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

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

_lowercase		:				str	       =		{
    "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		:				List[str]	       =		{
    "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		:				List[Any]	       =		{
    "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		:				Dict	       =		{
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
_lowercase		:				Tuple	       =		{
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
_lowercase		:				int	       =		{
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


_lowercase		:				Any	       =		{
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase		:				List[str]	       =		{
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
_lowercase		:				Any	       =		{
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = VOCAB_FILES_NAMES
			UpperCamelCase__       = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__       = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__       = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = VOCAB_FILES_NAMES
			UpperCamelCase__       = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__       = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__       = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION




_lowercase		:				Dict	       =		collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

_lowercase		:				List[Any]	       =		collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


_lowercase		:				Union[str, Any]	       =		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(_UpperCAmelCase)
class 					__magic_name__		:







			def __call__(			self       :						List[Any]       ,    lowercase_       :						List[Any]       ,    lowercase_       :						Optional[str] = None       ,    lowercase_       :						Optional[str] = None       ,    lowercase_       :						Union[bool, str] = False       ,    lowercase_       :						Union[bool, str] = False       ,    lowercase_       :						Optional[int] = None       ,    lowercase_       :						Optional[Union[str, TensorType]] = None       ,    lowercase_       :						Optional[bool] = None       ,    **lowercase_       :						int       ,    ):
										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:
																	lowercase_      :    Tuple       		= 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_       ,    )
										lowercase_      :    Optional[int]       		= titles if not isinstance(lowercase_       ,    lowercase_					) else [titles]
										lowercase_      :    List[str]       		= texts if not isinstance(lowercase_       ,    lowercase_					) else [texts]
										lowercase_      :    str       		= len(lowercase_					)
										lowercase_      :    int       		= 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.'''					)
										lowercase_      :    Tuple       		= super().__call__(lowercase_       ,    lowercase_       ,    padding=lowercase_       ,    truncation=lowercase_					)["""input_ids"""]
										lowercase_      :    int       		= super().__call__(lowercase_       ,    add_special_tokens=lowercase_       ,    padding=lowercase_       ,    truncation=lowercase_					)["""input_ids"""]
										lowercase_      :    str       		= {
										    """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:
																	lowercase_      :    Optional[Any]       		= []
																	for input_ids in encoded_inputs["input_ids"]:
																								attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
																	lowercase_      :    Any       		= attention_mask
										return self.pad(lowercase_       ,    padding=lowercase_       ,    max_length=lowercase_       ,    return_tensors=lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						BatchEncoding       ,    lowercase_       :						DPRReaderOutput       ,    lowercase_       :						int = 16       ,    lowercase_       :						int = 64       ,    lowercase_       :						int = 4       ,    ):
										lowercase_      :    str       		= reader_input["""input_ids"""]
										lowercase_		,		lowercase_		,		lowercase_      :    Tuple       		= reader_output[:3]
										lowercase_      :    Optional[Any]       		= len(lowercase_					)
										lowercase_      :    Optional[Any]       		= sorted(range(lowercase_					)       ,    reverse=lowercase_       ,    key=relevance_logits.__getitem__					)
										lowercase_      :    List[DPRReaderOutput]       		= []
										for doc_id in sorted_docs:
																	lowercase_      :    Optional[int]       		= list(input_ids[doc_id]					)
																	# assuming question & title information is at the beginning of the sequence
																	lowercase_      :    int       		= sequence_ids.index(self.sep_token_id       ,    2					) + 1  # second sep id
																	if sequence_ids[-1] == self.pad_token_id:
																								lowercase_      :    Any       		= sequence_ids.index(self.pad_token_id					)
																	else:
																								lowercase_      :    Optional[int]       		= len(lowercase_					)

																	lowercase_      :    Any       		= 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 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						List[int]       ,    lowercase_       :						List[int]       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    ):
										lowercase_      :    int       		= []
										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)					)
										lowercase_      :    Dict       		= sorted(lowercase_       ,    key=lambda lowercase_					: x[1]       ,    reverse=lowercase_					)
										lowercase_      :    Any       		= []
										for (start_index, end_index), score in scores:
																	if start_index > end_index:
																								raise ValueError(f'''Wrong span indices: [{start_index}:{end_index}]'''					)
																	lowercase_      :    Tuple       		= 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(_UpperCAmelCase)
class 					__magic_name__		(					_UpperCAmelCase,  _UpperCAmelCase):
			UpperCamelCase__       = VOCAB_FILES_NAMES
			UpperCamelCase__       = READER_PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__       = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__       = READER_PRETRAINED_INIT_CONFIGURATION
			UpperCamelCase__       = ['''input_ids''', '''attention_mask''']






'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase		:				Optional[Any]	       =		logging.get_logger(__name__)

_lowercase		:				Union[str, Any]	       =		{
    "facebook/s2t-small-librispeech-asr": (
        "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech_to_text
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = '''speech_to_text'''
			UpperCamelCase__       = ['''past_key_values''']
			UpperCamelCase__       = {'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







			def __init__(			self       :						str       ,    lowercase_       :						Optional[int]=10000       ,    lowercase_       :						int=12       ,    lowercase_       :						Any=2048       ,    lowercase_       :						Any=4       ,    lowercase_       :						Dict=6       ,    lowercase_       :						Any=2048       ,    lowercase_       :						List[str]=4       ,    lowercase_       :						str=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						List[Any]=True       ,    lowercase_       :						int="relu"       ,    lowercase_       :						str=256       ,    lowercase_       :						int=0.1       ,    lowercase_       :						int=0.0       ,    lowercase_       :						str=0.0       ,    lowercase_       :						Optional[int]=0.02       ,    lowercase_       :						str=2       ,    lowercase_       :						Union[str, Any]=True       ,    lowercase_       :						Any=1       ,    lowercase_       :						Dict=0       ,    lowercase_       :						List[str]=2       ,    lowercase_       :						List[Any]=6000       ,    lowercase_       :						Tuple=1024       ,    lowercase_       :						str=2       ,    lowercase_       :						Any=(5, 5)       ,    lowercase_       :						Union[str, Any]=1024       ,    lowercase_       :						Dict=80       ,    lowercase_       :						List[Any]=1       ,    **lowercase_       :						int       ,    ):
										lowercase_      :    List[Any]       		= vocab_size
										lowercase_      :    str       		= d_model
										lowercase_      :    List[Any]       		= encoder_ffn_dim
										lowercase_      :    str       		= encoder_layers
										lowercase_      :    Dict       		= encoder_attention_heads
										lowercase_      :    str       		= decoder_ffn_dim
										lowercase_      :    int       		= decoder_layers
										lowercase_      :    Any       		= decoder_attention_heads
										lowercase_      :    Any       		= dropout
										lowercase_      :    Dict       		= attention_dropout
										lowercase_      :    Optional[int]       		= activation_dropout
										lowercase_      :    Any       		= activation_function
										lowercase_      :    Union[str, Any]       		= init_std
										lowercase_      :    str       		= encoder_layerdrop
										lowercase_      :    Optional[int]       		= decoder_layerdrop
										lowercase_      :    Dict       		= use_cache
										lowercase_      :    Union[str, Any]       		= encoder_layers
										lowercase_      :    Tuple       		= scale_embedding  # scale factor will be sqrt(d_model) if True
										lowercase_      :    Dict       		= max_source_positions
										lowercase_      :    Optional[int]       		= max_target_positions
										lowercase_      :    Tuple       		= num_conv_layers
										lowercase_      :    Tuple       		= list(lowercase_					)
										lowercase_      :    Union[str, Any]       		= conv_channels
										lowercase_      :    str       		= input_feat_per_channel
										lowercase_      :    str       		= input_channels

										if len(self.conv_kernel_sizes					) != self.num_conv_layers:
																	raise ValueError(
																	    """Configuration for convolutional module is incorrect. """
																	    """It is required that `len(config.conv_kernel_sizes)` == `config.num_conv_layers` """
																	    f'''but is `len(config.conv_kernel_sizes) = {len(self.conv_kernel_sizes					)}`, '''
																	    f'''`config.num_conv_layers = {self.num_conv_layers}`.'''					)

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





