



'''simple docstring'''





import json
import os
from typing import Dict, List, Optional, Tuple

import regex as re

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__a          =		logging.get_logger(__name__)


__a          =		{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__a          =		{
    "vocab_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"
    },
    "merges_file": {
        "facebook/blenderbot_small-90M": "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"
    },
    "tokenizer_config_file": {
        "facebook/blenderbot_small-90M": (
            "https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"
        )
    },
}

__a          =		{"facebook/blenderbot_small-90M": 512}





def 						__snake_case(				_lowerCAmelCase		)		-> Optional[Any]:
      snake_case__  :    Any    						=      set()
      snake_case__  :    Tuple    						=      word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)		)
            snake_case__  :    str    						=      char

      snake_case__  :    Dict    						=      set(_lowerCAmelCase		)
      return pairs







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		VOCAB_FILES_NAMES
    lowercase					=		PRETRAINED_VOCAB_FILES_MAP
    lowercase					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase					=		["input_ids", "attention_mask"]




    def __init__(    self					:     str ,     snake_case_					:     Optional[int] ,     snake_case_					:     Optional[int] ,     snake_case_					:     int="__start__" ,     snake_case_					:     Optional[int]="__end__" ,     snake_case_					:     str="__unk__" ,     snake_case_					:     List[Any]="__null__" ,     **snake_case_					:     int ,     ):
          super().__init__(unk_token=snake_case_ ,     bos_token=snake_case_ ,     eos_token=snake_case_ ,     pad_token=snake_case_ ,     **snake_case_						)

          with open(snake_case_ ,     encoding="""utf-8"""						) as vocab_handle:
                snake_case__  :    Dict    						=      json.load(snake_case_						)
          snake_case__  :    Optional[Any]    						=      {v: k for k, v in self.encoder.items()}
          with open(snake_case_ ,     encoding="""utf-8"""						) as merges_handle:
                snake_case__  :    str    						=      merges_handle.read().split("""\n"""						)[1:-1]
          snake_case__  :    Tuple    						=      [tuple(merge.split()						) for merge in merges]
          snake_case__  :    Any    						=      dict(zip(snake_case_ ,     range(len(snake_case_						)						)						)						)
          snake_case__  :    Dict    						=      {}




    @property
    def lowerCamelCase       (    self					:     str						):
          return len(self.encoder						)




    def lowerCamelCase       (    self					:     List[Any]						):
          return dict(self.encoder ,     **self.added_tokens_encoder						)




    def lowerCamelCase       (    self					:     Dict ,     snake_case_					:     str						):
          if token in self.cache:
                return self.cache[token]
          snake_case__  :    Tuple    						=      re.sub("""([.,!?()])""" ,     r""" \1""" ,     snake_case_						)
          snake_case__  :    Optional[int]    						=      re.sub("""(')""" ,     r""" \1 """ ,     snake_case_						)
          snake_case__  :    int    						=      re.sub(r"""\s{2,}""" ,     """ """ ,     snake_case_						)
          if "\n" in token:
                snake_case__  :    int    						=      token.replace("""\n""" ,     """ __newln__"""						)

          snake_case__  :    Dict    						=      token.split(""" """						)
          snake_case__  :    Union[str, Any]    						=      []
          for token in tokens:
                if not len(snake_case_						):
                      continue

                snake_case__  :    List[str]    						=      token.lower()
                snake_case__  :    Dict    						=      tuple(snake_case_						)
                snake_case__  :    Optional[Any]    						=      tuple(list(word[:-1]						) + [word[-1] + """</w>"""]						)
                snake_case__  :    int    						=      get_pairs(snake_case_						)

                if not pairs:
                      words.append(snake_case_						)
                      continue

                while True:
                      snake_case__  :    Tuple    						=      min(snake_case_ ,     key=lambda snake_case_						: self.bpe_ranks.get(snake_case_ ,     float("""inf"""						)						)						)
                      if bigram not in self.bpe_ranks:
                            break
                      snake_case__						,				snake_case__  :    List[Any]    						=      bigram
                      snake_case__  :    str    						=      []
                      snake_case__  :    Tuple    						=      0

                      while i < len(snake_case_						):
                            try:
                                  snake_case__  :    Union[str, Any]    						=      word.index(snake_case_ ,     snake_case_						)
                                  new_word.extend(word[i:j]						)
                                  snake_case__  :    List[Any]    						=      j
                            except ValueError:
                                  new_word.extend(word[i:]						)
                                  break

                            if word[i] == first and i < len(snake_case_						) - 1 and word[i + 1] == second:
                                  new_word.append(first + second						)
                                  i += 2
                            else:
                                  new_word.append(word[i]						)
                                  i += 1
                      snake_case__  :    int    						=      tuple(snake_case_						)
                      snake_case__  :    Optional[Any]    						=      new_word
                      if len(snake_case_						) == 1:
                            break
                      else:
                            snake_case__  :    str    						=      get_pairs(snake_case_						)
                snake_case__  :    Optional[int]    						=      """@@ """.join(snake_case_						)
                snake_case__  :    Any    						=      word[:-4]

                snake_case__  :    str    						=      word
                words.append(snake_case_						)
          return " ".join(snake_case_						)




    def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     str						):
          snake_case__  :    Tuple    						=      []

          snake_case__  :    str    						=      re.findall(r"""\S+\n?""" ,     snake_case_						)

          for token in words:
                split_tokens.extend(list(self.bpe(snake_case_						).split(""" """						)						)						)
          return split_tokens




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     str						):
          snake_case__  :    Dict    						=      token.lower()
          return self.encoder.get(snake_case_ ,     self.encoder.get(self.unk_token						)						)




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     int						):
          return self.decoder.get(snake_case_ ,     self.unk_token						)




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[str]						):
          snake_case__  :    List[str]    						=      """ """.join(snake_case_						).replace("""@@ """ ,     """"""						).strip()
          return out_string




    def lowerCamelCase       (    self					:     Optional[Any] ,     snake_case_					:     str ,     snake_case_					:     Optional[str] = None						):
          if not os.path.isdir(snake_case_						):
                logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
                return
          snake_case__  :    Union[str, Any]    						=      os.path.join(
              snake_case_ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)
          snake_case__  :    List[Any]    						=      os.path.join(
              snake_case_ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]						)

          with open(snake_case_ ,     """w""" ,     encoding="""utf-8"""						) as f:
                f.write(json.dumps(self.encoder ,     indent=2 ,     sort_keys=snake_case_ ,     ensure_ascii=snake_case_						) + """\n"""						)

          snake_case__  :    Optional[Any]    						=      0
          with open(snake_case_ ,     """w""" ,     encoding="""utf-8"""						) as writer:
                writer.write("""#version: 0.2\n"""						)
                for bpe_tokens, token_index in sorted(self.bpe_ranks.items() ,     key=lambda snake_case_						: kv[1]						):
                      if index != token_index:
                            logger.warning(
                                f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                """ Please check that the tokenizer is not corrupted!"""						)
                            snake_case__  :    int    						=      token_index
                      writer.write(""" """.join(snake_case_						) + """\n"""						)
                      index += 1

          return vocab_file, merge_file







"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional

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


_snake_case     :		List[Any]       						= logging.get_logger(__name__)

_snake_case     :		int       						= {
    'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''marian'''
					UpperCamelCase      =							['''past_key_values''']
					UpperCamelCase      =							{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(					self			:int     ,       __UpperCamelCase			:Any=5_81_01     ,       __UpperCamelCase			:int=None     ,       __UpperCamelCase			:Union[str, Any]=10_24     ,       __UpperCamelCase			:Union[str, Any]=12     ,       __UpperCamelCase			:str=40_96     ,       __UpperCamelCase			:int=16     ,       __UpperCamelCase			:int=12     ,       __UpperCamelCase			:Optional[Any]=40_96     ,       __UpperCamelCase			:Optional[Any]=16     ,       __UpperCamelCase			:Dict=0.0     ,       __UpperCamelCase			:Dict=0.0     ,       __UpperCamelCase			:str=True     ,       __UpperCamelCase			:Optional[int]=True     ,       __UpperCamelCase			:Any="gelu"     ,       __UpperCamelCase			:Any=10_24     ,       __UpperCamelCase			:List[Any]=0.1     ,       __UpperCamelCase			:Optional[Any]=0.0     ,       __UpperCamelCase			:Union[str, Any]=0.0     ,       __UpperCamelCase			:Tuple=0.02     ,       __UpperCamelCase			:List[str]=5_81_00     ,       __UpperCamelCase			:str=False     ,       __UpperCamelCase			:Optional[int]=5_81_00     ,       __UpperCamelCase			:List[Any]=0     ,       __UpperCamelCase			:List[str]=0     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  vocab_size
												A		=  decoder_vocab_size or vocab_size
												A		=  max_position_embeddings
												A		=  d_model
												A		=  encoder_ffn_dim
												A		=  encoder_layers
												A		=  encoder_attention_heads
												A		=  decoder_ffn_dim
												A		=  decoder_layers
												A		=  decoder_attention_heads
												A		=  dropout
												A		=  attention_dropout
												A		=  activation_dropout
												A		=  activation_function
												A		=  init_std
												A		=  encoder_layerdrop
												A		=  decoder_layerdrop
												A		=  use_cache
												A		=  encoder_layers
												A		=  scale_embedding  # scale factor will be sqrt(d_model) if True
												A		=  share_encoder_decoder_embeddings
												super().__init__(
												    pad_token_id=__UpperCamelCase     ,       eos_token_id=__UpperCamelCase     ,       is_encoder_decoder=__UpperCamelCase     ,       decoder_start_token_id=__UpperCamelCase     ,       forced_eos_token_id=__UpperCamelCase     ,       **__UpperCamelCase     ,       )







class 					_UpperCAmelCase   (	lowercase_ ):


					@property
					# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
					def 	lowerCamelCase       (					self			:List[str]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)

																			if self.use_past:
																										A		=  {0: "batch"}
																										A		=  {0: "batch", 1: "past_decoder_sequence + sequence"}
																			else:
																										A		=  {0: "batch", 1: "decoder_sequence"}
																										A		=  {0: "batch", 1: "decoder_sequence"}

																			if self.use_past:
																										self.fill_with_past_key_values_(__UpperCamelCase     ,       direction="inputs"		)
												elif self.task == "causal-lm":
																			# TODO: figure this case out.
																			A		=  OrderedDict(
																			    [
																			        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																			        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																			    ]		)
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(__UpperCamelCase		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												else:
																			A		=  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 	lowerCamelCase       (					self			:List[str]		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super().outputs
												else:
																			A		=  super(__UpperCamelCase     ,       self		).outputs
																			if self.use_past:
																										A,	A		=  self.num_layers
																										for i in range(__UpperCamelCase		):
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
																																	A		=  {0: "batch", 2: "past_sequence + sequence"}
												return common_outputs


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)

												# Generate decoder inputs
												A		=  seq_length if not self.use_past else 1
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)
												A		=  {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
												A		=  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
																			A,	A		=  common_inputs["input_ids"].shape
																			A		=  common_inputs["decoder_input_ids"].shape[1]
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    encoder_seq_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)
																			A		=  decoder_seq_length + 3
																			A		=  (
																			    batch,
																			    num_decoder_attention_heads,
																			    decoder_past_length,
																			    self._config.hidden_size // num_decoder_attention_heads,
																			)

																			A		=  torch.cat(
																			    [common_inputs["decoder_attention_mask"], torch.ones(__UpperCamelCase     ,       __UpperCamelCase		)]     ,       dim=1		)

																			A		=  []
																			# If the number of encoder and decoder layers are present in the model configuration, both are considered
																			A,	A		=  self.num_layers
																			A		=  min(__UpperCamelCase     ,       __UpperCamelCase		)
																			A		=  max(__UpperCamelCase     ,       __UpperCamelCase		) - min_num_layers
																			A		=  "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

																			for _ in range(__UpperCamelCase		):
																										common_inputs["past_key_values"].append(
																										    (
																										        torch.zeros(__UpperCamelCase		),
																										        torch.zeros(__UpperCamelCase		),
																										        torch.zeros(__UpperCamelCase		),
																										        torch.zeros(__UpperCamelCase		),
																										    )		)
																			# TODO: test this.
																			A		=  encoder_shape if remaining_side_name == "encoder" else decoder_shape
																			for _ in range(__UpperCamelCase     ,       __UpperCamelCase		):
																										common_inputs["past_key_values"].append((torch.zeros(__UpperCamelCase		), torch.zeros(__UpperCamelCase		))		)
												return common_inputs


					def 	lowerCamelCase       (					self			:Optional[int]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												A		=  self._generate_dummy_inputs_for_encoder_and_decoder(
												    __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
																			A,	A		=  common_inputs["input_ids"].shape
																			# Not using the same length for past_key_values
																			A		=  seqlen + 2
																			A,	A		=  self.num_layers
																			A,	A		=  self.num_attention_heads
																			A		=  (
																			    batch,
																			    num_encoder_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // num_encoder_attention_heads,
																			)

																			A		=  common_inputs["attention_mask"].dtype
																			A		=  torch.cat(
																			    [common_inputs["attention_mask"], torch.ones(__UpperCamelCase     ,       __UpperCamelCase     ,       dtype=__UpperCamelCase		)]     ,       dim=1		)
																			A		=  [
																			    (torch.zeros(__UpperCamelCase		), torch.zeros(__UpperCamelCase		)) for _ in range(__UpperCamelCase		)
																			]
												return common_inputs


					def 	lowerCamelCase       (					self			:Tuple     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = 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
												A		=  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
												A		=  tokenizer.num_special_tokens_to_add(__UpperCamelCase		)
												A		=  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
												A		=  [" ".join([tokenizer.unk_token]		) * seq_length] * batch_size
												A		=  dict(tokenizer(__UpperCamelCase     ,       return_tensors=__UpperCamelCase		)		)
												return common_inputs


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:PreTrainedTokenizer     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:int = -1     ,       __UpperCamelCase			:bool = False     ,       __UpperCamelCase			:Optional[TensorType] = None     ,       ):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																			    __UpperCamelCase     ,       batch_size=__UpperCamelCase     ,       seq_length=__UpperCamelCase     ,       is_pair=__UpperCamelCase     ,       framework=__UpperCamelCase		)

												else:
																			A		=  self._generate_dummy_inputs_for_causal_lm(
																			    __UpperCamelCase     ,       batch_size=__UpperCamelCase     ,       seq_length=__UpperCamelCase     ,       is_pair=__UpperCamelCase     ,       framework=__UpperCamelCase		)

												return common_inputs


					def 	lowerCamelCase       (					self			:List[Any]     ,       __UpperCamelCase			:Tuple     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:str		):
												if self.task in ["default", "seq2seq-lm"]:
																			A		=  super()._flatten_past_key_values_(__UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)
												else:
																			A		=  super(__UpperCamelCase     ,       self		)._flatten_past_key_values_(
																			    __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase		)


					@property
					def 	lowerCamelCase       (					self			:List[str]		):
												return 1e-4


