
'''simple docstring'''






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_							: str =							logging.get_logger(__name__)

UpperCAmelCase_							: List[str] =							{
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}

class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		'mobilenet_v2'



    def __init__(						self   :							str,__A   :							Dict=3,__A   :							int=2_2_4,__A   :							List[Any]=1.0,__A   :							Any=8,__A   :							List[Any]=8,__A   :							Dict=6,__A   :							Tuple=3_2,__A   :							List[Any]=True,__A   :							Union[str, Any]=True,__A   :							int="relu6",__A   :							Tuple=True,__A   :							Optional[Any]=0.8,__A   :							List[Any]=0.02,__A   :							List[Any]=0.001,__A   :							str=2_5_5,**__A   :							Dict,):
          super().__init__(**__A  )

          if depth_multiplier <= 0:
                raise ValueError("depth_multiplier must be greater than zero."  )

          _lowerCamelCase					: List[str]		    = num_channels
          _lowerCamelCase					: Tuple		    = image_size
          _lowerCamelCase					: Optional[Any]		    = depth_multiplier
          _lowerCamelCase					: Optional[Any]		    = depth_divisible_by
          _lowerCamelCase					: Any		    = min_depth
          _lowerCamelCase					: Dict		    = expand_ratio
          _lowerCamelCase					: Tuple		    = output_stride
          _lowerCamelCase					: Tuple		    = first_layer_is_expansion
          _lowerCamelCase					: Tuple		    = finegrained_output
          _lowerCamelCase					: str		    = hidden_act
          _lowerCamelCase					: List[Any]		    = tf_padding
          _lowerCamelCase					: Dict		    = classifier_dropout_prob
          _lowerCamelCase					: Any		    = initializer_range
          _lowerCamelCase					: str		    = layer_norm_eps
          _lowerCamelCase					: Any		    = semantic_loss_ignore_index


class 			UpperCAmelCase__    (							A			):
    lowerCAmelCase_					     =		version.parse('1.11'			)



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          return OrderedDict([("pixel_values", {0: "batch"})]  )



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          if self.task == "image-classification":
                return OrderedDict([("logits", {0: "batch"})]  )
          else:
                return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]  )



    @property
    def 	lowerCamelCase_	(						self   :							Dict  ):
          return 1e-4


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   )


