





"""simple docstring"""



import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
       import faiss






@require_faiss
class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					tempfile.mkdtemp()
     __A   :						Tuple									=					8

     # DPR tok
     __A   :						Any									=					[
         '''[UNK]''',
         '''[CLS]''',
         '''[SEP]''',
         '''[PAD]''',
         '''[MASK]''',
         '''want''',
         '''##want''',
         '''##ed''',
         '''wa''',
         '''un''',
         '''runn''',
         '''##ing''',
         ''',''',
         '''low''',
         '''lowest''',
     ]
     __A   :						List[Any]									=					os.path.join(self.tmpdirname ,						'''dpr_tokenizer'''  )
     os.makedirs(__lowerCamelCase ,						exist_ok=__lowerCamelCase  )
     __A   :						Tuple									=					os.path.join(__lowerCamelCase ,						DPR_VOCAB_FILES_NAMES['''vocab_file''']  )
     with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''  ) as vocab_writer:
         vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]  )  )

     # BART tok
     __A   :						Optional[Any]									=					[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''\u0120''',
         '''\u0120l''',
         '''\u0120n''',
         '''\u0120lo''',
         '''\u0120low''',
         '''er''',
         '''\u0120lowest''',
         '''\u0120newer''',
         '''\u0120wider''',
         '''<unk>''',
     ]
     __A   :						Union[str, Any]									=					dict(zip(__lowerCamelCase ,						range(len(__lowerCamelCase  )  )  )  )
     __A   :						int									=					['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
     __A   :						Optional[int]									=					{'''unk_token''': '''<unk>'''}

     __A   :						Optional[Any]									=					os.path.join(self.tmpdirname ,						'''bart_tokenizer'''  )
     os.makedirs(__lowerCamelCase ,						exist_ok=__lowerCamelCase  )
     __A   :						str									=					os.path.join(__lowerCamelCase ,						BART_VOCAB_FILES_NAMES['''vocab_file''']  )
     __A   :						int									=					os.path.join(__lowerCamelCase ,						BART_VOCAB_FILES_NAMES['''merges_file''']  )
     with open(self.vocab_file ,						'''w''' ,						encoding='''utf-8'''  ) as fp:
         fp.write(json.dumps(__lowerCamelCase  ) + '''\n'''  )
     with open(self.merges_file ,						'''w''' ,						encoding='''utf-8'''  ) as fp:
         fp.write('''\n'''.join(__lowerCamelCase  )  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,						'''dpr_tokenizer'''  )  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,						'''dpr_tokenizer'''  )  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,						'''bart_tokenizer'''  )  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     shutil.rmtree(self.tmpdirname  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					Dataset.from_dict(
         {
             '''id''': ['''0''', '''1'''],
             '''text''': ['''foo''', '''bar'''],
             '''title''': ['''Foo''', '''Bar'''],
             '''embeddings''': [np.ones(self.retrieval_vector_size  ), 2 * np.ones(self.retrieval_vector_size  )],
         }  )
     dataset.add_faiss_index('''embeddings''' ,						string_factory='''Flat''' ,						metric_type=faiss.METRIC_INNER_PRODUCT  )
     return dataset

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					self.get_dummy_dataset()
     __A   :						str									=					RagConfig(
         retrieval_vector_size=self.retrieval_vector_size ,						question_encoder=DPRConfig().to_dict() ,						generator=BartConfig().to_dict() ,						)
     with patch('''transformers.models.rag.retrieval_rag.load_dataset'''  ) as mock_load_dataset:
         __A   :						Optional[int]									=					dataset
         __A   :						Tuple									=					RagRetriever(
             __lowerCamelCase ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer() ,						)
     return retriever

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						str									=					self.get_dummy_dataset()
     __A   :						Optional[int]									=					RagConfig(
         retrieval_vector_size=self.retrieval_vector_size ,						question_encoder=DPRConfig().to_dict() ,						generator=BartConfig().to_dict() ,						index_name='''custom''' ,						)
     if from_disk:
         __A   :						Dict									=					os.path.join(self.tmpdirname ,						'''dataset'''  )
         __A   :						Dict									=					os.path.join(self.tmpdirname ,						'''index.faiss'''  )
         dataset.get_index('''embeddings'''  ).save(os.path.join(self.tmpdirname ,						'''index.faiss'''  )  )
         dataset.drop_index('''embeddings'''  )
         dataset.save_to_disk(os.path.join(self.tmpdirname ,						'''dataset'''  )  )
         del dataset
         __A   :						Optional[Any]									=					RagRetriever(
             __lowerCamelCase ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer() ,						)
     else:
         __A   :						Union[str, Any]									=					RagRetriever(
             __lowerCamelCase ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer() ,						index=CustomHFIndex(config.retrieval_vector_size ,						__lowerCamelCase  ) ,						)
     return retriever

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						List[Any]									=					Dataset.from_dict(
         {
             '''id''': ['''0''', '''1'''],
             '''text''': ['''foo''', '''bar'''],
             '''title''': ['''Foo''', '''Bar'''],
             '''embeddings''': [np.ones(self.retrieval_vector_size + 1  ), 2 * np.ones(self.retrieval_vector_size + 1  )],
         }  )
     dataset.add_faiss_index('''embeddings''' ,						string_factory='''Flat''' ,						metric_type=faiss.METRIC_INNER_PRODUCT  )

     __A   :						Any									=					os.path.join(self.tmpdirname ,						'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''  )
     dataset.save_faiss_index('''embeddings''' ,						index_file_name + '''.index.dpr'''  )
     pickle.dump(dataset['''id'''] ,						open(index_file_name + '''.index_meta.dpr''' ,						'''wb'''  )  )

     __A   :						Tuple									=					os.path.join(self.tmpdirname ,						'''psgs_w100.tsv.pkl'''  )
     __A   :						Dict									=					{sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
     pickle.dump(__lowerCamelCase ,						open(__lowerCamelCase ,						'''wb'''  )  )

     __A   :						Union[str, Any]									=					RagConfig(
         retrieval_vector_size=self.retrieval_vector_size ,						question_encoder=DPRConfig().to_dict() ,						generator=BartConfig().to_dict() ,						index_name='''legacy''' ,						index_path=self.tmpdirname ,						)
     __A   :						Optional[int]									=					RagRetriever(
         __lowerCamelCase ,						question_encoder_tokenizer=self.get_dpr_tokenizer() ,						generator_tokenizer=self.get_bart_tokenizer()  )
     return retriever

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					1
     __A   :						str									=					self.get_dummy_canonical_hf_index_retriever()
     __A   :						Union[str, Any]									=					np.array(
         [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
     __A			,	__A			,	__A   :						Any									=					retriever.retrieve(__lowerCamelCase ,						n_docs=__lowerCamelCase  )
     self.assertEqual(retrieved_doc_embeds.shape ,						(2, n_docs, self.retrieval_vector_size)  )
     self.assertEqual(len(__lowerCamelCase  ) ,						2  )
     self.assertEqual(sorted(doc_dicts[0]  ) ,						['''embeddings''', '''id''', '''text''', '''title''']  )
     self.assertEqual(len(doc_dicts[0]['''id''']  ) ,						__lowerCamelCase  )
     self.assertEqual(doc_dicts[0]['''id'''][0] ,						'''1'''  )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''id'''][0] ,						'''0'''  )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist() ,						[[1], [0]]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.get_dummy_canonical_hf_index_retriever()
     with tempfile.TemporaryDirectory() as tmp_dirname:
         with patch('''transformers.models.rag.retrieval_rag.load_dataset'''  ) as mock_load_dataset:
             __A   :						str									=					self.get_dummy_dataset()
             retriever.save_pretrained(__lowerCamelCase  )
             __A   :						List[Any]									=					RagRetriever.from_pretrained(__lowerCamelCase  )
             self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )
             __A   :						Union[str, Any]									=					np.array(
                 [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
             __A   :						int									=					retriever.retrieve(__lowerCamelCase ,						n_docs=1  )
             self.assertTrue(out is not None  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					1
     __A   :						List[str]									=					self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase  )
     __A   :						Dict									=					np.array(
         [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
     __A			,	__A			,	__A   :						str									=					retriever.retrieve(__lowerCamelCase ,						n_docs=__lowerCamelCase  )
     self.assertEqual(retrieved_doc_embeds.shape ,						(2, n_docs, self.retrieval_vector_size)  )
     self.assertEqual(len(__lowerCamelCase  ) ,						2  )
     self.assertEqual(sorted(doc_dicts[0]  ) ,						['''embeddings''', '''id''', '''text''', '''title''']  )
     self.assertEqual(len(doc_dicts[0]['''id''']  ) ,						__lowerCamelCase  )
     self.assertEqual(doc_dicts[0]['''id'''][0] ,						'''1'''  )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''id'''][0] ,						'''0'''  )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist() ,						[[1], [0]]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase  )
     with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(__lowerCamelCase  )
         __A   :						str									=					RagRetriever.from_pretrained(__lowerCamelCase  )
         self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )
         __A   :						List[Any]									=					np.array(
             [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
         __A   :						List[Any]									=					retriever.retrieve(__lowerCamelCase ,						n_docs=1  )
         self.assertTrue(out is not None  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Any									=					1
     __A   :						Tuple									=					self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase  )
     __A   :						Union[str, Any]									=					np.array(
         [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
     __A			,	__A			,	__A   :						Optional[Any]									=					retriever.retrieve(__lowerCamelCase ,						n_docs=__lowerCamelCase  )
     self.assertEqual(retrieved_doc_embeds.shape ,						(2, n_docs, self.retrieval_vector_size)  )
     self.assertEqual(len(__lowerCamelCase  ) ,						2  )
     self.assertEqual(sorted(doc_dicts[0]  ) ,						['''embeddings''', '''id''', '''text''', '''title''']  )
     self.assertEqual(len(doc_dicts[0]['''id''']  ) ,						__lowerCamelCase  )
     self.assertEqual(doc_dicts[0]['''id'''][0] ,						'''1'''  )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''id'''][0] ,						'''0'''  )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist() ,						[[1], [0]]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Optional[Any]									=					self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase  )
     with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(__lowerCamelCase  )
         __A   :						Optional[int]									=					RagRetriever.from_pretrained(__lowerCamelCase  )
         self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )
         __A   :						Optional[Any]									=					np.array(
             [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
         __A   :						List[str]									=					retriever.retrieve(__lowerCamelCase ,						n_docs=1  )
         self.assertTrue(out is not None  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						Tuple									=					1
     __A   :						Optional[Any]									=					self.get_dummy_legacy_index_retriever()
     __A   :						Any									=					np.array(
         [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
     __A			,	__A			,	__A   :						Any									=					retriever.retrieve(__lowerCamelCase ,						n_docs=__lowerCamelCase  )
     self.assertEqual(retrieved_doc_embeds.shape ,						(2, n_docs, self.retrieval_vector_size)  )
     self.assertEqual(len(__lowerCamelCase  ) ,						2  )
     self.assertEqual(sorted(doc_dicts[0]  ) ,						['''text''', '''title''']  )
     self.assertEqual(len(doc_dicts[0]['''text''']  ) ,						__lowerCamelCase  )
     self.assertEqual(doc_dicts[0]['''text'''][0] ,						'''bar'''  )  # max inner product is reached with second doc
     self.assertEqual(doc_dicts[1]['''text'''][0] ,						'''foo'''  )  # max inner product is reached with first doc
     self.assertListEqual(doc_ids.tolist() ,						[[1], [0]]  )

 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.get_dummy_legacy_index_retriever()
     with tempfile.TemporaryDirectory() as tmp_dirname:
         retriever.save_pretrained(__lowerCamelCase  )
         __A   :						Union[str, Any]									=					RagRetriever.from_pretrained(__lowerCamelCase  )
         self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )
         __A   :						Tuple									=					np.array(
             [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
         __A   :						Dict									=					retriever.retrieve(__lowerCamelCase ,						n_docs=1  )
         self.assertTrue(out is not None  )

 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     import torch

     __A   :						str									=					1
     __A   :						Tuple									=					self.get_dummy_canonical_hf_index_retriever()
     __A   :						Dict									=					[[5, 7], [10, 11]]
     __A   :						List[Any]									=					np.array(
         [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
     __A   :						int									=					retriever(__lowerCamelCase ,						__lowerCamelCase ,						prefix=retriever.config.generator.prefix ,						n_docs=__lowerCamelCase  )
     __A			,	__A			,	__A   :						int									=					(
         out['''context_input_ids'''],
         out['''context_attention_mask'''],
         out['''retrieved_doc_embeds'''],
     )
     self.assertEqual(retrieved_doc_embeds.shape ,						(2, n_docs, self.retrieval_vector_size)  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )
     self.assertIsInstance(__lowerCamelCase ,						__lowerCamelCase  )
     self.assertIsInstance(__lowerCamelCase ,						np.ndarray  )

     __A   :						str									=					retriever(
         __lowerCamelCase ,						__lowerCamelCase ,						prefix=retriever.config.generator.prefix ,						n_docs=__lowerCamelCase ,						return_tensors='''pt''' ,						)
     __A			,	__A			,	__A			,	__A   :						Dict									=					(  # noqa: F841
         out['''context_input_ids'''],
         out['''context_attention_mask'''],
         out['''retrieved_doc_embeds'''],
         out['''doc_ids'''],
     )
     self.assertEqual(retrieved_doc_embeds.shape ,						(2, n_docs, self.retrieval_vector_size)  )
     self.assertIsInstance(__lowerCamelCase ,						torch.Tensor  )
     self.assertIsInstance(__lowerCamelCase ,						torch.Tensor  )
     self.assertIsInstance(__lowerCamelCase ,						torch.Tensor  )







 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def  UpperCamelCase__(   self  ):



     '''simple docstring'''




     __A   :						int									=					self.get_dpr_ctx_encoder_tokenizer()
     __A   :						str									=					1
     __A   :						Union[str, Any]									=					self.get_dummy_custom_hf_index_retriever(from_disk=__lowerCamelCase  )
     retriever.set_ctx_encoder_tokenizer(__lowerCamelCase  )

     __A   :						Union[str, Any]									=					[[5, 7], [10, 11]]
     __A   :						Tuple									=					np.array(
         [np.ones(self.retrieval_vector_size  ), -np.ones(self.retrieval_vector_size  )] ,						dtype=np.floataa  )
     __A   :						int									=					retriever(__lowerCamelCase ,						__lowerCamelCase ,						prefix=retriever.config.generator.prefix ,						n_docs=__lowerCamelCase  )

     self.assertEqual(
         len(__lowerCamelCase  ) ,						6  )  # check whether the retriever output consist of 6 attributes including tokenized docs
     self.assertEqual(
         all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')  ) ,						__lowerCamelCase  )  # check for doc token related keys in dictionary.











"""simple docstring"""



from collections import UserDict
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_torch_available():
       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
       from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
       from ..tf_utils import stable_softmax

a_			=      logging.get_logger(__name__)






@add_end_docstrings(SCREAMING_SNAKE_CASE__					)
class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 def __init__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     requires_backends(self ,						'''vision'''  )
     self.check_model_type(
         TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
         if self.framework == '''tf'''
         else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING  )

 def __call__(   self ,						__lowerCamelCase ,						**__lowerCamelCase  ):



     '''simple docstring'''




     return super().__call__(__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Union[str, Any]									=					{}
     if "candidate_labels" in kwargs:
         __A   :						Tuple									=					kwargs['''candidate_labels''']
     if "hypothesis_template" in kwargs:
         __A   :						List[str]									=					kwargs['''hypothesis_template''']

     return preprocess_params, {}, {}

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase=None ,						__lowerCamelCase="This is a photo of {}."  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					load_image(__lowerCamelCase  )
     __A   :						Optional[int]									=					self.image_processor(images=[image] ,						return_tensors=self.framework  )
     __A   :						int									=					candidate_labels
     __A   :						int									=					[hypothesis_template.format(__lowerCamelCase  ) for x in candidate_labels]
     __A   :						Dict									=					self.tokenizer(__lowerCamelCase ,						return_tensors=self.framework ,						padding=__lowerCamelCase  )
     __A   :						int									=					[text_inputs]
     return inputs

 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					model_inputs.pop('''candidate_labels'''  )
     __A   :						str									=					model_inputs.pop('''text_inputs'''  )
     if isinstance(text_inputs[0] ,						__lowerCamelCase  ):
         __A   :						Union[str, Any]									=					text_inputs[0]
     else:
         # Batching case.
         __A   :						str									=					text_inputs[0][0]

     __A   :						List[str]									=					self.model(**__lowerCamelCase ,						**__lowerCamelCase  )

     __A   :						Dict									=					{
         '''candidate_labels''': candidate_labels,
         '''logits''': outputs.logits_per_image,
     }
     return model_outputs







 def  UpperCamelCase__(   self ,						__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						Optional[int]									=					model_outputs.pop('''candidate_labels'''  )
     __A   :						int									=					model_outputs['''logits'''][0]
     if self.framework == "pt":
         __A   :						Union[str, Any]									=					logits.softmax(dim=-1  ).squeeze(-1  )
         __A   :						Dict									=					probs.tolist()
         if not isinstance(__lowerCamelCase ,						__lowerCamelCase  ):
             __A   :						List[Any]									=					[scores]
     elif self.framework == "tf":
         __A   :						List[Any]									=					stable_softmax(__lowerCamelCase ,						axis=-1  )
         __A   :						str									=					probs.numpy().tolist()
     else:
         raise ValueError(F"""Unsupported framework: {self.framework}"""  )

     __A   :						str									=					[
         {'''score''': score, '''label''': candidate_label}
         for score, candidate_label in sorted(zip(__lowerCamelCase ,						__lowerCamelCase  ) ,						key=lambda __lowerCamelCase  : -x[0]  )
     ]
     return result




