
"""simple docstring"""





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

_a														=		logging.get_logger(__name__)






@add_end_docstrings(lowerCamelCase     )
class    _UpperCAmelCase(   lowerCamelCase     ):






		"""simple docstring"""




		def __init__( self    ,		*__a    ,		**__a)	->	Tuple:






							'''simple docstring'''
							super().__init__(*__a    ,		**__a)
							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 						UpperCAmelCase							( self    ,		__a=None)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  {}
							if top_k is not None:
												_UpperCamelCase										=  top_k
							return {}, {}, postprocess_params



		def __call__( self    ,		__a    ,		**__a)	->	Tuple:






							'''simple docstring'''
							return super().__call__(__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a)	->	int:






							'''simple docstring'''
							_UpperCamelCase										=  load_image(__a)
							_UpperCamelCase										=  self.image_processor(images=__a    ,		return_tensors=self.framework)
							return model_inputs



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							_UpperCamelCase										=  self.model(**__a)
							return model_outputs




		def 						UpperCAmelCase							( self    ,		__a    ,		__a=5)	->	Union[str, Any]:






							'''simple docstring'''
							if top_k > self.model.config.num_labels:
												_UpperCamelCase										=  self.model.config.num_labels

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

							_UpperCamelCase										=  scores.tolist()
							_UpperCamelCase										=  ids.tolist()
							return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(__a    ,		__a)]








"""simple docstring"""





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


_a														=		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__		(      __snake_case,					__snake_case     )							->		Optional[int]:


					"""simple docstring"""





					return (abs(source - target     ) / target) < 0.01



@pytest.mark.integration
def 				lowerCamelCase__		(      __snake_case     )							->		Dict:


					"""simple docstring"""





					_UpperCamelCase										=  _TestCommandArgs(dataset=__snake_case,					all_configs=__snake_case,					save_infos=__snake_case     )
					_UpperCamelCase										=  TestCommand(*__snake_case     )
					test_command.run()
					_UpperCamelCase										=  os.path.join(__snake_case,					'''README.md'''     )
					assert os.path.exists(__snake_case     )
					_UpperCamelCase										=  DatasetInfosDict.from_directory(__snake_case     )
					_UpperCamelCase										=  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_35_15_63,
					                    '''num_examples''': 1_00_00,
					                },
					                {
					                    '''name''': '''validation''',
					                    '''num_bytes''': 23_84_18,
					                    '''num_examples''': 10_00,
					                },
					            ],					download_size=3_94_06_80,					dataset_size=2_58_99_81,					)
					    }     )
					assert dataset_infos.keys() == expected_dataset_infos.keys()
					for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
										_UpperCamelCase			,  _UpperCamelCase										=  getattr(dataset_infos['''default'''],					__snake_case     ), getattr(expected_dataset_infos['''default'''],					__snake_case     )
										if key == "num_bytes":
															assert is_apercent_close(__snake_case,					__snake_case     )
										elif key == "splits":
															assert list(__snake_case     ) == list(__snake_case     )
															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






