import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    return 1.0 / (1.0 + np.exp(-_outputs     ))



def lowerCamelCase  (       SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[int]        =		np.max(_outputs  ,      axis=-1  ,      keepdims=UpperCamelCase__     )
    __UpperCamelCase					:Any        =		np.exp(_outputs - maxes     )
    return shifted_exp / shifted_exp.sum(axis=-1  ,      keepdims=UpperCamelCase__     )


class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						List[str]      =				"""sigmoid"""
    a__     :						Union[str, Any]      =				"""softmax"""
    a__     :						Any      =				"""none"""


@add_end_docstrings(
    UpperCAmelCase_						,     r"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """						,     )
class      lowerCamelCase_					(       UpperCAmelCase_			):






    '''simple docstring'''






    a__     :						Any      =				False
    a__     :						Dict      =				ClassificationFunction.NONE







    def __init__(     self			,			**__lowercase)	->     str:
        super().__init__(**__lowercase)

        self.check_model_type(
            TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
            if self.framework == '''tf'''
            else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING)







    def       UpperCamelCase__       (     self			,			__lowercase=None			,			__lowercase=None			,			__lowercase=""			,			**__lowercase)	->     Dict:
        # Using "" as default argument because we're going to use `top_k=None` in user code to declare
        # "No top_k"
        __UpperCamelCase					:Any        =		tokenizer_kwargs

        __UpperCamelCase					:Optional[Any]        =		{}
        if hasattr(self.model.config			,			'''return_all_scores''') and return_all_scores is None:
            __UpperCamelCase					:Optional[int]        =		self.model.config.return_all_scores

        if isinstance(__lowercase			,			__lowercase) or top_k is None:
            __UpperCamelCase					:Any        =		top_k
            __UpperCamelCase					:List[Any]        =		False
        elif return_all_scores is not None:
            warnings.warn(
                '''`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'''
                ''' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'''			,			__lowercase			,			)
            if return_all_scores:
                __UpperCamelCase					:Dict        =		None
            else:
                __UpperCamelCase					:Tuple        =		1

        if isinstance(__lowercase			,			__lowercase):
            __UpperCamelCase					:int        =		ClassificationFunction[function_to_apply.upper()]

        if function_to_apply is not None:
            __UpperCamelCase					:Union[str, Any]        =		function_to_apply
        return preprocess_params, {}, postprocess_params







    def __call__(     self			,			*__lowercase			,			**__lowercase)	->     Dict:
        __UpperCamelCase					:Optional[int]        =		super().__call__(*__lowercase			,			**__lowercase)
        # TODO try and retrieve it in a nicer way from _sanitize_parameters.
        __UpperCamelCase					:List[str]        =		'''top_k''' not in kwargs
        if isinstance(args[0]			,			__lowercase) and _legacy:
            # This pipeline is odd, and return a list when single item is run
            return [result]
        else:
            return result







    def       UpperCamelCase__       (     self			,			__lowercase			,			**__lowercase)	->     Dict[str, GenericTensor]:
        __UpperCamelCase					:Union[str, Any]        =		self.framework
        if isinstance(__lowercase			,			__lowercase):
            return self.tokenizer(**__lowercase			,			return_tensors=__lowercase			,			**__lowercase)
        elif isinstance(__lowercase			,			__lowercase) and len(__lowercase) == 1 and isinstance(inputs[0]			,			__lowercase) and len(inputs[0]) == 2:
            # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
            return self.tokenizer(
                text=inputs[0][0]			,			text_pair=inputs[0][1]			,			return_tensors=__lowercase			,			**__lowercase)
        elif isinstance(__lowercase			,			__lowercase):
            # This is likely an invalid usage of the pipeline attempting to pass text pairs.
            raise ValueError(
                '''The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'''
                ''' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.''')
        return self.tokenizer(__lowercase			,			return_tensors=__lowercase			,			**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase)	->     List[Any]:
        return self.model(**__lowercase)







    def       UpperCamelCase__       (     self			,			__lowercase			,			__lowercase=None			,			__lowercase=1			,			__lowercase=True)	->     Any:
        # `_legacy` is used to determine if we're running the naked pipeline and in backward
        # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
        # the more natural result containing the list.
        # Default value before `set_parameters`
        if function_to_apply is None:
            if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                __UpperCamelCase					:Any        =		ClassificationFunction.SIGMOID
            elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                __UpperCamelCase					:int        =		ClassificationFunction.SOFTMAX
            elif hasattr(self.model.config			,			'''function_to_apply''') and function_to_apply is None:
                __UpperCamelCase					:Optional[int]        =		self.model.config.function_to_apply
            else:
                __UpperCamelCase					:str        =		ClassificationFunction.NONE

        __UpperCamelCase					:int        =		model_outputs['''logits'''][0]
        __UpperCamelCase					:Optional[Any]        =		outputs.numpy()

        if function_to_apply == ClassificationFunction.SIGMOID:
            __UpperCamelCase					:List[Any]        =		sigmoid(__lowercase)
        elif function_to_apply == ClassificationFunction.SOFTMAX:
            __UpperCamelCase					:List[Any]        =		softmax(__lowercase)
        elif function_to_apply == ClassificationFunction.NONE:
            __UpperCamelCase					:Dict        =		outputs
        else:
            raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}""")

        if top_k == 1 and _legacy:
            return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

        __UpperCamelCase					:Optional[int]        =		[
            {'''label''': self.model.config.idalabel[i], '''score''': score.item()} for i, score in enumerate(__lowercase)
        ]
        if not _legacy:
            dict_scores.sort(key=lambda __lowercase: x["score"]			,			reverse=__lowercase)
            if top_k is not None:
                __UpperCamelCase					:Tuple        =		dict_scores[:top_k]
        return dict_scores






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


__lowercase										= {
    '''configuration_layoutlmv3''': [
        '''LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LayoutLMv3Config''',
        '''LayoutLMv3OnnxConfig''',
    ],
    '''processing_layoutlmv3''': ['''LayoutLMv3Processor'''],
    '''tokenization_layoutlmv3''': ['''LayoutLMv3Tokenizer'''],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3TokenizerFast''']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''LayoutLMv3ForQuestionAnswering''',
        '''LayoutLMv3ForSequenceClassification''',
        '''LayoutLMv3ForTokenClassification''',
        '''LayoutLMv3Model''',
        '''LayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= [
        '''TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFLayoutLMv3ForQuestionAnswering''',
        '''TFLayoutLMv3ForSequenceClassification''',
        '''TFLayoutLMv3ForTokenClassification''',
        '''TFLayoutLMv3Model''',
        '''TFLayoutLMv3PreTrainedModel''',
    ]

try:
    if not is_vision_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __lowercase										= ['''LayoutLMv3FeatureExtractor''']
    __lowercase										= ['''LayoutLMv3ImageProcessor''']


if TYPE_CHECKING:
    from .configuration_layoutlmva import (
        LAYOUTLMV3_PRETRAINED_CONFIG_ARCHIVE_MAP,
        LayoutLMvaConfig,
        LayoutLMvaOnnxConfig,
    )
    from .processing_layoutlmva import LayoutLMvaProcessor
    from .tokenization_layoutlmva import LayoutLMvaTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_layoutlmva_fast import LayoutLMvaTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_layoutlmva import (
            LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            LayoutLMvaForQuestionAnswering,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaModel,
            LayoutLMvaPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_layoutlmva import (
            TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFLayoutLMvaForQuestionAnswering,
            TFLayoutLMvaForSequenceClassification,
            TFLayoutLMvaForTokenClassification,
            TFLayoutLMvaModel,
            TFLayoutLMvaPreTrainedModel,
        )

    try:
        if not is_vision_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .feature_extraction_layoutlmva import LayoutLMvaFeatureExtractor
        from .image_processing_layoutlmva import LayoutLMvaImageProcessor

else:
    import sys

    __lowercase										= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





