


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			import tensorflow as tf

			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

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

_snake_case					:		Optional[int]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  Tuple   ,     *lowerCamelCase	:  Any   ,     **lowerCamelCase	:  Tuple    )     ->   int:
														super().__init__(*lowerCamelCase   ,     **lowerCamelCase    )
														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING    )

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  Optional[Any]=None    )     ->   Optional[int]:
														__snake_case		:      Optional[Any]										=       {}
														if top_k is not None:
																					__snake_case		:      List[Any]										=       top_k
														return {}, {}, postprocess_params

							def __call__(							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, List[str], "Image.Image", List["Image.Image"]]   ,     **lowerCamelCase	:  Dict    )     ->   Optional[int]:
														return super().__call__(lowerCamelCase   ,     **lowerCamelCase    )

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  List[Any]    )     ->   int:
														__snake_case		:      Any										=       load_image(lowerCamelCase    )
														__snake_case		:      str										=       self.image_processor(images=lowerCamelCase   ,     return_tensors=self.framework    )
														return model_inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  List[str]    )     ->   Tuple:
														__snake_case		:      List[Any]										=       self.model(**lowerCamelCase    )
														return model_outputs






							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Optional[Any]=5    )     ->   List[str]:
														if top_k > self.model.config.num_labels:
																					__snake_case		:      int										=       self.model.config.num_labels

														if self.framework == "pt":
																					__snake_case		:      Optional[Any]										=       model_outputs.logits.softmax(-1    )[0]
																					__snake_case			,      __snake_case		:      List[str]										=       probs.topk(lowerCamelCase    )
														elif self.framework == "tf":
																					__snake_case		:      Tuple										=       stable_softmax(model_outputs.logits   ,     axis=-1    )[0]
																					__snake_case		:      Optional[Any]										=       tf.math.top_k(lowerCamelCase   ,     k=lowerCamelCase    )
																					__snake_case			,      __snake_case		:      List[Any]										=       topk.values.numpy(), topk.indices.numpy()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Any										=       scores.tolist()
														__snake_case		:      Optional[int]										=       ids.tolist()
														return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase   ,     lowerCamelCase    )]





import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_snake_case					:		List[str]      =    namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							return (abs(source - target     ) / target) < 0.0_1







@pytest.mark.integration
def     lowerCAmelCase_					( __lowerCamelCase     ):
							__snake_case		:      Tuple										=       _TestCommandArgs(dataset=__lowerCamelCase							,     all_configs=__lowerCamelCase							,     save_infos=__lowerCamelCase     )
							__snake_case		:      List[Any]										=       TestCommand(*__lowerCamelCase     )
							test_command.run()
							__snake_case		:      List[Any]										=       os.path.join(__lowerCamelCase							,     "README.md"     )
							assert os.path.exists(__lowerCamelCase     )
							__snake_case		:      Optional[Any]										=       DatasetInfosDict.from_directory(__lowerCamelCase     )
							__snake_case		:      List[str]										=       DatasetInfosDict(
							    {
							        "default": DatasetInfo(
							            features=Features(
							                {
							                    "tokens": Sequence(Value("string"     )     ),
							                    "ner_tags": Sequence(
							                        ClassLabel(names=["O", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]     )     ),
							                    "langs": Sequence(Value("string"     )     ),
							                    "spans": Sequence(Value("string"     )     ),
							                }     )							,     splits=[
							                {
							                    "name": "train",
							                    "num_bytes": 2_3_5_1_5_6_3,
							                    "num_examples": 1_0_0_0_0,
							                },
							                {
							                    "name": "validation",
							                    "num_bytes": 2_3_8_4_1_8,
							                    "num_examples": 1_0_0_0,
							                },
							            ]							,     download_size=3_9_4_0_6_8_0							,     dataset_size=2_5_8_9_9_8_1							,     )
							    }     )
							assert dataset_infos.keys() == expected_dataset_infos.keys()
							for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
														__snake_case			,      __snake_case		:      Tuple										=       getattr(dataset_infos["default"]							,     __lowerCamelCase     ), getattr(expected_dataset_infos["default"]							,     __lowerCamelCase     )
														if key == "num_bytes":
																					assert is_apercent_close(__lowerCamelCase							,     __lowerCamelCase     )
														elif key == "splits":
																					assert list(__lowerCamelCase     ) == list(__lowerCamelCase     )
																					for split in result:
																												assert result[split].name == expected[split].name
																												assert result[split].num_examples == expected[split].num_examples
																												assert is_apercent_close(result[split].num_bytes							,     expected[split].num_bytes     )
														else:
																					result == expected

