
"""simple docstring"""




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     __magic_name__      ( __snake_case							:			Tuple    )					->							Tuple:
       return 1.0 / (1.0 + np.exp(-_outputs    ))


def     __magic_name__      ( __snake_case							:			Optional[Any]    )					->							int:
       lowercase       :							str									=				np.max(_outputs	,					axis=-1	,					keepdims=__snake_case    )
       lowercase       :							Any									=				np.exp(_outputs - maxes    )
       return shifted_exp / shifted_exp.sum(axis=-1	,					keepdims=__snake_case    )

class 						a__		( a_		):
   __lowerCAmelCase				=							"""sigmoid"""
   __lowerCAmelCase				=							"""softmax"""
   __lowerCAmelCase				=							"""none"""


@add_end_docstrings(
    a_,     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 						a__		( a_		):
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							ClassificationFunction.NONE
   def __init__(     self      ,       **_a							):
          super().__init__(**_a							)

          self.check_model_type(
              TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
              if self.framework == "tf"
              else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING							)
   def      __magic_name__     (     self      ,       _a=None      ,       _a=None      ,       _a=""      ,       **_a							):
          # Using "" as default argument because we're going to use `top_k=None` in user code to declare
          # "No top_k"
          lowercase       :							Any									=				tokenizer_kwargs

          lowercase       :							Dict									=				{}
          if hasattr(self.model.config      ,       "return_all_scores"							) and return_all_scores is None:
                 lowercase       :							Dict									=				self.model.config.return_all_scores

          if isinstance(_a      ,       _a							) or top_k is None:
                 lowercase       :							List[str]									=				top_k
                 lowercase       :							Tuple									=				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`."      ,       _a      ,       )
                 if return_all_scores:
                        lowercase       :							Tuple									=				None
                 else:
                        lowercase       :							Tuple									=				1

          if isinstance(_a      ,       _a							):
                 lowercase       :							Optional[int]									=				ClassificationFunction[function_to_apply.upper()]

          if function_to_apply is not None:
                 lowercase       :							Union[str, Any]									=				function_to_apply
          return preprocess_params, {}, postprocess_params
   def __call__(     self      ,       *_a      ,       **_a							):
          lowercase       :							Optional[Any]									=				super().__call__(*_a      ,       **_a							)
          # TODO try and retrieve it in a nicer way from _sanitize_parameters.
          lowercase       :							Any									=				"top_k" not in kwargs
          if isinstance(args[0]      ,       _a							) and _legacy:
                 # This pipeline is odd, and return a list when single item is run
                 return [result]
          else:
                 return result
   def      __magic_name__     (     self      ,       _a      ,       **_a							):
          lowercase       :							Union[str, Any]									=				self.framework
          if isinstance(_a      ,       _a							):
                 return self.tokenizer(**_a      ,       return_tensors=_a      ,       **_a							)
          elif isinstance(_a      ,       _a							) and len(_a							) == 1 and isinstance(inputs[0]      ,       _a							) 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=_a      ,       **_a							)
          elif isinstance(_a      ,       _a							):
                 # 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(_a      ,       return_tensors=_a      ,       **_a							)
   def      __magic_name__     (     self      ,       _a							):
          return self.model(**_a							)

   def      __magic_name__     (     self      ,       _a      ,       _a=None      ,       _a=1      ,       _a=True							):
          # `_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:
                        lowercase       :							int									=				ClassificationFunction.SIGMOID
                 elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                        lowercase       :							Tuple									=				ClassificationFunction.SOFTMAX
                 elif hasattr(self.model.config      ,       "function_to_apply"							) and function_to_apply is None:
                        lowercase       :							Optional[Any]									=				self.model.config.function_to_apply
                 else:
                        lowercase       :							List[Any]									=				ClassificationFunction.NONE

          lowercase       :							Tuple									=				model_outputs["logits"][0]
          lowercase       :							Dict									=				outputs.numpy()

          if function_to_apply == ClassificationFunction.SIGMOID:
                 lowercase       :							Optional[Any]									=				sigmoid(_a							)
          elif function_to_apply == ClassificationFunction.SOFTMAX:
                 lowercase       :							Tuple									=				softmax(_a							)
          elif function_to_apply == ClassificationFunction.NONE:
                 lowercase       :							str									=				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()}

          lowercase       :							Union[str, Any]									=				[
              {"label": self.model.config.idalabel[i], "score": score.item()} for i, score in enumerate(_a							)
          ]
          if not _legacy:
                 dict_scores.sort(key=lambda _a							: x["score"]      ,       reverse=_a							)
                 if top_k is not None:
                        lowercase       :							Any									=				dict_scores[:top_k]
          return dict_scores




"""simple docstring"""




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A     :			Optional[int]  						=			{
    """configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
    """processing_mgp_str""": ["""MgpstrProcessor"""],
    """tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			[
       """MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """MgpstrModel""",
       """MgpstrPreTrainedModel""",
       """MgpstrForSceneTextRecognition""",
   ]

if TYPE_CHECKING:
   from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
   from .processing_mgp_str import MgpstrProcessor
   from .tokenization_mgp_str import MgpstrTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_mgp_str import (
          MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
          MgpstrForSceneTextRecognition,
          MgpstrModel,
          MgpstrPreTrainedModel,
      )
else:
   import sys

   _A     :			Tuple  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


