
'''simple docstring'''
import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


a_           =    logging.get_logger(__name__)






@dataclass
class  UpperCAmelCase_					:
 UpperCAmelCase_		     =    42
 UpperCAmelCase_		     =    42
 UpperCAmelCase_		     =    None
 UpperCAmelCase_		     =    None





 def 							snake_case__		(     self):
     return json.dumps(dataclasses.asdict(self)			,							indent=2) + "\n"






@dataclass(frozen=snake_case__       )
class  UpperCAmelCase_					:
 UpperCAmelCase_		     =    42
 UpperCAmelCase_		     =    None
 UpperCAmelCase_		     =    None
 UpperCAmelCase_		     =    None





 def 							snake_case__		(     self):
     return json.dumps(dataclasses.asdict(self)) + "\n"






class  UpperCAmelCase_					:





 def 							snake_case__		(     self			,							lowercase_):
     raise NotImplementedError()





 def 							snake_case__		(     self			,							lowercase_):
     raise NotImplementedError()





 def 							snake_case__		(     self			,							lowercase_):
     raise NotImplementedError()





 def 							snake_case__		(     self			,							lowercase_):
     raise NotImplementedError()





 def 							snake_case__		(     self):
     raise NotImplementedError()





 def 							snake_case__		(     self			,							lowercase_):
     if len(self.get_labels()) > 1:
         snake_case_			:     List[str]        =    self.get_labels()[int(example.label)]
     return example





 @classmethod
 def 							snake_case__		(     cls			,							lowercase_			,							lowercase_=None):
     with open(lowercase_			,							"r"			,							encoding="utf-8-sig") as f:
         return list(csv.reader(lowercase_			,							delimiter="\t"			,							quotechar=lowercase_))






class  UpperCAmelCase_					(						snake_case__       ):





 def __init__(     self			,							lowercase_=None			,							lowercase_=None			,							lowercase_="classification"			,							lowercase_=False):
     snake_case_			:     List[str]        =    [] if labels is None else labels
     snake_case_			:     Tuple        =    [] if examples is None else examples
     snake_case_			:     Dict        =    mode
     snake_case_			:     List[Any]        =    verbose





 def __len__(     self):
     return len(self.examples)





 def __getitem__(     self			,							lowercase_):
     if isinstance(lowercase_			,							lowercase_):
         return SingleSentenceClassificationProcessor(labels=self.labels			,							examples=self.examples[idx])
     return self.examples[idx]





 @classmethod
 def 							snake_case__		(     cls			,							lowercase_			,							lowercase_=""			,							lowercase_=0			,							lowercase_=1			,							lowercase_=None			,							lowercase_=False			,							**lowercase_):
     snake_case_			:     Union[str, Any]        =    cls(**lowercase_)
     processor.add_examples_from_csv(
         lowercase_			,							split_name=lowercase_			,							column_label=lowercase_			,							column_text=lowercase_			,							column_id=lowercase_			,							skip_first_row=lowercase_			,							overwrite_labels=lowercase_			,							overwrite_examples=lowercase_			,							)
     return processor





 @classmethod
 def 							snake_case__		(     cls			,							lowercase_			,							lowercase_=None			,							**lowercase_):
     snake_case_			:     Union[str, Any]        =    cls(**lowercase_)
     processor.add_examples(lowercase_			,							labels=lowercase_)
     return processor





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_=""			,							lowercase_=0			,							lowercase_=1			,							lowercase_=None			,							lowercase_=False			,							lowercase_=False			,							lowercase_=False			,							):
     snake_case_			:     Optional[Any]        =    self._read_tsv(lowercase_)
     if skip_first_row:
         snake_case_			:     Optional[int]        =    lines[1:]
     snake_case_			:     Optional[int]        =    []
     snake_case_			:     int        =    []
     snake_case_			:     Optional[Any]        =    []
     for i, line in enumerate(lowercase_):
         texts.append(line[column_text])
         labels.append(line[column_label])
         if column_id is not None:
             ids.append(line[column_id])
         else:
             snake_case_			:     Tuple        =    F'{split_name}-{i}' if split_name else str(lowercase_)
             ids.append(lowercase_)

     return self.add_examples(
         lowercase_			,							lowercase_			,							lowercase_			,							overwrite_labels=lowercase_			,							overwrite_examples=lowercase_)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_=None			,							lowercase_=None			,							lowercase_=False			,							lowercase_=False):
     if labels is not None and len(lowercase_) != len(lowercase_):
         raise ValueError(
             F'Text and labels have mismatched lengths {len(lowercase_)} and {len(lowercase_)}')
     if ids is not None and len(lowercase_) != len(lowercase_):
         raise ValueError(F'Text and ids have mismatched lengths {len(lowercase_)} and {len(lowercase_)}')
     if ids is None:
         snake_case_			:     Optional[Any]        =    [None] * len(lowercase_)
     if labels is None:
         snake_case_			:     Optional[Any]        =    [None] * len(lowercase_)
     snake_case_			:     List[Any]        =    []
     snake_case_			:     Optional[int]        =    set()
     for text_or_text_and_label, label, guid in zip(lowercase_			,							lowercase_			,							lowercase_):
         if isinstance(lowercase_			,							(tuple, list)) and label is None:
             snake_case_      ,			snake_case_			:     List[str]        =    text_or_text_and_label
         else:
             snake_case_			:     int        =    text_or_text_and_label
         added_labels.add(lowercase_)
         examples.append(InputExample(guid=lowercase_			,							text_a=lowercase_			,							text_b=lowercase_			,							label=lowercase_))

     # Update examples
     if overwrite_examples:
         snake_case_			:     List[str]        =    examples
     else:
         self.examples.extend(lowercase_)

     # Update labels
     if overwrite_labels:
         snake_case_			:     int        =    list(lowercase_)
     else:
         snake_case_			:     List[str]        =    list(set(self.labels).union(lowercase_))

     return self.examples





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_=None			,							lowercase_=False			,							lowercase_=0			,							lowercase_=True			,							lowercase_=None			,							):
     if max_length is None:
         snake_case_			:     int        =    tokenizer.max_len

     snake_case_			:     List[Any]        =    {label: i for i, label in enumerate(self.labels)}

     snake_case_			:     List[Any]        =    []
     for ex_index, example in enumerate(self.examples):
         if ex_index % 1_00_00 == 0:
             logger.info(F'Tokenizing example {ex_index}')

         snake_case_			:     int        =    tokenizer.encode(
             example.text_a			,							add_special_tokens=lowercase_			,							max_length=min(lowercase_			,							tokenizer.max_len)			,							)
         all_input_ids.append(lowercase_)

     snake_case_			:     Tuple        =    max(len(lowercase_) for input_ids in all_input_ids)

     snake_case_			:     Optional[int]        =    []
     for ex_index, (input_ids, example) in enumerate(zip(lowercase_			,							self.examples)):
         if ex_index % 1_00_00 == 0:
             logger.info(F'Writing example {ex_index}/{len(self.examples)}')
         # The mask has 1 for real tokens and 0 for padding tokens. Only real
         # tokens are attended to.
         snake_case_			:     Optional[int]        =    [1 if mask_padding_with_zero else 0] * len(lowercase_)

         # Zero-pad up to the sequence length.
         snake_case_			:     Tuple        =    batch_length - len(lowercase_)
         if pad_on_left:
             snake_case_			:     Optional[Any]        =    ([pad_token] * padding_length) + input_ids
             snake_case_			:     Optional[Any]        =    ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
         else:
             snake_case_			:     Union[str, Any]        =    input_ids + ([pad_token] * padding_length)
             snake_case_			:     Tuple        =    attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

         if len(lowercase_) != batch_length:
             raise ValueError(F'Error with input length {len(lowercase_)} vs {batch_length}')
         if len(lowercase_) != batch_length:
             raise ValueError(F'Error with input length {len(lowercase_)} vs {batch_length}')

         if self.mode == "classification":
             snake_case_			:     str        =    label_map[example.label]
         elif self.mode == "regression":
             snake_case_			:     Optional[int]        =    float(example.label)
         else:
             raise ValueError(self.mode)

         if ex_index < 5 and self.verbose:
             logger.info("*** Example ***")
             logger.info(F'guid: {example.guid}')
             logger.info(F'input_ids: {" ".join([str(lowercase_) for x in input_ids])}')
             logger.info(F'attention_mask: {" ".join([str(lowercase_) for x in attention_mask])}')
             logger.info(F'label: {example.label} (id = {label})')

         features.append(InputFeatures(input_ids=lowercase_			,							attention_mask=lowercase_			,							label=lowercase_))

     if return_tensors is None:
         return features
     elif return_tensors == "tf":
         if not is_tf_available():
             raise RuntimeError("return_tensors set to 'tf' but TensorFlow 2.0 can't be imported")
         import tensorflow as tf

         def gen():
             for ex in features:
                 yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

         snake_case_			:     List[Any]        =    tf.data.Dataset.from_generator(
             lowercase_			,							({"input_ids": tf.intaa, "attention_mask": tf.intaa}, tf.intaa)			,							({"input_ids": tf.TensorShape([None]), "attention_mask": tf.TensorShape([None])}, tf.TensorShape([]))			,							)
         return dataset
     elif return_tensors == "pt":
         if not is_torch_available():
             raise RuntimeError("return_tensors set to 'pt' but PyTorch can't be imported")
         import torch
         from torch.utils.data import TensorDataset

         snake_case_			:     List[str]        =    torch.tensor([f.input_ids for f in features]			,							dtype=torch.long)
         snake_case_			:     Any        =    torch.tensor([f.attention_mask for f in features]			,							dtype=torch.long)
         if self.mode == "classification":
             snake_case_			:     List[str]        =    torch.tensor([f.label for f in features]			,							dtype=torch.long)
         elif self.mode == "regression":
             snake_case_			:     Optional[int]        =    torch.tensor([f.label for f in features]			,							dtype=torch.float)

         snake_case_			:     Dict        =    TensorDataset(lowercase_			,							lowercase_			,							lowercase_)
         return dataset
     else:
         raise ValueError("return_tensors should be one of 'tf' or 'pt'")






'''simple docstring'''
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


a_           =    logging.get_logger(__name__)

a_           =    {
    "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  UpperCAmelCase_					(						snake_case__       ):
 UpperCAmelCase_		     =    """blenderbot"""
 UpperCAmelCase_		     =    ["""past_key_values"""]
 UpperCAmelCase_		     =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}





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

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






class  UpperCAmelCase_					(						snake_case__       ):





 @property
 def 							snake_case__		(     self):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     int        =    OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ])
         if self.use_past:
             snake_case_			:     Any        =    {0: "batch"}
             snake_case_			:     Optional[int]        =    {0: "batch", 1: "past_decoder_sequence + sequence"}
         else:
             snake_case_			:     int        =    {0: "batch", 1: "decoder_sequence"}
             snake_case_			:     int        =    {0: "batch", 1: "decoder_sequence"}
         if self.use_past:
             self.fill_with_past_key_values_(lowercase_			,							direction="inputs")
     elif self.task == "causal-lm":
         snake_case_			:     Tuple        =    OrderedDict(
             [
                 ("input_ids", {0: "batch", 1: "encoder_sequence"}),
                 ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
             ])
         if self.use_past:
             snake_case_      ,			snake_case_			:     Any        =    self.num_layers
             for i in range(lowercase_):
                 snake_case_			:     Dict        =    {0: "batch", 2: "past_sequence + sequence"}
                 snake_case_			:     Dict        =    {0: "batch", 2: "past_sequence + sequence"}
     else:
         snake_case_			:     str        =    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 							snake_case__		(     self):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     Optional[int]        =    super().outputs
     else:
         snake_case_			:     Dict        =    super(lowercase_			,							self).outputs
         if self.use_past:
             snake_case_      ,			snake_case_			:     Any        =    self.num_layers
             for i in range(lowercase_):
                 snake_case_			:     str        =    {0: "batch", 2: "past_sequence + sequence"}
                 snake_case_			:     int        =    {0: "batch", 2: "past_sequence + sequence"}
     return common_outputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     snake_case_			:     List[Any]        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     # Generate decoder inputs
     snake_case_			:     int        =    seq_length if not self.use_past else 1
     snake_case_			:     List[str]        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     snake_case_			:     str        =    {F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
     snake_case_			:     Any        =    dict(**lowercase_			,							**lowercase_)

     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_			:     int        =    common_inputs["input_ids"].shape
         snake_case_			:     str        =    common_inputs["decoder_input_ids"].shape[1]
         snake_case_      ,			snake_case_			:     Tuple        =    self.num_attention_heads
         snake_case_			:     str        =    (
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case_			:     Dict        =    decoder_seq_length
         snake_case_			:     List[Any]        =    (
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )
         snake_case_			:     Optional[Any]        =    torch.cat(
             [common_inputs["decoder_attention_mask"], torch.ones(lowercase_			,							lowercase_)]			,							dim=1)
         snake_case_			:     Tuple        =    []
         snake_case_      ,			snake_case_			:     Tuple        =    self.num_layers

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





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = None			,							):
     snake_case_			:     str        =    self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
         lowercase_			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)

     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_			:     Optional[Any]        =    common_inputs["input_ids"].shape
         snake_case_			:     List[str]        =    seqlen
         snake_case_      ,			snake_case_			:     Optional[int]        =    self.num_layers
         snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.num_attention_heads
         snake_case_			:     Optional[int]        =    (
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         snake_case_			:     Dict        =    common_inputs["attention_mask"].dtype
         snake_case_			:     Optional[Any]        =    torch.cat(
             [common_inputs["attention_mask"], torch.ones(lowercase_			,							lowercase_			,							dtype=lowercase_)]			,							dim=1)
         snake_case_			:     Tuple        =    [
             (torch.zeros(lowercase_), torch.zeros(lowercase_)) for _ in range(lowercase_)
         ]
     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = -1			,							lowercase_ = -1			,							lowercase_ = False			,							lowercase_ = 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_			:     Any        =    compute_effective_axis_dimension(
         lowercase_			,							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_			:     Tuple        =    tokenizer.num_special_tokens_to_add(lowercase_)
     snake_case_			:     Tuple        =    compute_effective_axis_dimension(
         lowercase_			,							fixed_dimension=OnnxConfig.default_fixed_sequence			,							num_token_to_add=lowercase_)

     # Generate dummy inputs according to compute batch and sequence
     snake_case_			:     Optional[int]        =    [" ".join([tokenizer.unk_token]) * seq_length] * batch_size
     snake_case_			:     Tuple        =    dict(tokenizer(lowercase_			,							return_tensors=lowercase_))
     return common_inputs





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

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

     return common_inputs





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_			,							lowercase_			,							lowercase_):
     if self.task in ["default", "seq2seq-lm"]:
         snake_case_			:     int        =    super()._flatten_past_key_values_(lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)
     else:
         snake_case_			:     List[Any]        =    super(lowercase_			,							self)._flatten_past_key_values_(
             lowercase_			,							lowercase_			,							lowercase_			,							lowercase_)





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

     snake_case_			:     Union[str, Any]        =    "past_key_values" if direction == "inputs" else "present"
     snake_case_      ,			snake_case_			:     Union[str, Any]        =    self.num_layers

     snake_case_			:     List[Any]        =    "past_encoder_sequence"
     snake_case_			:     str        =    "past_decoder_sequence" if direction == "inputs" else "past_decoder_sequence + sequence"

     for i in range(lowercase_):
         snake_case_			:     Union[str, Any]        =    {0: "batch", 2: decoder_sequence}
         snake_case_			:     str        =    {0: "batch", 2: decoder_sequence}
         snake_case_			:     Any        =    {0: "batch", 2: encoder_sequence}
         snake_case_			:     Dict        =    {0: "batch", 2: encoder_sequence}




