import os
from typing import List, Union

import tensorflow as tf
from tensorflow_text import BertTokenizer as BertTokenizerLayer
from tensorflow_text import FastBertTokenizer, ShrinkLongestTrimmer, case_fold_utfa, combine_segments, pad_model_inputs

from .tokenization_bert import BertTokenizer






class 				_SCREAMING_SNAKE_CASE( tf.keras.layers.Layer						):
   def __init__(     self				,  lowerCAmelCase_				,  lowerCAmelCase_				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = "longest"				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = 5_12				,  lowerCAmelCase_ = None				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = True				,  lowerCAmelCase_ = True				,  **lowerCAmelCase_				,  )						->List[str]:
        super().__init__()
        if use_fast_bert_tokenizer:
             a	:     Any						      =    FastBertTokenizer(
                 _UpperCAmelCase				,  token_out_type=tf.intaa				,  lower_case_nfd_strip_accents=_UpperCAmelCase				,  **_UpperCAmelCase     )
        else:
             a	:     List[str]						      =    tf.lookup.StaticVocabularyTable(
                 tf.lookup.KeyValueTensorInitializer(
                     keys=_UpperCAmelCase				,  key_dtype=tf.string				,  values=tf.range(tf.size(_UpperCAmelCase				,  out_type=tf.intaa     )				,  dtype=tf.intaa     )				,  value_dtype=tf.intaa				,  )				,  num_oov_buckets=1				,  )
             a	:     Optional[Any]						      =    BertTokenizerLayer(
                 _UpperCAmelCase				,  token_out_type=tf.intaa				,  lower_case=_UpperCAmelCase				,  **_UpperCAmelCase     )

        a	:     Any						      =    vocab_list
        a	:     List[str]						      =    do_lower_case
        a	:     Optional[int]						      =    cls_token_id or vocab_list.index('''[CLS]'''     )
        a	:     Optional[Any]						      =    sep_token_id or vocab_list.index('''[SEP]'''     )
        a	:     List[str]						      =    pad_token_id or vocab_list.index('''[PAD]'''     )
        a	:     Optional[Any]						      =    ShrinkLongestTrimmer(max_length - 3				,  axis=1     )  # Allow room for special tokens
        a	:     List[Any]						      =    max_length
        a	:     Optional[int]						      =    padding
        a	:     Tuple						      =    truncation
        a	:     Tuple						      =    pad_to_multiple_of
        a	:     Any						      =    return_token_type_ids
        a	:     Optional[Any]						      =    return_attention_mask
   @classmethod
   def _lowercase(     cls				,  lowerCAmelCase_				,  **lowerCAmelCase_     )						->Tuple:  # noqa: F821
        a	:     Optional[int]						      =    kwargs.pop('''do_lower_case'''				,  _UpperCAmelCase     )
        a	:     Optional[int]						      =    tokenizer.do_lower_case if do_lower_case is None else do_lower_case
        a	:     Tuple						      =    kwargs.pop('''cls_token_id'''				,  _UpperCAmelCase     )
        a	:     Tuple						      =    tokenizer.cls_token_id if cls_token_id is None else cls_token_id
        a	:     Union[str, Any]						      =    kwargs.pop('''sep_token_id'''				,  _UpperCAmelCase     )
        a	:     Union[str, Any]						      =    tokenizer.sep_token_id if sep_token_id is None else sep_token_id
        a	:     List[str]						      =    kwargs.pop('''pad_token_id'''				,  _UpperCAmelCase     )
        a	:     int						      =    tokenizer.pad_token_id if pad_token_id is None else pad_token_id

        a	:     List[str]						      =    tokenizer.get_vocab()
        a	:     Optional[int]						      =    sorted([(wordpiece, idx) for wordpiece, idx in vocab.items()]				,  key=lambda lowerCAmelCase_     : x[1]     )
        a	:     Optional[int]						      =    [entry[0] for entry in vocab]
        return cls(
            vocab_list=_UpperCAmelCase				,  do_lower_case=_UpperCAmelCase				,  cls_token_id=_UpperCAmelCase				,  sep_token_id=_UpperCAmelCase				,  pad_token_id=_UpperCAmelCase				,  **_UpperCAmelCase				,  )
   @classmethod
   def _lowercase(     cls				,  lowerCAmelCase_				,  *lowerCAmelCase_				,  **lowerCAmelCase_     )						->Any:
        try:
             a	:     Optional[int]						      =    BertTokenizer.from_pretrained(_UpperCAmelCase				,  *_UpperCAmelCase				,  **_UpperCAmelCase     )
        except:  # noqa: E722
             from .tokenization_bert_fast import BertTokenizerFast

             a	:     List[Any]						      =    BertTokenizerFast.from_pretrained(_UpperCAmelCase				,  *_UpperCAmelCase				,  **_UpperCAmelCase     )
        return cls.from_tokenizer(_UpperCAmelCase				,  **_UpperCAmelCase     )
   def _lowercase(     self				,  lowerCAmelCase_     )						->List[Any]:
        if self.do_lower_case:
             a	:     str						      =    case_fold_utfa(_UpperCAmelCase     )
        a	:     List[Any]						      =    self.tf_tokenizer.tokenize(_UpperCAmelCase     )
        return tokens.merge_dims(1				,  -1     )
   def _lowercase(     self				,  lowerCAmelCase_				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  lowerCAmelCase_=None				,  )						->Optional[Any]:
        if padding is None:
             a	:     Tuple						      =    self.padding
        if padding not in ("longest", "max_length"):
             raise ValueError('''Padding must be either \'longest\' or \'max_length\'!'''     )
        if max_length is not None and text_pair is not None:
             # Because we have to instantiate a Trimmer to do it properly
             raise ValueError('''max_length cannot be overridden at call time when truncating paired texts!'''     )
        if max_length is None:
             a	:     List[str]						      =    self.max_length
        if truncation is None:
             a	:     Optional[int]						      =    self.truncation
        if pad_to_multiple_of is None:
             a	:     int						      =    self.pad_to_multiple_of
        if return_token_type_ids is None:
             a	:     List[Any]						      =    self.return_token_type_ids
        if return_attention_mask is None:
             a	:     str						      =    self.return_attention_mask
        if not isinstance(_UpperCAmelCase				,  tf.Tensor     ):
             a	:     List[Any]						      =    tf.convert_to_tensor(_UpperCAmelCase     )
        if text_pair is not None and not isinstance(_UpperCAmelCase				,  tf.Tensor     ):
             a	:     Dict						      =    tf.convert_to_tensor(_UpperCAmelCase     )
        if text_pair is not None:
             if text.shape.rank > 1:
                  raise ValueError('''text argument should not be multidimensional when a text pair is supplied!'''     )
             if text_pair.shape.rank > 1:
                  raise ValueError('''text_pair should not be multidimensional!'''     )
        if text.shape.rank == 2:
             a      ,				a	:     Optional[Any]						      =    text[:, 0], text[:, 1]
        a	:     Optional[Any]						      =    self.unpaired_tokenize(_UpperCAmelCase     )
        if text_pair is None:  # Unpaired text
             if truncation:
                  a	:     Optional[Any]						      =    text[:, : max_length - 2]  # Allow room for special tokens
             a      ,				a	:     Any						      =    combine_segments(
                 (text,)				,  start_of_sequence_id=self.cls_token_id				,  end_of_segment_id=self.sep_token_id     )
        else:  # Paired text
             a	:     List[str]						      =    self.unpaired_tokenize(_UpperCAmelCase     )
             if truncation:
                  a      ,				a	:     int						      =    self.paired_trimmer.trim([text, text_pair]     )
             a      ,				a	:     Any						      =    combine_segments(
                 (text, text_pair)				,  start_of_sequence_id=self.cls_token_id				,  end_of_segment_id=self.sep_token_id     )
        if padding == "longest":
             a	:     Optional[int]						      =    input_ids.bounding_shape(axis=1     )
             if pad_to_multiple_of is not None:
                  # No ceiling division in tensorflow, so we negate floordiv instead
                  a	:     str						      =    pad_to_multiple_of * (-tf.math.floordiv(-pad_length				,  _UpperCAmelCase     ))
        else:
             a	:     Optional[Any]						      =    max_length

        a      ,				a	:     Dict						      =    pad_model_inputs(_UpperCAmelCase				,  max_seq_length=_UpperCAmelCase				,  pad_value=self.pad_token_id     )
        a	:     str						      =    {'''input_ids''': input_ids}
        if return_attention_mask:
             a	:     Tuple						      =    attention_mask
        if return_token_type_ids:
             a      ,				a	:     Dict						      =    pad_model_inputs(
                 _UpperCAmelCase				,  max_seq_length=_UpperCAmelCase				,  pad_value=self.pad_token_id     )
             a	:     List[Any]						      =    token_type_ids
        return output


   def _lowercase(     self     )						->Union[str, Any]:
        return {
            "vocab_list": self.vocab_list,
            "do_lower_case": self.do_lower_case,
            "cls_token_id": self.cls_token_id,
            "sep_token_id": self.sep_token_id,
            "pad_token_id": self.pad_token_id,
        }



from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase     =       logging.get_logger(__name__)

UpperCAmelCase     =       {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 __snake_case				=    "blenderbot"
 __snake_case				=    ["past_key_values"]
 __snake_case				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}



 def __init__(      self						, _UpperCAmelCase=80_08						, _UpperCAmelCase=1_28						, _UpperCAmelCase=2						, _UpperCAmelCase=1_02_40						, _UpperCAmelCase=32						, _UpperCAmelCase=24						, _UpperCAmelCase=1_02_40						, _UpperCAmelCase=32						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.0						, _UpperCAmelCase=True						, _UpperCAmelCase=True						, _UpperCAmelCase="gelu"						, _UpperCAmelCase=25_60						, _UpperCAmelCase=0.1						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.0						, _UpperCAmelCase=0.02						, _UpperCAmelCase=1						, _UpperCAmelCase=False						, _UpperCAmelCase=0						, _UpperCAmelCase=1						, _UpperCAmelCase=2						, _UpperCAmelCase=3						, _UpperCAmelCase=2						, **_UpperCAmelCase						, ):
       snake_case_			= vocab_size
       snake_case_			= max_position_embeddings
       snake_case_			= d_model
       snake_case_			= encoder_ffn_dim
       snake_case_			= encoder_layers
       snake_case_			= encoder_attention_heads
       snake_case_			= decoder_ffn_dim
       snake_case_			= decoder_layers
       snake_case_			= decoder_attention_heads
       snake_case_			= dropout
       snake_case_			= attention_dropout
       snake_case_			= activation_dropout
       snake_case_			= activation_function
       snake_case_			= init_std
       snake_case_			= encoder_layerdrop
       snake_case_			= decoder_layerdrop
       snake_case_			= use_cache
       snake_case_			= encoder_layers
       snake_case_			= scale_embedding  # scale factor will be sqrt(d_model) if True

       super().__init__(
           pad_token_id=_UpperCAmelCase						, bos_token_id=_UpperCAmelCase						, eos_token_id=_UpperCAmelCase						, is_encoder_decoder=_UpperCAmelCase						, decoder_start_token_id=_UpperCAmelCase						, encoder_no_repeat_ngram_size=_UpperCAmelCase						, forced_eos_token_id=_UpperCAmelCase						, **_UpperCAmelCase						, )







class lowerCAmelCase_		(					lowerCamelCase__			):

 '''simple docstring'''

 @property
 def 		UpperCamelCase__	(      self   ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]   )
             if self.use_past:
                   snake_case_			= {0: '''batch'''}
                   snake_case_			= {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
             else:
                   snake_case_			= {0: '''batch''', 1: '''decoder_sequence'''}
                   snake_case_			= {0: '''batch''', 1: '''decoder_sequence'''}
             if self.use_past:
                   self.fill_with_past_key_values_(_UpperCAmelCase						, direction='''inputs'''   )
       elif self.task == "causal-lm":
             snake_case_			= OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                 ]   )
             if self.use_past:
                   snake_case_			,       snake_case_			= self.num_layers
                   for i in range(_UpperCAmelCase   ):
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
       else:
             snake_case_			= OrderedDict(
                 [
                     ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
                     ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
                     ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
                 ]   )

       return common_inputs



 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def 		UpperCamelCase__	(      self   ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= super().outputs
       else:
             snake_case_			= super(_UpperCAmelCase						, self   ).outputs
             if self.use_past:
                   snake_case_			,       snake_case_			= self.num_layers
                   for i in range(_UpperCAmelCase   ):
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
                         snake_case_			= {0: '''batch''', 2: '''past_sequence + sequence'''}
       return common_outputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )
       # Generate decoder inputs
       snake_case_			= seq_length if not self.use_past else 1
       snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )
       snake_case_			= {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
       snake_case_			= dict(**_UpperCAmelCase						, **_UpperCAmelCase   )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
             else:
                   import torch
             snake_case_			,       snake_case_			= common_inputs['''input_ids'''].shape
             snake_case_			= common_inputs['''decoder_input_ids'''].shape[1]
             snake_case_			,       snake_case_			= self.num_attention_heads
             snake_case_			= (
                 batch,
                 num_encoder_attention_heads,
                 encoder_seq_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             snake_case_			= decoder_seq_length
             snake_case_			= (
                 batch,
                 num_decoder_attention_heads,
                 decoder_past_length,
                 self._config.hidden_size // num_decoder_attention_heads,
             )
             snake_case_			= torch.cat(
                 [common_inputs['''decoder_attention_mask'''], torch.ones(_UpperCAmelCase						, _UpperCAmelCase   )]						, dim=1   )
             snake_case_			= []
             snake_case_			,       snake_case_			= self.num_layers

             for _ in range(_UpperCAmelCase   ):
                   common_inputs["past_key_values"].append(
                       (
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                           torch.zeros(_UpperCAmelCase   ),
                       )   )
       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
           _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )

       if self.use_past:
             if not is_torch_available():
                   raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''   )
             else:
                   import torch
             snake_case_			,       snake_case_			= common_inputs['''input_ids'''].shape
             snake_case_			= seqlen
             snake_case_			,       snake_case_			= self.num_layers
             snake_case_			,       snake_case_			= self.num_attention_heads
             snake_case_			= (
                 batch,
                 num_encoder_attention_heads,
                 past_key_values_length,
                 self._config.hidden_size // num_encoder_attention_heads,
             )
             snake_case_			= common_inputs['''attention_mask'''].dtype
             snake_case_			= torch.cat(
                 [common_inputs['''attention_mask'''], torch.ones(_UpperCAmelCase						, _UpperCAmelCase						, dtype=_UpperCAmelCase   )]						, dim=1   )
             snake_case_			= [
                 (torch.zeros(_UpperCAmelCase   ), torch.zeros(_UpperCAmelCase   )) for _ in range(_UpperCAmelCase   )
             ]
       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       # Copied from OnnxConfig.generate_dummy_inputs
       # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       snake_case_			= compute_effective_axis_dimension(
           _UpperCAmelCase						, fixed_dimension=OnnxConfig.default_fixed_batch						, num_token_to_add=0   )

       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       snake_case_			= tokenizer.num_special_tokens_to_add(_UpperCAmelCase   )
       snake_case_			= compute_effective_axis_dimension(
           _UpperCAmelCase						, fixed_dimension=OnnxConfig.default_fixed_sequence						, num_token_to_add=_UpperCAmelCase   )

       # Generate dummy inputs according to compute batch and sequence
       snake_case_			= [''' '''.join([tokenizer.unk_token]   ) * seq_length] * batch_size
       snake_case_			= dict(tokenizer(_UpperCAmelCase						, return_tensors=_UpperCAmelCase   )   )
       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase = -1						, _UpperCAmelCase = -1						, _UpperCAmelCase = False						, _UpperCAmelCase = None						, ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                 _UpperCAmelCase						, batch_size=_UpperCAmelCase						, seq_length=_UpperCAmelCase						, is_pair=_UpperCAmelCase						, framework=_UpperCAmelCase   )

       elif self.task == "causal-lm":
             snake_case_			= self._generate_dummy_inputs_for_causal_lm(
                 _UpperCAmelCase						, batch_size=_UpperCAmelCase						, seq_length=_UpperCAmelCase						, is_pair=_UpperCAmelCase						, framework=_UpperCAmelCase   )
       else:
             snake_case_			= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                 _UpperCAmelCase						, batch_size=_UpperCAmelCase						, seq_length=_UpperCAmelCase						, is_pair=_UpperCAmelCase						, framework=_UpperCAmelCase   )

       return common_inputs



 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if self.task in ["default", "seq2seq-lm"]:
             snake_case_			= super()._flatten_past_key_values_(_UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )
       else:
             snake_case_			= super(_UpperCAmelCase						, self   )._flatten_past_key_values_(
                 _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase						, _UpperCAmelCase   )





 def 		UpperCamelCase__	(      self						, _UpperCAmelCase						, _UpperCAmelCase   ):
       if direction not in ["inputs", "outputs"]:
             raise ValueError(F'''direction must either be "inputs" or "outputs", but {direction} was given'''   )

       snake_case_			= '''past_key_values''' if direction == '''inputs''' else '''present'''
       snake_case_			,       snake_case_			= self.num_layers

       snake_case_			= '''past_encoder_sequence'''
       snake_case_			= '''past_decoder_sequence''' if direction == '''inputs''' else '''past_decoder_sequence + sequence'''

       for i in range(_UpperCAmelCase   ):
             snake_case_			= {0: '''batch''', 2: decoder_sequence}
             snake_case_			= {0: '''batch''', 2: decoder_sequence}
             snake_case_			= {0: '''batch''', 2: encoder_sequence}
             snake_case_			= {0: '''batch''', 2: encoder_sequence}