

import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
  from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
  from tensorflow.keras.optimizers import Adam





class 					__lowerCAmelCase	(   tf.keras.optimizers.schedules.LearningRateSchedule				):




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = 1.0				,  lowerCAmelCase = None				,  )    ->					List[str]:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=initial_learning_rate
        _lowercase      			=warmup_steps
        _lowercase      			=power
        _lowercase      			=decay_schedule_fn
        _lowercase      			=name




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






        with tf.name_scope(self.name or 'WarmUp'   ) as name:
              # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
              # learning rate will be `global_step/num_warmup_steps * init_lr`.
              _lowercase      			=tf.cast(lowerCAmelCase				,  tf.floataa   )
              _lowercase      			=tf.cast(self.warmup_steps				,  tf.floataa   )
              _lowercase      			=global_step_float / warmup_steps_float
              _lowercase      			=self.initial_learning_rate * tf.math.pow(lowerCAmelCase				,  self.power   )
              return tf.cond(
                  global_step_float < warmup_steps_float				,  lambda: warmup_learning_rate				,  lambda: self.decay_schedule_fn(step - self.warmup_steps   )				,  name=lowerCAmelCase				,  )




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






        return {
            "initial_learning_rate": self.initial_learning_rate,
            "decay_schedule_fn": self.decay_schedule_fn,
            "warmup_steps": self.warmup_steps,
            "power": self.power,
            "name": self.name,
        }
def 	a (     A__      :  float ,  A__      :  int ,  A__      :  int ,  A__      :  float = 0.0 ,  A__      :  float = 0.9 ,  A__      :  float = 0.999 ,  A__      :  float = 1e-8 ,  A__      :  Optional[float] = None ,  A__      :  Optional[float] = None ,  A__      :  float = 0.0 ,  A__      :  float = 1.0 ,  A__      :  Optional[List[str]] = None ,  )       -> Any:





      """simple docstring"""



      _lowercase      			=tf.keras.optimizers.schedules.PolynomialDecay(
          initial_learning_rate=A__ ,  decay_steps=num_train_steps - num_warmup_steps ,  end_learning_rate=init_lr * min_lr_ratio ,  power=A__ ,  )
      if num_warmup_steps:
            _lowercase      			=WarmUp(
                initial_learning_rate=A__ ,  decay_schedule_fn=A__ ,  warmup_steps=A__ ,  )
      if weight_decay_rate > 0.0:
            _lowercase      			=AdamWeightDecay(
                learning_rate=A__ ,  weight_decay_rate=A__ ,  beta_a=A__ ,  beta_a=A__ ,  epsilon=A__ ,  clipnorm=A__ ,  global_clipnorm=A__ ,  exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] ,  include_in_weight_decay=A__ ,  )
      else:
            _lowercase      			=tf.keras.optimizers.Adam(
                learning_rate=A__ ,  beta_a=A__ ,  beta_a=A__ ,  epsilon=A__ ,  clipnorm=A__ ,  global_clipnorm=A__ ,  )
      # We return the optimizer and the LR scheduler in order to better track the
      # evolution of the LR independently of the optimizer.
      return optimizer, lr_schedule





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self				,  lowerCAmelCase = 0.001				,  lowerCAmelCase = 0.9				,  lowerCAmelCase = 0.999				,  lowerCAmelCase = 1e-7				,  lowerCAmelCase = False				,  lowerCAmelCase = 0.0				,  lowerCAmelCase = None				,  lowerCAmelCase = None				,  lowerCAmelCase = "AdamWeightDecay"				,  **lowerCAmelCase				,  )    ->					Optional[int]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  **lowerCAmelCase   )
        _lowercase      			=weight_decay_rate
        _lowercase      			=include_in_weight_decay
        _lowercase      			=exclude_from_weight_decay




  @classmethod
  def     A__	(							cls				,  lowerCAmelCase   )    ->					List[str]:
        '''simple docstring'''






        _lowercase      			={'WarmUp': WarmUp}
        return super(lowerCAmelCase				,  cls   ).from_config(lowerCAmelCase				,  custom_objects=lowerCAmelCase   )




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






        super(lowerCAmelCase				,  self   )._prepare_local(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )
        _lowercase      			=tf.constant(
            self.weight_decay_rate				,  name='adam_weight_decay_rate'   )




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






        _lowercase      			=self._do_use_weight_decay(var.name   )
        if do_decay:
              return var.assign_sub(
                  learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']				,  use_locking=self._use_locking				,  )
        return tf.no_op()




  def     A__	(							self				,  lowerCAmelCase				,  lowerCAmelCase=None				,  **lowerCAmelCase   )    ->					List[str]:
        '''simple docstring'''






        _lowercase			,			_lowercase      			=list(zip(*lowerCAmelCase   )   )
        return super(lowerCAmelCase				,  self   ).apply_gradients(zip(lowerCAmelCase				,  lowerCAmelCase   )				,  name=lowerCAmelCase				,  **lowerCAmelCase   )




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






        if apply_state is None:
              return self._decayed_lr_t[var_dtype], {}

        _lowercase      			=apply_state or {}
        _lowercase      			=apply_state.get((var_device, var_dtype)   )
        if coefficients is None:
              _lowercase      			=self._fallback_apply_state(lowerCAmelCase				,  lowerCAmelCase   )
              _lowercase      			=coefficients

        return coefficients["lr_t"], {"apply_state": apply_state}




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






        _lowercase			,			_lowercase      			=self._get_lr(var.device				,  var.dtype.base_dtype				,  lowerCAmelCase   )
        _lowercase      			=self._decay_weights_op(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )
        with tf.control_dependencies([decay]   ):
              return super(lowerCAmelCase				,  self   )._resource_apply_dense(lowerCAmelCase				,  lowerCAmelCase				,  **lowerCAmelCase   )




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






        _lowercase			,			_lowercase      			=self._get_lr(var.device				,  var.dtype.base_dtype				,  lowerCAmelCase   )
        _lowercase      			=self._decay_weights_op(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase   )
        with tf.control_dependencies([decay]   ):
              return super(lowerCAmelCase				,  self   )._resource_apply_sparse(lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase				,  **lowerCAmelCase   )




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






        _lowercase      			=super().get_config()
        config.update({'weight_decay_rate': self.weight_decay_rate}   )
        return config




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






        if self.weight_decay_rate == 0:
              return False

        if self._include_in_weight_decay:
              for r in self._include_in_weight_decay:
                    if re.search(lowerCAmelCase				,  lowerCAmelCase   ) is not None:
                          return True

        if self._exclude_from_weight_decay:
              for r in self._exclude_from_weight_decay:
                    if re.search(lowerCAmelCase				,  lowerCAmelCase   ) is not None:
                          return False
        return True





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  def __init__(							self   )    ->					Optional[int]:
        '''simple docstring'''






        _lowercase      			=[]
        _lowercase      			=None




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






        if self._accum_steps is None:
              _lowercase      			=tf.Variable(
                  tf.constant(0				,  dtype=tf.intaa   )				,  trainable=lowerCAmelCase				,  synchronization=tf.VariableSynchronization.ON_READ				,  aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,  )

        return self._accum_steps.value()




  @property
  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        if not self._gradients:
              raise ValueError('The accumulator should be called first to initialize the gradients'   )
        return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]




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






        if not self._gradients:
              _lowercase      			=self.step  # Create the step variable.
              self._gradients.extend(
                  [
                      tf.Variable(
                          tf.zeros_like(lowerCAmelCase   )				,  trainable=lowerCAmelCase				,  synchronization=tf.VariableSynchronization.ON_READ				,  aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA				,  )
                      if gradient is not None
                      else gradient
                      for gradient in gradients
                  ]   )
        if len(lowerCAmelCase   ) != len(self._gradients   ):
              raise ValueError(F'''Expected {len(self._gradients   )} gradients, but got {len(lowerCAmelCase   )}'''   )

        for accum_gradient, gradient in zip(self._gradients				,  lowerCAmelCase   ):
              if accum_gradient is not None and gradient is not None:
                    accum_gradient.assign_add(lowerCAmelCase   )

        self._accum_steps.assign_add(1   )




  def     A__	(							self   )    ->					Union[str, Any]:
        '''simple docstring'''






        if not self._gradients:
              return
        self._accum_steps.assign(0   )
        for gradient in self._gradients:
              if gradient is not None:
                    gradient.assign(tf.zeros_like(lowerCAmelCase   )   )





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   )


