'''simple docstring'''

import os
import unittest

from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin



@require_tokenizers
class       UpperCAmelCase  (						a__   ,						unittest.TestCase ):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE 		=   FunnelTokenizer
       SCREAMING_SNAKE_CASE 		=   FunnelTokenizerFast
       SCREAMING_SNAKE_CASE 		=   True
       SCREAMING_SNAKE_CASE 		=   True

       def _lowerCAmelCase(						self			)  ->  Tuple:
        super().setUp()

        lowercase__    :   List[Any]						= [
            '''<unk>''',
            '''<cls>''',
            '''<sep>''',
            '''want''',
            '''##want''',
            '''##ed''',
            '''wa''',
            '''un''',
            '''runn''',
            '''##ing''',
            ''',''',
            '''low''',
            '''lowest''',
        ]
        lowercase__    :   Union[str, Any]						= os.path.join(self.tmpdirname     ,       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]			)			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        return FunnelTokenizer.from_pretrained(self.tmpdirname     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Any:
        return FunnelTokenizerFast.from_pretrained(self.tmpdirname     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  int:
        lowercase__    :   Tuple						= '''UNwant\u00E9d,running'''
        lowercase__    :   Union[str, Any]						= '''unwanted, running'''
        return input_text, output_text

       def _lowerCAmelCase(						self			)  ->  Dict:
        lowercase__    :   List[Any]						= self.tokenizer_class(self.vocab_file			)

        lowercase__    :   Optional[Any]						= tokenizer.tokenize('''UNwant\u00E9d,running'''			)
        self.assertListEqual(__lowerCAmelCase     ,       ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing''']			)
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase			)     ,       [7, 4, 5, 10, 8, 9]			)


       def _lowerCAmelCase(						self			)  ->  str:
        lowercase__    :   int						= self.get_tokenizers(do_lower_case=__lowerCAmelCase			)
        for tokenizer in tokenizers:
         lowercase__    :   Optional[int]						= tokenizer('''UNwant\u00E9d,running'''			)
         lowercase__    :   int						= len(inputs['''input_ids''']			) - 1
         self.assertListEqual(inputs['''token_type_ids''']     ,       [2] + [0] * sentence_len			)

         lowercase__    :   str						= tokenizer('''UNwant\u00E9d,running'''     ,       '''UNwant\u00E9d,running'''			)
         self.assertListEqual(inputs['''token_type_ids''']     ,       [2] + [0] * sentence_len + [1] * sentence_len			)


'''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:			Optional[Any]	       =       logging.get_logger(__name__)



@add_end_docstrings(a__ )
class       UpperCAmelCase  (						a__ ):





       '''simple docstring'''



       def __init__(						self     ,       **__lowerCAmelCase			)  ->  int:
        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			)  ->  List[str]:
        return super().__call__(__lowerCAmelCase     ,       **__lowerCAmelCase			)

       def _lowerCAmelCase(						self     ,       **__lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   str						= {}
        if "candidate_labels" in kwargs:
         lowercase__    :   str						= kwargs['''candidate_labels''']
        if "hypothesis_template" in kwargs:
         lowercase__    :   str						= kwargs['''hypothesis_template''']

        return preprocess_params, {}, {}

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase     ,       __lowerCAmelCase=None     ,       __lowerCAmelCase="This is a photo of {}."			)  ->  Any:
        lowercase__    :   Union[str, Any]						= load_image(__lowerCAmelCase			)
        lowercase__    :   List[str]						= self.image_processor(images=[image]     ,       return_tensors=self.framework			)
        lowercase__    :   Union[str, Any]						= candidate_labels
        lowercase__    :   int						= [hypothesis_template.format(__lowerCAmelCase			) for x in candidate_labels]
        lowercase__    :   Any						= self.tokenizer(__lowerCAmelCase     ,       return_tensors=self.framework     ,       padding=__lowerCAmelCase			)
        lowercase__    :   Any						= [text_inputs]
        return inputs

       def _lowerCAmelCase(						self     ,       __lowerCAmelCase			)  ->  Optional[Any]:
        lowercase__    :   Any						= model_inputs.pop('''candidate_labels'''			)
        lowercase__    :   int						= model_inputs.pop('''text_inputs'''			)
        if isinstance(text_inputs[0]     ,       __lowerCAmelCase			):
         lowercase__    :   Union[str, Any]						= text_inputs[0]
        else:
         # Batching case.
         lowercase__    :   Optional[Any]						= text_inputs[0][0]

        lowercase__    :   Any						= self.model(**__lowerCAmelCase     ,       **__lowerCAmelCase			)

        lowercase__    :   Any						= {
            '''candidate_labels''': candidate_labels,
            '''logits''': outputs.logits_per_image,
        }
        return model_outputs


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

        lowercase__    :   Optional[int]						= [
            {'''score''': score, '''label''': candidate_label}
            for score, candidate_label in sorted(zip(__lowerCAmelCase     ,       __lowerCAmelCase			)     ,       key=lambda __lowerCAmelCase			: -x[0]			)
        ]
        return result

