

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 	a (     A__      :  Union[str, Any]	)       -> Tuple:





      """simple docstring"""



      return 1.0 / (1.0 + np.exp(-_outputs	))
def 	a (     A__      :  Optional[int]	)       -> str:





      """simple docstring"""



      _lowercase      			=np.max(_outputs ,  axis=-1 ,  keepdims=A__	)
      _lowercase      			=np.exp(_outputs - maxes	)
      return shifted_exp / shifted_exp.sum(axis=-1 ,  keepdims=A__	)





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """sigmoid"""
  _a		     =    """softmax"""
  _a		     =    """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 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    False
  _a		     =    ClassificationFunction.NONE




  def __init__(							self				,  **lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        super().__init__(**lowerCAmelCase   )

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




  def     A__	(							self				,  lowerCAmelCase=None				,  lowerCAmelCase=None				,  lowerCAmelCase=""				,  **lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=tokenizer_kwargs

        _lowercase      			={}
        if hasattr(self.model.config				,  'return_all_scores'   ) and return_all_scores is None:
              _lowercase      			=self.model.config.return_all_scores

        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ) or top_k is None:
              _lowercase      			=top_k
              _lowercase      			=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`.'				,  lowerCAmelCase				,  )
              if return_all_scores:
                    _lowercase      			=None
              else:
                    _lowercase      			=1

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

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




  def __call__(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        _lowercase      			=super().__call__(*lowerCAmelCase				,  **lowerCAmelCase   )
        # TODO try and retrieve it in a nicer way from _sanitize_parameters.
        _lowercase      			='top_k' not in kwargs
        if isinstance(args[0]				,  lowerCAmelCase   ) and _legacy:
              # This pipeline is odd, and return a list when single item is run
              return [result]
        else:
              return result




  def     A__	(							self				,  lowerCAmelCase				,  **lowerCAmelCase   )    ->					Dict[str, GenericTensor]:
        '''simple docstring'''






        _lowercase      			=self.framework
        if isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              return self.tokenizer(**lowerCAmelCase				,  return_tensors=lowerCAmelCase				,  **lowerCAmelCase   )
        elif isinstance(lowerCAmelCase				,  lowerCAmelCase   ) and len(lowerCAmelCase   ) == 1 and isinstance(inputs[0]				,  lowerCAmelCase   ) 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=lowerCAmelCase				,  **lowerCAmelCase   )
        elif isinstance(lowerCAmelCase				,  lowerCAmelCase   ):
              # 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(lowerCAmelCase				,  return_tensors=lowerCAmelCase				,  **lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        return self.model(**lowerCAmelCase   )




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  lowerCAmelCase=1				,  lowerCAmelCase=True   )    ->					str:
        '''simple docstring'''






        if function_to_apply is None:
              if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
                    _lowercase      			=ClassificationFunction.SIGMOID
              elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
                    _lowercase      			=ClassificationFunction.SOFTMAX
              elif hasattr(self.model.config				,  'function_to_apply'   ) and function_to_apply is None:
                    _lowercase      			=self.model.config.function_to_apply
              else:
                    _lowercase      			=ClassificationFunction.NONE

        _lowercase      			=model_outputs['logits'][0]
        _lowercase      			=outputs.numpy()

        if function_to_apply == ClassificationFunction.SIGMOID:
              _lowercase      			=sigmoid(lowerCAmelCase   )
        elif function_to_apply == ClassificationFunction.SOFTMAX:
              _lowercase      			=softmax(lowerCAmelCase   )
        elif function_to_apply == ClassificationFunction.NONE:
              _lowercase      			=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()}

        _lowercase      			=[
            {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(lowerCAmelCase   )
        ]
        if not _legacy:
              dict_scores.sort(key=lambda lowerCAmelCase   : x["score"]				,  reverse=lowerCAmelCase   )
              if top_k is not None:
                    _lowercase      			=dict_scores[:top_k]
        return dict_scores





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor





@require_vision
class 					__lowerCAmelCase	(   unittest.TestCase				):




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=tempfile.mkdtemp()

        _lowercase      			=[
            '[UNK]',
            '[CLS]',
            '[SEP]',
            '[PAD]',
            '[MASK]',
            '的',
            '价',
            '格',
            '是',
            '15',
            '便',
            'alex',
            '##andra',
            '，',
            '。',
            '-',
            't',
            'shirt',
        ]
        _lowercase      			=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]   )   )

        _lowercase      			={
            'do_resize': True,
            'size': {'height': 224, 'width': 224},
            'do_center_crop': True,
            'crop_size': {'height': 18, 'width': 18},
            'do_normalize': True,
            'image_mean': [0.48145466, 0.4578275, 0.40821073],
            'image_std': [0.26862954, 0.26130258, 0.27577711],
            'do_convert_rgb': True,
        }
        _lowercase      			=os.path.join(self.tmpdirname				,  lowerCAmelCase   )
        with open(self.image_processor_file				,  'w'				,  encoding='utf-8'   ) as fp:
              json.dump(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






        return BertTokenizer.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Optional[Any]:
        '''simple docstring'''






        return BertTokenizerFast.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self				,  **lowerCAmelCase   )    ->					Any:
        '''simple docstring'''






        return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname				,  **lowerCAmelCase   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        shutil.rmtree(self.tmpdirname   )




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        _lowercase      			=[np.random.randint(255				,  size=(3, 30, 400)				,  dtype=np.uinta   )]

        _lowercase      			=[Image.fromarray(np.moveaxis(lowerCAmelCase				,  0				,  -1   )   ) for x in image_inputs]

        return image_inputs




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_tokenizer()
        _lowercase      			=self.get_rust_tokenizer()
        _lowercase      			=self.get_image_processor()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_slow.save_pretrained(self.tmpdirname   )
        _lowercase      			=ChineseCLIPProcessor.from_pretrained(self.tmpdirname				,  use_fast=lowerCAmelCase   )

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )
        processor_fast.save_pretrained(self.tmpdirname   )
        _lowercase      			=ChineseCLIPProcessor.from_pretrained(self.tmpdirname   )

        self.assertEqual(processor_slow.tokenizer.get_vocab()				,  tokenizer_slow.get_vocab()   )
        self.assertEqual(processor_fast.tokenizer.get_vocab()				,  tokenizer_fast.get_vocab()   )
        self.assertEqual(tokenizer_slow.get_vocab()				,  tokenizer_fast.get_vocab()   )
        self.assertIsInstance(processor_slow.tokenizer				,  lowerCAmelCase   )
        self.assertIsInstance(processor_fast.tokenizer				,  lowerCAmelCase   )

        self.assertEqual(processor_slow.image_processor.to_json_string()				,  image_processor.to_json_string()   )
        self.assertEqual(processor_fast.image_processor.to_json_string()				,  image_processor.to_json_string()   )
        self.assertIsInstance(processor_slow.image_processor				,  lowerCAmelCase   )
        self.assertIsInstance(processor_fast.image_processor				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=ChineseCLIPProcessor(tokenizer=self.get_tokenizer()				,  image_processor=self.get_image_processor()   )
        processor.save_pretrained(self.tmpdirname   )

        _lowercase      			=self.get_tokenizer(cls_token='(CLS)'				,  sep_token='(SEP)'   )
        _lowercase      			=self.get_image_processor(do_normalize=lowerCAmelCase   )

        _lowercase      			=ChineseCLIPProcessor.from_pretrained(
            self.tmpdirname				,  cls_token='(CLS)'				,  sep_token='(SEP)'				,  do_normalize=lowerCAmelCase   )

        self.assertEqual(processor.tokenizer.get_vocab()				,  tokenizer_add_kwargs.get_vocab()   )
        self.assertIsInstance(processor.tokenizer				,  lowerCAmelCase   )

        self.assertEqual(processor.image_processor.to_json_string()				,  image_processor_add_kwargs.to_json_string()   )
        self.assertIsInstance(processor.image_processor				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=image_processor(lowerCAmelCase				,  return_tensors='np'   )
        _lowercase      			=processor(images=lowerCAmelCase				,  return_tensors='np'   )

        for key in input_feat_extract.keys():
              self.assertAlmostEqual(input_feat_extract[key].sum()				,  input_processor[key].sum()				,  delta=1e-2   )




  def     A__	(							self   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'

        _lowercase      			=processor(text=lowerCAmelCase   )

        _lowercase      			=tokenizer(lowerCAmelCase   )

        for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]				,  encoded_processor[key]   )




  def     A__	(							self   )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'
        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=processor(text=lowerCAmelCase				,  images=lowerCAmelCase   )

        self.assertListEqual(list(inputs.keys()   )				,  ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']   )

        # test if it raises when no input is passed
        with pytest.raises(lowerCAmelCase   ):
              processor()




  def     A__	(							self   )    ->					Optional[Any]:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			=[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        _lowercase      			=processor.batch_decode(lowerCAmelCase   )
        _lowercase      			=tokenizer.batch_decode(lowerCAmelCase   )

        self.assertListEqual(lowerCAmelCase				,  lowerCAmelCase   )




  def     A__	(							self   )    ->					str:
        '''simple docstring'''






        _lowercase      			=self.get_image_processor()
        _lowercase      			=self.get_tokenizer()

        _lowercase      			=ChineseCLIPProcessor(tokenizer=lowerCAmelCase				,  image_processor=lowerCAmelCase   )

        _lowercase      			='Alexandra，T-shirt的价格是15便士。'
        _lowercase      			=self.prepare_image_inputs()

        _lowercase      			=processor(text=lowerCAmelCase				,  images=lowerCAmelCase   )

        self.assertListEqual(list(inputs.keys()   )				,  processor.model_input_names   )


