_   :  Dict=True   ,				lowerCamelCase_   :  List[str]=True   ,				lowerCamelCase_   :  Tuple=True   ,				lowerCamelCase_   :  Any=False   ,				lowerCamelCase_   :  Union[str, Any]=True   ,				lowerCamelCase_   :  int=2   ,				lowerCamelCase_   :  Tuple=3_2   ,				lowerCamelCase_   :  int=4   ,				lowerCamelCase_   :  Dict=4   ,				lowerCamelCase_   :  str=3_0   ,				lowerCamelCase_   :  Optional[int]=0   ,				lowerCamelCase_   :  Optional[Any]=1   ,				lowerCamelCase_   :  Any=2   ,				lowerCamelCase_   :  Optional[int]=None   ,				):
            a_     :  Optional[Any]				      =     parent
            a_     :  Union[str, Any]				      =     batch_size
            a_     :  Tuple				      =     decoder_seq_length
            # For common tests
            a_     :  Union[str, Any]				      =     self.decoder_seq_length
            a_     :  List[str]				      =     is_training
            a_     :  int				      =     use_attention_mask
            a_     :  Dict				      =     use_labels

            a_     :  List[str]				      =     vocab_size
            a_     :  Optional[int]				      =     d_model
            a_     :  str				      =     d_model
            a_     :  Any				      =     decoder_layers
            a_     :  Tuple				      =     decoder_layers
            a_     :  Dict				      =     decoder_ffn_dim
            a_     :  List[str]				      =     decoder_attention_heads
            a_     :  List[Any]				      =     decoder_attention_heads
            a_     :  List[str]				      =     eos_token_id
            a_     :  Any				      =     bos_token_id
            a_     :  List[Any]				      =     pad_token_id
            a_     :  Dict				      =     decoder_start_token_id
            a_     :  Tuple				      =     use_cache
            a_     :  List[Any]				      =     max_position_embeddings

            a_     :  List[str]				      =     None
            a_     :  Any				      =     decoder_seq_length
            a_     :  List[Any]				      =     2
            a_     :  Optional[Any]				      =     1

       def     UpperCAmelCase( self   :  str     ):
            a_     :  Any				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size     )

            a_     :  Any				      =     None
            if self.use_attention_mask:
                 a_     :  Optional[Any]				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				vocab_size=2     )

            a_     :  Tuple				      =     None
            if self.use_labels:
                 a_     :  int				      =     ids_tensor([self.batch_size, self.decoder_seq_length]   ,				self.vocab_size     )

            a_     :  int				      =     SpeechaTextaConfig(
                vocab_size=self.vocab_size   ,				d_model=self.d_model   ,				decoder_layers=self.decoder_layers   ,				decoder_ffn_dim=self.decoder_ffn_dim   ,				decoder_attention_heads=self.decoder_attention_heads   ,				eos_token_id=self.eos_token_id   ,				bos_token_id=self.bos_token_id   ,				use_cache=self.use_cache   ,				pad_token_id=self.pad_token_id   ,				decoder_start_token_id=self.decoder_start_token_id   ,				max_position_embeddings=self.max_position_embeddings   ,				)

            return (
                config,
                input_ids,
                attention_mask,
                lm_labels,
            )

       def     UpperCAmelCase( self   :  Tuple   ,				lowerCamelCase_   :  List[str]   ,				lowerCamelCase_   :  str   ,				lowerCamelCase_   :  Optional[int]   ,				lowerCamelCase_   :  List[str]   ,				):
            a_     :  List[Any]				      =     True
            a_     :  Optional[int]				      =     SpeechaTextaDecoder(config=lowerCamelCase_     ).to(lowerCamelCase_     ).eval()
            a_     :  Optional[int]				      =     input_ids[:2]

            input_ids[input_ids == 0] += 1
            # first forward pass
            a_     :  Union[str, Any]				      =     model(lowerCamelCase_   ,				use_cache=lowerCamelCase_     )
            a_     :  Tuple				      =     model(lowerCamelCase_     )
            a_     :  str				      =     model(lowerCamelCase_   ,				use_cache=lowerCamelCase_     )

            self.parent.assertTrue(len(lowerCamelCase_     ) == len(lowerCamelCase_     )     )
            self.parent.assertTrue(len(lowerCamelCase_     ) == len(lowerCamelCase_     ) + 1     )

            a_     :  Tuple				      =     outputs["""past_key_values"""]

            # create hypothetical next token and extent to next_input_ids
            a_     :  Any				      =     ids_tensor((2, 1)   ,				config.vocab_size - 1     ) + 1

            # append to next input_ids and
            a_     :  str				      =     torch.cat([input_ids, next_tokens]   ,				dim=-1     )

            print(lowerCamelCase_     )

            a_     :  Any				      =     model(lowerCamelCase_     )["""last_hidden_state"""]
            a_     :  int				      =     model(lowerCamelCase_   ,				past_key_values=lowerCamelCase_     )["""last_hidden_state"""]

            # select random slice
            a_     :  Tuple				      =     ids_tensor((1,)   ,				output_from_past.shape[-1]     ).item()
            a_     :  List[str]				      =     output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
            a_     :  List[Any]				      =     output_from_past[:, 0, random_slice_idx].detach()

            # test that outputs are equal for slice
            assert torch.allclose(lowerCamelCase_   ,				lowerCamelCase_   ,				atol=1E-3     )

       def     UpperCAmelCase( self   :  Any     ):
            a_     :  List[Any]				      =     self.prepare_config_and_inputs()
            (
                (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      (
                a_
            )      ,      
            )     :  Optional[int]				      =     config_and_inputs

            a_     :  Tuple				      =     {
                """input_ids""": input_ids,
                """attention_mask""": attention_mask,
            }
            return config, inputs_dict


@require_torch
class 	a__						(    lowerCAmelCase_    ,							lowerCAmelCase_    ,							lowerCAmelCase_    ,							unittest.TestCase     ):
       lowerCamelCase__:  int				     =  (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
       lowerCamelCase__:  int				     =  (SpeechaTextaForCausalLM,) if is_torch_available() else ()
       lowerCamelCase__:  int				     =  {"""text-generation""": SpeechaTextaForCausalLM} if is_torch_available() else {}
       lowerCamelCase__:  Dict				     =  True
       lowerCamelCase__:  List[Any]				     =  False

       def     UpperCAmelCase( self   :  List[str]   ,				):
            a_     :  Optional[int]				      =     SpeechaTextaStandaloneDecoderModelTester(self   ,				is_training=lowerCamelCase_     )
            a_     :  str				      =     ConfigTester(self   ,				config_class=lowerCamelCase_     )

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  Optional[Any]     ):
            pass

       def     UpperCAmelCase( self   :  str     ):
            self.config_tester.run_common_tests()

       def     UpperCAmelCase( self   :  Dict     ):
            a_     :  int				      =     self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_decoder_model_past(*lowerCamelCase_     )

       def     UpperCAmelCase( self   :  int     ):
            return



