"""simple docstring"""
import argparse
import os

from . import (
    ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
    DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AlbertConfig,
    BartConfig,
    BertConfig,
    CamembertConfig,
    CTRLConfig,
    DistilBertConfig,
    DPRConfig,
    ElectraConfig,
    FlaubertConfig,
    GPTaConfig,
    LayoutLMConfig,
    LxmertConfig,
    OpenAIGPTConfig,
    RobertaConfig,
    TaConfig,
    TFAlbertForPreTraining,
    TFBartForConditionalGeneration,
    TFBartForSequenceClassification,
    TFBertForPreTraining,
    TFBertForQuestionAnswering,
    TFBertForSequenceClassification,
    TFCamembertForMaskedLM,
    TFCTRLLMHeadModel,
    TFDistilBertForMaskedLM,
    TFDistilBertForQuestionAnswering,
    TFDPRContextEncoder,
    TFDPRQuestionEncoder,
    TFDPRReader,
    TFElectraForPreTraining,
    TFFlaubertWithLMHeadModel,
    TFGPTaLMHeadModel,
    TFLayoutLMForMaskedLM,
    TFLxmertForPreTraining,
    TFLxmertVisualFeatureEncoder,
    TFOpenAIGPTLMHeadModel,
    TFRobertaForCausalLM,
    TFRobertaForMaskedLM,
    TFRobertaForSequenceClassification,
    TFTaForConditionalGeneration,
    TFTransfoXLLMHeadModel,
    TFWavaVecaModel,
    TFXLMRobertaForMaskedLM,
    TFXLMWithLMHeadModel,
    TFXLNetLMHeadModel,
    TransfoXLConfig,
    WavaVecaConfig,
    WavaVecaModel,
    XLMConfig,
    XLMRobertaConfig,
    XLNetConfig,
    is_torch_available,
    load_pytorch_checkpoint_in_tfa_model,
)
from .utils import CONFIG_NAME, WEIGHTS_NAME, cached_file, logging


if is_torch_available():
 import numpy as np
 import torch

 from . import (
     AlbertForPreTraining,
     BartForConditionalGeneration,
     BertForPreTraining,
     BertForQuestionAnswering,
     BertForSequenceClassification,
     CamembertForMaskedLM,
     CTRLLMHeadModel,
     DistilBertForMaskedLM,
     DistilBertForQuestionAnswering,
     DPRContextEncoder,
     DPRQuestionEncoder,
     DPRReader,
     ElectraForPreTraining,
     FlaubertWithLMHeadModel,
     GPTaLMHeadModel,
     LayoutLMForMaskedLM,
     LxmertForPreTraining,
     LxmertVisualFeatureEncoder,
     OpenAIGPTLMHeadModel,
     RobertaForMaskedLM,
     RobertaForSequenceClassification,
     TaForConditionalGeneration,
     TransfoXLLMHeadModel,
     XLMRobertaForMaskedLM,
     XLMWithLMHeadModel,
     XLNetLMHeadModel,
 )


logging.set_verbosity_info()

lowercase__ 	=     {
    'bart': (
        BartConfig,
        TFBartForConditionalGeneration,
        TFBartForSequenceClassification,
        BartForConditionalGeneration,
        BART_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'bert': (
        BertConfig,
        TFBertForPreTraining,
        BertForPreTraining,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-uncased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-large-cased-whole-word-masking-finetuned-squad': (
        BertConfig,
        TFBertForQuestionAnswering,
        BertForQuestionAnswering,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'bert-base-cased-finetuned-mrpc': (
        BertConfig,
        TFBertForSequenceClassification,
        BertForSequenceClassification,
        BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'dpr': (
        DPRConfig,
        TFDPRQuestionEncoder,
        TFDPRContextEncoder,
        TFDPRReader,
        DPRQuestionEncoder,
        DPRContextEncoder,
        DPRReader,
        DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
        DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'gpt2': (
        GPTaConfig,
        TFGPTaLMHeadModel,
        GPTaLMHeadModel,
        GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlnet': (
        XLNetConfig,
        TFXLNetLMHeadModel,
        XLNetLMHeadModel,
        XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm': (
        XLMConfig,
        TFXLMWithLMHeadModel,
        XLMWithLMHeadModel,
        XLM_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'xlm-roberta': (
        XLMRobertaConfig,
        TFXLMRobertaForMaskedLM,
        XLMRobertaForMaskedLM,
        XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'transfo-xl': (
        TransfoXLConfig,
        TFTransfoXLLMHeadModel,
        TransfoXLLMHeadModel,
        TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'openai-gpt': (
        OpenAIGPTConfig,
        TFOpenAIGPTLMHeadModel,
        OpenAIGPTLMHeadModel,
        OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'roberta': (
        RobertaConfig,
        TFRobertaForCausalLM,
        TFRobertaForMaskedLM,
        RobertaForMaskedLM,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'layoutlm': (
        LayoutLMConfig,
        TFLayoutLMForMaskedLM,
        LayoutLMForMaskedLM,
        LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
    ),
    'roberta-large-mnli': (
        RobertaConfig,
        TFRobertaForSequenceClassification,
        RobertaForSequenceClassification,
        ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'camembert': (
        CamembertConfig,
        TFCamembertForMaskedLM,
        CamembertForMaskedLM,
        CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'flaubert': (
        FlaubertConfig,
        TFFlaubertWithLMHeadModel,
        FlaubertWithLMHeadModel,
        FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert': (
        DistilBertConfig,
        TFDistilBertForMaskedLM,
        DistilBertForMaskedLM,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'distilbert-base-distilled-squad': (
        DistilBertConfig,
        TFDistilBertForQuestionAnswering,
        DistilBertForQuestionAnswering,
        DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert': (
        LxmertConfig,
        TFLxmertForPreTraining,
        LxmertForPreTraining,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'lxmert-visual-feature-encoder': (
        LxmertConfig,
        TFLxmertVisualFeatureEncoder,
        LxmertVisualFeatureEncoder,
        LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'ctrl': (
        CTRLConfig,
        TFCTRLLMHeadModel,
        CTRLLMHeadModel,
        CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'albert': (
        AlbertConfig,
        TFAlbertForPreTraining,
        AlbertForPreTraining,
        ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    't5': (
        TaConfig,
        TFTaForConditionalGeneration,
        TaForConditionalGeneration,
        T5_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'electra': (
        ElectraConfig,
        TFElectraForPreTraining,
        ElectraForPreTraining,
        ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
    'wav2vec2': (
        WavaVecaConfig,
        TFWavaVecaModel,
        WavaVecaModel,
        WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    ),
}







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=True       )     ->Tuple:
  if model_type not in MODEL_CLASSES:
    raise ValueError(F'Unrecognized model type, should be one of {list(MODEL_CLASSES.keys()       )}.'       )

  a__:					List[str]      							=     MODEL_CLASSES[model_type]

  # Initialise TF model
  if config_file in aws_config_map:
    a__:					Optional[int]      							=     cached_file(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
  a__:					Any      							=     config_class.from_json_file(_SCREAMING_SNAKE_CASE       )
  a__:					Optional[int]      							=     True
  a__:					str      							=     True
  print(F'Building TensorFlow model from configuration: {config}'       )
  a__:					Optional[int]      							=     model_class(_SCREAMING_SNAKE_CASE       )

  # Load weights from tf checkpoint
  if pytorch_checkpoint_path in aws_config_map.keys():
    a__:					Union[str, Any]      							=     cached_file(
        _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
  # Load PyTorch checkpoint in tf2 model:
  a__:					Tuple      							=     load_pytorch_checkpoint_in_tfa_model(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  if compare_with_pt_model:
    a__:					Tuple      							=     tf_model(tf_model.dummy_inputs		,					training=_SCREAMING_SNAKE_CASE       )  # build the network

    a__:					Tuple      							=     torch.load(_SCREAMING_SNAKE_CASE		,					map_location='cpu'       )
    a__:					str      							=     pt_model_class.from_pretrained(
        pretrained_model_name_or_path=_SCREAMING_SNAKE_CASE		,					config=_SCREAMING_SNAKE_CASE		,					state_dict=_SCREAMING_SNAKE_CASE       )

    with torch.no_grad():
      a__:					str      							=     pt_model(**pt_model.dummy_inputs       )

    a__:					List[str]      							=     pto[0].numpy()
    a__:					int      							=     tfo[0].numpy()
    a__:					List[Any]      							=     np.amax(np.abs(np_pt - np_tf       )       )
    print(F'Max absolute difference between models outputs {diff}'       )
    assert diff <= 2e-2, F'Error, model absolute difference is >2e-2: {diff}'

  # Save pytorch-model
  print(F'Save TensorFlow model to {tf_dump_path}'       )
  tf_model.save_weights(_SCREAMING_SNAKE_CASE		,					save_format='h5'       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=None		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=False		,					_SCREAMING_SNAKE_CASE=False		,					)     ->Union[str, Any]:
  if args_model_type is None:
    a__:					List[str]      							=     list(MODEL_CLASSES.keys()       )
  else:
    a__:					Optional[Any]      							=     [args_model_type]

  for j, model_type in enumerate(_SCREAMING_SNAKE_CASE		,					start=1       ):
    print('=' * 100       )
    print(F' Converting model type {j}/{len(_SCREAMING_SNAKE_CASE       )}: {model_type}'       )
    print('=' * 100       )
    if model_type not in MODEL_CLASSES:
      raise ValueError(F'Unrecognized model type {model_type}, should be one of {list(MODEL_CLASSES.keys()       )}.'       )

    a__:					int      							=     MODEL_CLASSES[model_type]

    if model_shortcut_names_or_path is None:
      a__:					Optional[Any]      							=     list(aws_model_maps.keys()       )
    if config_shortcut_names_or_path is None:
      a__:					Optional[Any]      							=     model_shortcut_names_or_path

    for i, (model_shortcut_name, config_shortcut_name) in enumerate(
        zip(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					start=1       ):
      print('-' * 100       )
      if "-squad" in model_shortcut_name or "-mrpc" in model_shortcut_name or "-mnli" in model_shortcut_name:
        if not only_convert_finetuned_models:
          print(F'    Skipping finetuned checkpoint {model_shortcut_name}'       )
          continue
        a__:					Union[str, Any]      							=     model_shortcut_name
      elif only_convert_finetuned_models:
        print(F'    Skipping not finetuned checkpoint {model_shortcut_name}'       )
        continue
      print(
          F'    Converting checkpoint {i}/{len(_SCREAMING_SNAKE_CASE       )}: {model_shortcut_name} - model_type {model_type}'       )
      print('-' * 100       )

      if config_shortcut_name in aws_config_map:
        a__:					int      							=     cached_file(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
      else:
        a__:					List[Any]      							=     config_shortcut_name

      if model_shortcut_name in aws_model_maps:
        a__:					int      							=     cached_file(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					force_download=not use_cached_models       )
      else:
        a__:					List[Any]      							=     model_shortcut_name

      if os.path.isfile(_SCREAMING_SNAKE_CASE       ):
        a__:					Union[str, Any]      							=     'converted_model'

      convert_pt_checkpoint_to_tf(
          model_type=_SCREAMING_SNAKE_CASE		,					pytorch_checkpoint_path=_SCREAMING_SNAKE_CASE		,					config_file=_SCREAMING_SNAKE_CASE		,					tf_dump_path=os.path.join(_SCREAMING_SNAKE_CASE		,					model_shortcut_name + '-tf_model.h5'       )		,					compare_with_pt_model=_SCREAMING_SNAKE_CASE		,					)
      if remove_cached_files:
        os.remove(_SCREAMING_SNAKE_CASE       )
        os.remove(_SCREAMING_SNAKE_CASE       )


if __name__ == "__main__":
 lowercase__ 	=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--tf_dump_path', default=None, type=str, required=True, help='Path to the output Tensorflow dump file.'
 )
 parser.add_argument(
     '--model_type',
     default=None,
     type=str,
     help=(
         f"Model type selected in the list of {list(MODEL_CLASSES.keys())}. If not given, will download and "
         'convert all the models from AWS.'
     ),
 )
 parser.add_argument(
     '--pytorch_checkpoint_path',
     default=None,
     type=str,
     help=(
         'Path to the PyTorch checkpoint path or shortcut name to download from AWS. '
         'If not given, will download and convert all the checkpoints from AWS.'
     ),
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     help=(
         'The config json file corresponding to the pre-trained model. \n'
         'This specifies the model architecture. If not given and '
         '--pytorch_checkpoint_path is not given or is a shortcut name '
         'use the configuration associated to the shortcut name on the AWS'
     ),
 )
 parser.add_argument(
     '--compare_with_pt_model', action='store_true', help='Compare Tensorflow and PyTorch model predictions.'
 )
 parser.add_argument(
     '--use_cached_models',
     action='store_true',
     help='Use cached models if possible instead of updating to latest checkpoint versions.',
 )
 parser.add_argument(
     '--remove_cached_files',
     action='store_true',
     help='Remove pytorch models after conversion (save memory when converting in batches).',
 )
 parser.add_argument('--only_convert_finetuned_models', action='store_true', help='Only convert finetuned models.')
 lowercase__ 	=     parser.parse_args()

 # if args.pytorch_checkpoint_path is not None:
 #     convert_pt_checkpoint_to_tf(args.model_type.lower(),
 #                                 args.pytorch_checkpoint_path,
 #                                 args.config_file if args.config_file is not None else args.pytorch_checkpoint_path,
 #                                 args.tf_dump_path,
 #                                 compare_with_pt_model=args.compare_with_pt_model,
 #                                 use_cached_models=args.use_cached_models)
 # else:
 convert_all_pt_checkpoints_to_tf(
     args.model_type.lower() if args.model_type is not None else None,
     args.tf_dump_path,
     model_shortcut_names_or_path=[args.pytorch_checkpoint_path]
     if args.pytorch_checkpoint_path is not None
     else None,
     config_shortcut_names_or_path=[args.config_file] if args.config_file is not None else None,
     compare_with_pt_model=args.compare_with_pt_model,
     use_cached_models=args.use_cached_models,
     remove_cached_files=args.remove_cached_files,
     only_convert_finetuned_models=args.only_convert_finetuned_models,
 )



"""simple docstring"""
import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer


lowercase__ 	=     logging.get_logger(__name__)

lowercase__ 	=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__ 	=     {
    'vocab_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-ctx_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-ctx_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowercase__ 	=     {
    'vocab_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-question_encoder-single-nq-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-question_encoder-multiset-base': (
            'https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}
lowercase__ 	=     {
    'vocab_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'facebook/dpr-reader-single-nq-base': (
            'https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'
        ),
        'facebook/dpr-reader-multiset-base': (
            'https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__ 	=     {
    'facebook/dpr-ctx_encoder-single-nq-base': 512,
    'facebook/dpr-ctx_encoder-multiset-base': 512,
}
lowercase__ 	=     {
    'facebook/dpr-question_encoder-single-nq-base': 512,
    'facebook/dpr-question_encoder-multiset-base': 512,
}
lowercase__ 	=     {
    'facebook/dpr-reader-single-nq-base': 512,
    'facebook/dpr-reader-multiset-base': 512,
}


lowercase__ 	=     {
    'facebook/dpr-ctx_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-ctx_encoder-multiset-base': {'do_lower_case': True},
}
lowercase__ 	=     {
    'facebook/dpr-question_encoder-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-question_encoder-multiset-base': {'do_lower_case': True},
}
lowercase__ 	=     {
    'facebook/dpr-reader-single-nq-base': {'do_lower_case': True},
    'facebook/dpr-reader-multiset-base': {'do_lower_case': True},
}




class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION







class        __snake_case	(					__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION







lowercase__ 	=     collections.namedtuple(
    'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)

lowercase__ 	=     collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])


lowercase__ 	=     r'\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    ```\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n    ```\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `\'tf\'`: Return TensorFlow `tf.constant` objects.\n                - `\'pt\'`: Return PyTorch `torch.Tensor` objects.\n                - `\'np\'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer\'s default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Returns:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    '




@add_start_docstrings(__lowerCAmelCase    )
class        __snake_case	:



      def __call__(						self   ,      lowercase   ,      lowercase = None   ,      lowercase = None   ,      lowercase = False   ,      lowercase = False   ,      lowercase = None   ,      lowercase = None   ,      lowercase = None   ,      **lowercase   ,      )     ->      BatchEncoding:





        '''simple docstring'''





        if titles is None and texts is None:
          return super().__call__(
              lowercase   ,      padding=lowercase   ,      truncation=lowercase   ,      max_length=lowercase   ,      return_tensors=lowercase   ,      return_attention_mask=lowercase   ,      **lowercase   ,      )
        elif titles is None or texts is None:
          a__:					str      							=     titles if texts is None else texts
          return super().__call__(
              lowercase   ,      lowercase   ,      padding=lowercase   ,      truncation=lowercase   ,      max_length=lowercase   ,      return_tensors=lowercase   ,      return_attention_mask=lowercase   ,      **lowercase   ,      )
        a__:					Tuple      							=     titles if not isinstance(lowercase   ,      lowercase) else [titles]
        a__:					Optional[int]      							=     texts if not isinstance(lowercase   ,      lowercase) else [texts]
        a__:					Dict      							=     len(lowercase)
        a__:					Dict      							=     questions if not isinstance(lowercase   ,      lowercase) else [questions] * n_passages
        if len(lowercase) != len(lowercase):
          raise ValueError(
              f'There should be as many titles than texts but got {len(lowercase)} titles and {len(lowercase)} texts.')
        a__:					List[str]      							=     super().__call__(lowercase   ,      lowercase   ,      padding=lowercase   ,      truncation=lowercase)['input_ids']
        a__:					List[Any]      							=     super().__call__(lowercase   ,      add_special_tokens=lowercase   ,      padding=lowercase   ,      truncation=lowercase)['input_ids']
        a__:					Optional[Any]      							=     {
            'input_ids': [
                (encoded_question_and_title + encoded_text)[:max_length]
                if max_length is not None and truncation
                else encoded_question_and_title + encoded_text
                for encoded_question_and_title, encoded_text in zip(lowercase   ,      lowercase)
            ]
        }
        if return_attention_mask is not False:
          a__:					Dict      							=     []
          for input_ids in encoded_inputs["input_ids"]:
            attention_mask.append([int(input_id != self.pad_token_id) for input_id in input_ids])
          a__:					int      							=     attention_mask
        return self.pad(lowercase   ,      padding=lowercase   ,      max_length=lowercase   ,      return_tensors=lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase = 16   ,      lowercase = 64   ,      lowercase = 4   ,      )     ->      List[DPRSpanPrediction]:





        '''simple docstring'''





        a__:					Dict      							=     reader_input['input_ids']
        a__							,   a__							,   a__:					Tuple      							=     reader_output[:3]
        a__:					Tuple      							=     len(lowercase)
        a__:					Optional[Any]      							=     sorted(range(lowercase)   ,      reverse=lowercase   ,      key=relevance_logits.__getitem__)
        a__:					List[DPRReaderOutput]      							=     []
        for doc_id in sorted_docs:
          a__:					Tuple      							=     list(input_ids[doc_id])
          # assuming question & title information is at the beginning of the sequence
          a__:					Dict      							=     sequence_ids.index(self.sep_token_id   ,      2) + 1  # second sep id
          if sequence_ids[-1] == self.pad_token_id:
            a__:					Any      							=     sequence_ids.index(self.pad_token_id)
          else:
            a__:					Optional[Any]      							=     len(lowercase)

          a__:					Optional[int]      							=     self._get_best_spans(
              start_logits=start_logits[doc_id][passage_offset:sequence_len]   ,      end_logits=end_logits[doc_id][passage_offset:sequence_len]   ,      max_answer_length=lowercase   ,      top_spans=lowercase   ,      )
          for start_index, end_index in best_spans:
            start_index += passage_offset
            end_index += passage_offset
            nbest_spans_predictions.append(
                DPRSpanPrediction(
                    span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index]   ,      relevance_score=relevance_logits[doc_id]   ,      doc_id=lowercase   ,      start_index=lowercase   ,      end_index=lowercase   ,      text=self.decode(sequence_ids[start_index : end_index + 1])   ,      ))
          if len(lowercase) >= num_spans:
            break
        return nbest_spans_predictions[:num_spans]



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase   ,      lowercase   ,      )     ->      List[DPRSpanPrediction]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     []
        for start_index, start_score in enumerate(lowercase):
          for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]):
            scores.append(((start_index, start_index + answer_length), start_score + end_score))
        a__:					str      							=     sorted(lowercase   ,      key=lambda lowercase: x[1]   ,      reverse=lowercase)
        a__:					Optional[Any]      							=     []
        for (start_index, end_index), score in scores:
          if start_index > end_index:
            raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]')
          a__:					str      							=     end_index - start_index + 1
          if length > max_answer_length:
            raise ValueError(f'Span is too long: {length} > {max_answer_length}')
          if any(
              start_index <= prev_start_index <= prev_end_index <= end_index
              or prev_start_index <= start_index <= end_index <= prev_end_index
              for (prev_start_index, prev_end_index) in chosen_span_intervals):
            continue
          chosen_span_intervals.append((start_index, end_index))

          if len(lowercase) == top_spans:
            break
        return chosen_span_intervals







@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase    ):
      a__				=		VOCAB_FILES_NAMES
      a__				=		READER_PRETRAINED_VOCAB_FILES_MAP
      a__				=		READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      a__				=		READER_PRETRAINED_INIT_CONFIGURATION
      a__				=		["""input_ids""", """attention_mask"""]


