

"""simple docstring"""






import argparse
import logging
import os
import time
import timeit

import datasets
import numpy as np
import pycuda.autoinit  # noqa: F401
import pycuda.driver as cuda
import tensorrt as trt
import torch
from absl import logging as absl_logging
from accelerate import Accelerator
from datasets import load_dataset, load_metric
from torch.utils.data import DataLoader
from utils_qa import postprocess_qa_predictions

import transformers
from transformers import AutoTokenizer, EvalPrediction, default_data_collator, set_seed
from transformers.trainer_pt_utils import nested_concat, nested_truncate


__lowerCamelCase										=		trt.Logger(trt.Logger.WARNING)
__lowerCamelCase										=		absl_logging.get_absl_logger()
absl_logger.setLevel(logging.WARNING)

__lowerCamelCase										=		logging.getLogger(__name__)

__lowerCamelCase										=		argparse.ArgumentParser()

# Required parameters
parser.add_argument(
    "--onnx_model_path",
    default=None,
    type=str,
    required=True,
    help="Path to ONNX model: ",
)

parser.add_argument(
    "--output_dir",
    default=None,
    type=str,
    required=True,
    help="The output directory where the model checkpoints and predictions will be written.",
)

# Other parameters

parser.add_argument(
    "--tokenizer_name",
    default="",
    type=str,
    required=True,
    help="Pretrained tokenizer name or path if not the same as model_name",
)

parser.add_argument(
    "--version_2_with_negative",
    action="store_true",
    help="If true, the SQuAD examples contain some that do not have an answer.",
)
parser.add_argument(
    "--null_score_diff_threshold",
    type=float,
    default=0.0,
    help="If null_score - best_non_null is greater than the threshold predict null.",
)

parser.add_argument(
    "--max_seq_length",
    default=3_84,
    type=int,
    help=(
        "The maximum total input sequence length after WordPiece tokenization. Sequences "
        "longer than this will be truncated, and sequences shorter than this will be padded."
    ),
)
parser.add_argument(
    "--doc_stride",
    default=1_28,
    type=int,
    help="When splitting up a long document into chunks, how much stride to take between chunks.",
)

parser.add_argument("--per_device_eval_batch_size", default=8, type=int, help="Batch size per GPU/CPU for evaluation.")

parser.add_argument(
    "--n_best_size",
    default=20,
    type=int,
    help="The total number of n-best predictions to generate in the nbest_predictions.json output file.",
)
parser.add_argument(
    "--max_answer_length",
    default=30,
    type=int,
    help=(
        "The maximum length of an answer that can be generated. This is needed because the start "
        "and end predictions are not conditioned on one another."
    ),
)

parser.add_argument("--seed", type=int, default=42, help="random seed for initialization")

parser.add_argument(
    "--dataset_name",
    type=str,
    default=None,
    required=True,
    help="The name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--dataset_config_name",
    type=str,
    default=None,
    help="The configuration name of the dataset to use (via the datasets library).",
)
parser.add_argument(
    "--preprocessing_num_workers", type=int, default=4, help="A csv or a json file containing the training data."
)
parser.add_argument("--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets")
parser.add_argument(
    "--fp16",
    action="store_true",
    help="Whether to use 16-bit (mixed) precision instead of 32-bit",
)
parser.add_argument(
    "--int8",
    action="store_true",
    help="Whether to use INT8",
)

__lowerCamelCase										=		parser.parse_args()

if args.tokenizer_name:
       __lowerCamelCase										=		AutoTokenizer.from_pretrained(args.tokenizer_name, use_fast=True)
else:
       raise ValueError(
           "You are instantiating a new tokenizer from scratch. This is not supported by this script."
           "You can do it from another script, save it, and load it from here, using --tokenizer_name."
       )

logger.info("Training/evaluation parameters %s", args)

__lowerCamelCase										=		args.per_device_eval_batch_size

__lowerCamelCase										=		(args.eval_batch_size, args.max_seq_length)

# TRT Engine properties
__lowerCamelCase										=		True

__lowerCamelCase										=		"temp_engine/bert-fp32.engine"
if args.fpaa:
       __lowerCamelCase										=		"temp_engine/bert-fp16.engine"
if args.inta:
       __lowerCamelCase										=		"temp_engine/bert-int8.engine"

# import ONNX file
if not os.path.exists("temp_engine"):
       os.makedirs("temp_engine")

__lowerCamelCase										=		1 << (int)(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)
with trt.Builder(TRT_LOGGER) as builder, builder.create_network(EXPLICIT_BATCH) as network, trt.OnnxParser(
    network, TRT_LOGGER
) as parser:
       with open(args.onnx_model_path, "rb") as model:
              if not parser.parse(model.read()):
                     for error in range(parser.num_errors):
                            print(parser.get_error(error))

                            # Query input names and shapes from parsed TensorRT network
       __lowerCamelCase										=		[network.get_input(i) for i in range(network.num_inputs)]
       __lowerCamelCase										=		[_input.name for _input in network_inputs]  # ex: ["actual_input1"]

       with builder.create_builder_config() as config:
              __lowerCamelCase										=		1 << 50
              if STRICT_TYPES:
                     config.set_flag(trt.BuilderFlag.STRICT_TYPES)
              if args.fpaa:
                     config.set_flag(trt.BuilderFlag.FPaa)
              if args.inta:
                     config.set_flag(trt.BuilderFlag.INTa)
              __lowerCamelCase										=		builder.create_optimization_profile()
              config.add_optimization_profile(profile)
              for i in range(len(input_names)):
                     profile.set_shape(input_names[i], INPUT_SHAPE, INPUT_SHAPE, INPUT_SHAPE)
              __lowerCamelCase										=		builder.build_engine(network, config)

              # serialize_engine and store in file (can be directly loaded and deserialized):
              with open(engine_name, "wb") as f:
                     f.write(engine.serialize())





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						) ->      Tuple:
 __magic_name__     =      np.asarray(inputs['''input_ids''']      ,   dtype=np.intaa						)
 __magic_name__     =      np.asarray(inputs['''attention_mask''']      ,   dtype=np.intaa						)
 __magic_name__     =      np.asarray(inputs['''token_type_ids''']      ,   dtype=np.intaa						)

 # Copy inputs
 cuda.memcpy_htod_async(d_inputs[0]      ,   input_ids.ravel()      ,   __UpperCamelCase						)
 cuda.memcpy_htod_async(d_inputs[1]      ,   attention_mask.ravel()      ,   __UpperCamelCase						)
 cuda.memcpy_htod_async(d_inputs[2]      ,   token_type_ids.ravel()      ,   __UpperCamelCase						)
 # start time
 __magic_name__     =      time.time()
 # Run inference
 context.execute_async(
     bindings=[int(__UpperCamelCase						) for d_inp in d_inputs] + [int(__UpperCamelCase						), int(__UpperCamelCase						)]      ,   stream_handle=stream.handle						)
 # Transfer predictions back from GPU
 cuda.memcpy_dtoh_async(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						)
 cuda.memcpy_dtoh_async(__UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase						)
 # Synchronize the stream and take time
 stream.synchronize()
 # end time
 __magic_name__     =      time.time()
 __magic_name__     =      end_time - start_time
 __magic_name__     =      (h_outputa, h_outputa)
 # print(outputs)
 return outputs, infer_time


# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
__lowerCamelCase										=		Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
    datefmt="%m/%d/%Y %H:%M:%S",
    level=logging.INFO,
)

# Setup logging, we only want one process per machine to log things on the screen.
# accelerator.is_local_main_process is only True for one process per machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR)
if accelerator.is_local_main_process:
       datasets.utils.logging.set_verbosity_warning()
       transformers.utils.logging.set_verbosity_info()
else:
       datasets.utils.logging.set_verbosity_error()
       transformers.utils.logging.set_verbosity_error()

# If passed along, set the training seed now.
if args.seed is not None:
       set_seed(args.seed)

# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
if args.dataset_name is not None:
       # Downloading and loading a dataset from the hub.
       __lowerCamelCase										=		load_dataset(args.dataset_name, args.dataset_config_name)
else:
       raise ValueError("Evaluation requires a dataset name")
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.

# Preprocessing the datasets.
# Preprocessing is slighlty different for training and evaluation.

__lowerCamelCase										=		raw_datasets["validation"].column_names

__lowerCamelCase										=		"question" if "question" in column_names else column_names[0]
__lowerCamelCase										=		"context" if "context" in column_names else column_names[1]
__lowerCamelCase										=		"answers" if "answers" in column_names else column_names[2]

# Padding side determines if we do (question|context) or (context|question).
__lowerCamelCase										=		tokenizer.padding_side == "right"

if args.max_seq_length > tokenizer.model_max_length:
       logger.warning(
           f"""The max_seq_length passed ({args.max_seq_length}) is larger than the maximum length for the"""
           f"""model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."""
       )

__lowerCamelCase										=		min(args.max_seq_length, tokenizer.model_max_length)





def 	lowercase      (   __UpperCamelCase						) ->      Tuple:
 # Some of the questions have lots of whitespace on the left, which is not useful and will make the
 # truncation of the context fail (the tokenized question will take a lots of space). So we remove that
 # left whitespace
 __magic_name__     =      [q.lstrip() for q in examples[question_column_name]]

 # Tokenize our examples with truncation and maybe padding, but keep the overflows using a stride. This results
 # in one example possible giving several features when a context is long, each of those features having a
 # context that overlaps a bit the context of the previous feature.
 __magic_name__     =      tokenizer(
     examples[question_column_name if pad_on_right else context_column_name]      ,   examples[context_column_name if pad_on_right else question_column_name]      ,   truncation='''only_second''' if pad_on_right else '''only_first'''      ,   max_length=__UpperCamelCase      ,   stride=args.doc_stride      ,   return_overflowing_tokens=__UpperCamelCase      ,   return_offsets_mapping=__UpperCamelCase      ,   padding='''max_length'''      ,   )

 # Since one example might give us several features if it has a long context, we need a map from a feature to
 # its corresponding example. This key gives us just that.
 __magic_name__     =      tokenized_examples.pop('''overflow_to_sample_mapping'''						)

 # For evaluation, we will need to convert our predictions to substrings of the context, so we keep the
 # corresponding example_id and we will store the offset mappings.
 __magic_name__     =      []

 for i in range(len(tokenized_examples['''input_ids''']						)						):
  # Grab the sequence corresponding to that example (to know what is the context and what is the question).
  __magic_name__     =      tokenized_examples.sequence_ids(__UpperCamelCase						)
  __magic_name__     =      1 if pad_on_right else 0

  # One example can give several spans, this is the index of the example containing this span of text.
  __magic_name__     =      sample_mapping[i]
  tokenized_examples["example_id"].append(examples['''id'''][sample_index]						)

  # Set to None the offset_mapping that are not part of the context so it's easy to determine if a token
  # position is part of the context or not.
  __magic_name__     =      [
      (o if sequence_ids[k] == context_index else None)
      for k, o in enumerate(tokenized_examples['''offset_mapping'''][i]						)
  ]

 return tokenized_examples


__lowerCamelCase										=		raw_datasets["validation"]
# Validation Feature Creation
__lowerCamelCase										=		eval_examples.map(
    prepare_validation_features,
    batched=True,
    num_proc=args.preprocessing_num_workers,
    remove_columns=column_names,
    load_from_cache_file=not args.overwrite_cache,
    desc="Running tokenizer on validation dataset",
)

__lowerCamelCase										=		default_data_collator

__lowerCamelCase										=		eval_dataset.remove_columns(["example_id", "offset_mapping"])
__lowerCamelCase										=		DataLoader(
    eval_dataset_for_model, collate_fn=data_collator, batch_size=args.per_device_eval_batch_size
)





def 	lowercase      (   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase      ,   __UpperCamelCase="eval"						) ->      List[str]:
 # Post-processing: we match the start logits and end logits to answers in the original context.
 __magic_name__     =      postprocess_qa_predictions(
     examples=__UpperCamelCase      ,   features=__UpperCamelCase      ,   predictions=__UpperCamelCase      ,   version_2_with_negative=args.version_2_with_negative      ,   n_best_size=args.n_best_size      ,   max_answer_length=args.max_answer_length      ,   null_score_diff_threshold=args.null_score_diff_threshold      ,   output_dir=args.output_dir      ,   prefix=__UpperCamelCase      ,   )
 # Format the result to the format the metric expects.
 if args.version_2_with_negative:
  __magic_name__     =      [
      {'''id''': k, '''prediction_text''': v, '''no_answer_probability''': 0.0} for k, v in predictions.items()
  ]
 else:
  __magic_name__     =      [{'''id''': k, '''prediction_text''': v} for k, v in predictions.items()]

 __magic_name__     =      [{'''id''': ex['''id'''], '''answers''': ex[answer_column_name]} for ex in examples]
 return EvalPrediction(predictions=__UpperCamelCase      ,   label_ids=__UpperCamelCase						)


__lowerCamelCase										=		load_metric("squad_v2" if args.version_2_with_negative else "squad")

# Evaluation!
logger.info("Loading ONNX model %s for evaluation", args.onnx_model_path)
with open(engine_name, "rb") as f, trt.Runtime(TRT_LOGGER) as runtime, runtime.deserialize_cuda_engine(
    f.read()
) as engine, engine.create_execution_context() as context:
       # setup for TRT inferrence
       for i in range(len(input_names)):
              context.set_binding_shape(i, INPUT_SHAPE)
       assert context.all_binding_shapes_specified





       def 	lowercase      (   __UpperCamelCase						) ->      Optional[Any]:
        return trt.volume(engine.get_binding_shape(__UpperCamelCase						)						) * engine.get_binding_dtype(__UpperCamelCase						).itemsize

       # Allocate device memory for inputs and outputs.
       __lowerCamelCase										=		[cuda.mem_alloc(binding_nbytes(binding)) for binding in engine if engine.binding_is_input(binding)]

       # Allocate output buffer
       __lowerCamelCase										=		cuda.pagelocked_empty(tuple(context.get_binding_shape(3)), dtype=np.floataa)
       __lowerCamelCase										=		cuda.pagelocked_empty(tuple(context.get_binding_shape(4)), dtype=np.floataa)
       __lowerCamelCase										=		cuda.mem_alloc(h_outputa.nbytes)
       __lowerCamelCase										=		cuda.mem_alloc(h_outputa.nbytes)

       # Create a stream in which to copy inputs/outputs and run inference.
       __lowerCamelCase										=		cuda.Stream()

       # Evaluation
       logger.info("***** Running Evaluation *****")
       logger.info(f"""  Num examples = {len(eval_dataset)}""")
       logger.info(f"""  Batch size = {args.per_device_eval_batch_size}""")

       __lowerCamelCase										=		0.0
       __lowerCamelCase										=		0
       __lowerCamelCase										=		timeit.default_timer()

       __lowerCamelCase										=		None
       for step, batch in enumerate(eval_dataloader):
              __lowerCamelCase						,    __lowerCamelCase										=		model_infer(batch, context, d_inputs, h_outputa, h_outputa, d_outputa, d_outputa, stream)
              total_time += infer_time
              niter += 1

              __lowerCamelCase						,    __lowerCamelCase										=		outputs
              __lowerCamelCase										=		torch.tensor(start_logits)
              __lowerCamelCase										=		torch.tensor(end_logits)

              # necessary to pad predictions and labels for being gathered
              __lowerCamelCase										=		accelerator.pad_across_processes(start_logits, dim=1, pad_index=-1_00)
              __lowerCamelCase										=		accelerator.pad_across_processes(end_logits, dim=1, pad_index=-1_00)

              __lowerCamelCase										=		(accelerator.gather(start_logits).cpu().numpy(), accelerator.gather(end_logits).cpu().numpy())
              __lowerCamelCase										=		logits if all_preds is None else nested_concat(all_preds, logits, padding_index=-1_00)

       if all_preds is not None:
              __lowerCamelCase										=		nested_truncate(all_preds, len(eval_dataset))

       __lowerCamelCase										=		timeit.default_timer() - start_time
       logger.info("  Evaluation done in total %f secs (%f sec per example)", evalTime, evalTime / len(eval_dataset))
       # Inference time from TRT
       logger.info("Average Inference Time = {:.3f} ms".format(total_time * 10_00 / niter))
       logger.info("Total Inference Time =  {:.3f} ms".format(total_time * 10_00))
       logger.info("Total Number of Inference =  %d", niter)

__lowerCamelCase										=		post_processing_function(eval_examples, eval_dataset, all_preds)
__lowerCamelCase										=		metric.compute(predictions=prediction.predictions, references=prediction.label_ids)
logger.info(f"""Evaluation metrics: {eval_metric}""")




"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


__lowerCamelCase										=		logging.get_logger(__name__)

__lowerCamelCase										=		{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

__lowerCamelCase										=		{
    "vocab_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/vocab.txt",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/vocab.txt",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt"
        ),
        "bert-base-multilingual-cased": "https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt",
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt"
        ),
        "bert-base-german-dbmdz-cased": "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt",
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "bert-base-uncased": "https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json",
        "bert-large-uncased": "https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json",
        "bert-base-cased": "https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json",
        "bert-large-cased": "https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json",
        "bert-base-multilingual-uncased": (
            "https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json"
        ),
        "bert-base-multilingual-cased": (
            "https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-chinese": "https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json",
        "bert-base-german-cased": "https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json",
        "bert-large-uncased-whole-word-masking": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking": (
            "https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json"
        ),
        "bert-large-uncased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-large-cased-whole-word-masking-finetuned-squad": (
            "https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json"
        ),
        "bert-base-cased-finetuned-mrpc": (
            "https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-cased": (
            "https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json"
        ),
        "bert-base-german-dbmdz-uncased": (
            "https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-cased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json"
        ),
        "TurkuNLP/bert-base-finnish-uncased-v1": (
            "https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json"
        ),
        "wietsedv/bert-base-dutch-cased": (
            "https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json"
        ),
    },
}

__lowerCamelCase										=		{
    "bert-base-uncased": 5_12,
    "bert-large-uncased": 5_12,
    "bert-base-cased": 5_12,
    "bert-large-cased": 5_12,
    "bert-base-multilingual-uncased": 5_12,
    "bert-base-multilingual-cased": 5_12,
    "bert-base-chinese": 5_12,
    "bert-base-german-cased": 5_12,
    "bert-large-uncased-whole-word-masking": 5_12,
    "bert-large-cased-whole-word-masking": 5_12,
    "bert-large-uncased-whole-word-masking-finetuned-squad": 5_12,
    "bert-large-cased-whole-word-masking-finetuned-squad": 5_12,
    "bert-base-cased-finetuned-mrpc": 5_12,
    "bert-base-german-dbmdz-cased": 5_12,
    "bert-base-german-dbmdz-uncased": 5_12,
    "TurkuNLP/bert-base-finnish-cased-v1": 5_12,
    "TurkuNLP/bert-base-finnish-uncased-v1": 5_12,
    "wietsedv/bert-base-dutch-cased": 5_12,
}

__lowerCamelCase										=		{
    "bert-base-uncased": {"do_lower_case": True},
    "bert-large-uncased": {"do_lower_case": True},
    "bert-base-cased": {"do_lower_case": False},
    "bert-large-cased": {"do_lower_case": False},
    "bert-base-multilingual-uncased": {"do_lower_case": True},
    "bert-base-multilingual-cased": {"do_lower_case": False},
    "bert-base-chinese": {"do_lower_case": False},
    "bert-base-german-cased": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking": {"do_lower_case": False},
    "bert-large-uncased-whole-word-masking-finetuned-squad": {"do_lower_case": True},
    "bert-large-cased-whole-word-masking-finetuned-squad": {"do_lower_case": False},
    "bert-base-cased-finetuned-mrpc": {"do_lower_case": False},
    "bert-base-german-dbmdz-cased": {"do_lower_case": False},
    "bert-base-german-dbmdz-uncased": {"do_lower_case": True},
    "TurkuNLP/bert-base-finnish-cased-v1": {"do_lower_case": False},
    "TurkuNLP/bert-base-finnish-uncased-v1": {"do_lower_case": True},
    "wietsedv/bert-base-dutch-cased": {"do_lower_case": False},
}
class        _lowercase					(     __UpperCAmelCase     ):
     _lowerCamelCase     =       VOCAB_FILES_NAMES
     _lowerCamelCase     =       PRETRAINED_VOCAB_FILES_MAP
     _lowerCamelCase     =       PRETRAINED_INIT_CONFIGURATION
     _lowerCamelCase     =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     _lowerCamelCase     =       BertTokenizer







     def __init__(      self ,						UpperCamelCase_=None ,						UpperCamelCase_=None ,						UpperCamelCase_=True ,						UpperCamelCase_="[UNK]" ,						UpperCamelCase_="[SEP]" ,						UpperCamelCase_="[PAD]" ,						UpperCamelCase_="[CLS]" ,						UpperCamelCase_="[MASK]" ,						UpperCamelCase_=True ,						UpperCamelCase_=None ,						**UpperCamelCase_ ,						):
      super().__init__(
          UpperCamelCase_ ,						tokenizer_file=UpperCamelCase_ ,						do_lower_case=UpperCamelCase_ ,						unk_token=UpperCamelCase_ ,						sep_token=UpperCamelCase_ ,						pad_token=UpperCamelCase_ ,						cls_token=UpperCamelCase_ ,						mask_token=UpperCamelCase_ ,						tokenize_chinese_chars=UpperCamelCase_ ,						strip_accents=UpperCamelCase_ ,						**UpperCamelCase_ ,						)

      __magic_name__     =      json.loads(self.backend_tokenizer.normalizer.__getstate__()       )
      if (
          normalizer_state.get('''lowercase''' ,						UpperCamelCase_       ) != do_lower_case
          or normalizer_state.get('''strip_accents''' ,						UpperCamelCase_       ) != strip_accents
          or normalizer_state.get('''handle_chinese_chars''' ,						UpperCamelCase_       ) != tokenize_chinese_chars
      ):
       __magic_name__     =      getattr(UpperCamelCase_ ,						normalizer_state.pop('''type'''       )       )
       __magic_name__     =      do_lower_case
       __magic_name__     =      strip_accents
       __magic_name__     =      tokenize_chinese_chars
       __magic_name__     =      normalizer_class(**UpperCamelCase_       )

      __magic_name__     =      do_lower_case







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_=None       ):
      __magic_name__     =      [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
       output += token_ids_a + [self.sep_token_id]

      return output







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None       ):
      __magic_name__     =      [self.sep_token_id]
      __magic_name__     =      [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       ) * [0] + len(token_ids_a + sep       ) * [1]







     def 	lowerCAmelCase__    (      self ,						UpperCamelCase_ ,						UpperCamelCase_ = None       ):
      __magic_name__     =      self._tokenizer.model.save(UpperCamelCase_ ,						name=UpperCamelCase_       )
      return tuple(UpperCamelCase_       )

