



"""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  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






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

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[int] ):
						"""simple docstring"""






						snake_case   :			Any						    = np.max(_outputs     ,		axis=-1     ,		keepdims=lowerCamelCase_ )
						snake_case   :			Tuple						    = np.exp(_outputs - maxes )
						return shifted_exp / shifted_exp.sum(axis=-1     ,		keepdims=lowerCamelCase_ )

class       _a  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     """sigmoid"""
			__magic_name__     					=     """softmax"""
			__magic_name__     					=     """none"""



@add_end_docstrings(
    SCREAMING_SNAKE_CASE__     ,	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  ( SCREAMING_SNAKE_CASE__):
			__magic_name__     					=     False
			__magic_name__     					=     ClassificationFunction.NONE



			def __init__(			self							:				List[Any]   ,	**_lowercase							:				Tuple		)							->					Union[str, Any]:
									super().__init__(**_lowercase		)

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



			def 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Dict=None   ,	_lowercase							:				List[Any]=None   ,	_lowercase							:				Optional[int]=""   ,	**_lowercase							:				List[Any]		)							->					Tuple:
									# Using "" as default argument because we're going to use `top_k=None` in user code to declare
									# "No top_k"
									snake_case   :			Optional[int]						    = tokenizer_kwargs

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

									if isinstance(_lowercase   ,	_lowercase		) or top_k is None:
															snake_case   :			str						    = top_k
															snake_case   :			int						    = 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:
																					snake_case   :			List[Any]						    = None
															else:
																					snake_case   :			Optional[int]						    = 1

									if isinstance(_lowercase   ,	_lowercase		):
															snake_case   :			Tuple						    = ClassificationFunction[function_to_apply.upper()]

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



			def __call__(			self							:				Optional[Any]   ,	*_lowercase							:				Optional[int]   ,	**_lowercase							:				Any		)							->					Dict:
									snake_case   :			Optional[int]						    = super().__call__(*_lowercase   ,	**_lowercase		)
									# TODO try and retrieve it in a nicer way from _sanitize_parameters.
									snake_case   :			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 				__lowercase					(			self							:				List[str]   ,	_lowercase							:				Optional[Any]   ,	**_lowercase							:				List[Any]		)							->					Dict[str, GenericTensor]:
									snake_case   :			str						    = 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 				__lowercase					(			self							:				List[Any]   ,	_lowercase							:				Optional[Any]		)							->					List[Any]:
									return self.model(**_lowercase		)



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

									snake_case   :			List[Any]						    = model_outputs["logits"][0]
									snake_case   :			Dict						    = outputs.numpy()

									if function_to_apply == ClassificationFunction.SIGMOID:
															snake_case   :			Union[str, Any]						    = sigmoid(_lowercase		)
									elif function_to_apply == ClassificationFunction.SOFTMAX:
															snake_case   :			List[Any]						    = softmax(_lowercase		)
									elif function_to_apply == ClassificationFunction.NONE:
															snake_case   :			Tuple						    = 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()}

									snake_case   :			List[Any]						    = [
									    {"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:
																					snake_case   :			List[Any]						    = dict_scores[:top_k]
									return dict_scores








"""simple docstring"""







import os
import textwrap

import pyarrow as pa
import pytest

from datasets import ClassLabel, Features, Image
from datasets.packaged_modules.csv.csv import Csv

from ..utils import require_pil

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			str						    = tmp_path / "file.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = tmp_path / "malformed_file.csv"
						snake_case   :			Union[str, Any]						    = textwrap.dedent(
						    "\\n        header1,header2\n        1,2\n        10,20,\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int     ,		lowerCamelCase_:							Optional[Any] ):
						"""simple docstring"""






						snake_case   :			Dict						    = tmp_path / "csv_with_image.csv"
						snake_case   :			List[Any]						    = textwrap.dedent(
						    f'''\
        image
        {image_file}
        ''' )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Optional[Any]						    = tmp_path / "csv_with_label.csv"
						snake_case   :			Tuple						    = textwrap.dedent(
						    "\\n        label\n        good\n        bad\n        good\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

@pytest.fixture
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[Any] ):
						"""simple docstring"""






						snake_case   :			Union[str, Any]						    = tmp_path / "csv_with_int_list.csv"
						snake_case   :			Optional[int]						    = textwrap.dedent(
						    "\\n        int_list\n        1 2 3\n        4 5 6\n        7 8 9\n        " )
						with open(lowerCamelCase_     ,		"w" ) as f:
												f.write(lowerCamelCase_ )
						return str(lowerCamelCase_ )

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							List[str]     ,		lowerCamelCase_:							Any     ,		lowerCamelCase_:							List[str] ):
						"""simple docstring"""






						snake_case   :			Tuple						    = Csv()
						snake_case   :			str						    = csv._generate_tables([[csv_file, malformed_csv_file]] )
						with pytest.raises(lowerCamelCase_     ,		match="Error tokenizing data" ):
												for _ in generator:
																		pass
						assert any(
						    record.levelname == "ERROR"
						    and "Failed to read file" in record.message
						    and os.path.basename(lowerCamelCase_ ) in record.message
						    for record in caplog.records )

@require_pil
def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							str ):
						"""simple docstring"""






						with open(lowerCamelCase_     ,		encoding="utf-8" ) as f:
												snake_case   :			List[Any]						    = f.read().splitlines()[1]
						snake_case   :			List[str]						    = Csv(encoding="utf-8"     ,		features=Features({"image": Image()} ) )
						snake_case   :			Optional[int]						    = csv._generate_tables([[csv_file_with_image]] )
						snake_case   :			List[Any]						    = pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field("image" ).type == Image()()
						snake_case   :			str						    = pa_table.to_pydict()["image"]
						assert generated_content == [{"path": image_file, "bytes": None}]

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							int ):
						"""simple docstring"""






						with open(lowerCamelCase_     ,		encoding="utf-8" ) as f:
												snake_case   :			Optional[Any]						    = f.read().splitlines()[1:]
						snake_case   :			List[str]						    = Csv(encoding="utf-8"     ,		features=Features({"label": ClassLabel(names=["good", "bad"] )} ) )
						snake_case   :			int						    = csv._generate_tables([[csv_file_with_label]] )
						snake_case   :			Dict						    = pa.concat_tables([table for _, table in generator] )
						assert pa_table.schema.field("label" ).type == ClassLabel(names=["good", "bad"] )()
						snake_case   :			Dict						    = pa_table.to_pydict()["label"]
						assert generated_content == [ClassLabel(names=["good", "bad"] ).straint(lowerCamelCase_ ) for label in labels]

def  __SCREAMING_SNAKE_CASE		(			lowerCamelCase_:							Union[str, Any] ):
						"""simple docstring"""






						snake_case   :			List[Any]						    = Csv(encoding="utf-8"     ,		sep=","     ,		converters={"int_list": lambda lowerCamelCase_ : [int(lowerCamelCase_ ) for i in x.split()]} )
						snake_case   :			Tuple						    = csv._generate_tables([[csv_file_with_int_list]] )
						snake_case   :			str						    = pa.concat_tables([table for _, table in generator] )
						assert pa.types.is_list(pa_table.schema.field("int_list" ).type )
						snake_case   :			Any						    = pa_table.to_pydict()["int_list"]
						assert generated_content == [[1, 2, 3], [4, 5, 6], [7, 8, 9]]



