


"""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 _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




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


def _lowerCamelCase      (						_UpperCamelCase ):

			'''simple docstring'''




			__lowerCAmelCase							       =     np.max(_outputs       ,					axis=-1       ,					keepdims=_UpperCamelCase )
			__lowerCAmelCase							       =     np.exp(_outputs - maxes )
			return shifted_exp / shifted_exp.sum(axis=-1       ,					keepdims=_UpperCamelCase )
class     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       Optional[int]							 ="""sigmoid"""
							__UpperCAmelCase		:       Optional[int]							 ="""softmax"""
							__UpperCAmelCase		:       Dict							 ="""none"""
@add_end_docstrings(
    lowerCAmelCase__			,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     _UpperCamelCase    (  lowerCAmelCase__						):
							'''simple docstring'''







							__UpperCAmelCase		:       int							 =False
							__UpperCAmelCase		:       Tuple							 =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      snake_case							(	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"
										__lowerCAmelCase							       =     tokenizer_kwargs

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

										if isinstance(__a       ,  __a      ) or top_k is None:
													__lowerCAmelCase							       =     top_k
													__lowerCAmelCase							       =     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:
																__lowerCAmelCase							       =     None
													else:
																__lowerCAmelCase							       =     1

										if isinstance(__a       ,  __a      ):
													__lowerCAmelCase							       =     ClassificationFunction[function_to_apply.upper()]

										if function_to_apply is not None:
													__lowerCAmelCase							       =     function_to_apply
										return preprocess_params, {}, postprocess_params

							def __call__(	self       ,  *__a       ,  **__a      ):
										__lowerCAmelCase							       =     super().__call__(*__a       ,  **__a      )
										# TODO try and retrieve it in a nicer way from _sanitize_parameters.
										__lowerCAmelCase							       =     "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      snake_case							(	self       ,  __a       ,  **__a      ):
										__lowerCAmelCase							       =     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      snake_case							(	self       ,  __a      ):
										return self.model(**__a      )



							def      snake_case							(	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:
																__lowerCAmelCase							       =     ClassificationFunction.SIGMOID
													elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
																__lowerCAmelCase							       =     ClassificationFunction.SOFTMAX
													elif hasattr(self.model.config       ,  "function_to_apply"      ) and function_to_apply is None:
																__lowerCAmelCase							       =     self.model.config.function_to_apply
													else:
																__lowerCAmelCase							       =     ClassificationFunction.NONE

										__lowerCAmelCase							       =     model_outputs["logits"][0]
										__lowerCAmelCase							       =     outputs.numpy()

										if function_to_apply == ClassificationFunction.SIGMOID:
													__lowerCAmelCase							       =     sigmoid(__a      )
										elif function_to_apply == ClassificationFunction.SOFTMAX:
													__lowerCAmelCase							       =     softmax(__a      )
										elif function_to_apply == ClassificationFunction.NONE:
													__lowerCAmelCase							       =     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()}

										__lowerCAmelCase							       =     [
										    {"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:
																__lowerCAmelCase							       =     dict_scores[:top_k]
										return dict_scores








"""simple docstring"""


import unittest

from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:
	class     _UpperCamelCase    :
								'''simple docstring'''







								@staticmethod
								def      snake_case							(	*__a       ,  **__a      ):
											pass
@is_pipeline_test
@require_torch
@require_vision
class     _UpperCamelCase    (  unittest.TestCase						):
							'''simple docstring'''







							__UpperCAmelCase		:       str							 =MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

							def      snake_case							(	self       ,  __a       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     [
										    {
										        "image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      ),
										        "question": "How many cats are there?",
										    },
										    {
										        "image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
										        "question": "How many cats are there?",
										    },
										]
										return vqa_pipeline, examples

							def      snake_case							(	self       ,  __a       ,  __a      ):
										__lowerCAmelCase							       =     vqa_pipeline(__a       ,  top_k=1      )
										self.assertEqual(
										    __a       ,  [
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										        [{"score": ANY(__a      ), "answer": ANY(__a      )}],
										    ]       ,  )

							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="hf-internal-testing/tiny-vilt-random-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question="How many cats are there?"       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    __a       ,  [{"score": ANY(__a      ), "answer": ANY(__a      )}, {"score": ANY(__a      ), "answer": ANY(__a      )}]      )

							@slow
							@require_torch
							def      snake_case							(	self      ):
										__lowerCAmelCase							       =     pipeline("visual-question-answering"       ,  model="dandelin/vilt-b32-finetuned-vqa"      )
										__lowerCAmelCase							       =     "./tests/fixtures/tests_samples/COCO/000000039769.png"
										__lowerCAmelCase							       =     "How many cats are there?"

										__lowerCAmelCase							       =     vqa_pipeline(image=__a       ,  question=__a       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline({"image": image, "question": question}       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]      )

										__lowerCAmelCase							       =     vqa_pipeline(
										    [{"image": image, "question": question}, {"image": image, "question": question}]       ,  top_k=2      )
										self.assertEqual(
										    nested_simplify(__a       ,  decimals=4      )       ,  [[{"score": 0.8_7_9_9, "answer": "2"}, {"score": 0.2_9_6, "answer": "1"}]] * 2       ,  )



							@require_tf
							@unittest.skip("Visual question answering not implemented in TF"      )
							def      snake_case							(	self      ):
										pass




