





import pickle

import numpy as np
from matplotlib import pyplot as plt


class     A     :
			'''simple docstring'''




			def __init__(     self  :       Any				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       Any=0.2				,      __lowerCAmelCase  :       Any=0.2  )      ->      str:







					"""simple docstring"""






					A__     =  bp_numa
					A__     =  bp_numa
					A__     =  bp_numa
					A__     =  conva_get[:2]
					A__     =  conva_get[2]
					A__     =  size_pa
					A__     =  rate_w
					A__     =  rate_t
					A__     =  [
					    np.mat(-1 * np.random.rand(self.conva[0]				,      self.conva[0]  ) + 0.5  )
					    for i in range(self.conva[1]  )
					]
					A__     =  np.mat(-1 * np.random.rand(self.num_bpa				,      self.num_bpa  ) + 0.5  )
					A__     =  np.mat(-1 * np.random.rand(self.num_bpa				,      self.num_bpa  ) + 0.5  )
					A__     =  -2 * np.random.rand(self.conva[1]  ) + 1
					A__     =  -2 * np.random.rand(self.num_bpa  ) + 1
					A__     =  -2 * np.random.rand(self.num_bpa  ) + 1






			def   a_			(     self  :       str				,      __lowerCAmelCase  :       Optional[Any]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  {
					    """num_bp1""": self.num_bpa,
					    """num_bp2""": self.num_bpa,
					    """num_bp3""": self.num_bpa,
					    """conv1""": self.conva,
					    """step_conv1""": self.step_conva,
					    """size_pooling1""": self.size_poolinga,
					    """rate_weight""": self.rate_weight,
					    """rate_thre""": self.rate_thre,
					    """w_conv1""": self.w_conva,
					    """wkj""": self.wkj,
					    """vji""": self.vji,
					    """thre_conv1""": self.thre_conva,
					    """thre_bp2""": self.thre_bpa,
					    """thre_bp3""": self.thre_bpa,
					}
					with open(__lowerCAmelCase				,      """wb"""  ) as f:
							pickle.dump(__lowerCAmelCase				,      __lowerCAmelCase  )

					print(f'Model saved： {save_path}'  )






			@classmethod
			def   a_			(     cls  :       List[str]				,      __lowerCAmelCase  :       int  )      ->      Tuple:







					"""simple docstring"""






					with open(__lowerCAmelCase				,      """rb"""  ) as f:
							A__     =  pickle.load(__lowerCAmelCase  )  # noqa: S301

					A__     =  model_dic.get("""conv1"""  )
					conv_get.append(model_dic.get("""step_conv1"""  )  )
					A__     =  model_dic.get("""size_pooling1"""  )
					A__     =  model_dic.get("""num_bp1"""  )
					A__     =  model_dic.get("""num_bp2"""  )
					A__     =  model_dic.get("""num_bp3"""  )
					A__     =  model_dic.get("""rate_weight"""  )
					A__     =  model_dic.get("""rate_thre"""  )
					# create model instance
					A__     =  CNN(__lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase				,      __lowerCAmelCase  )
					# modify model parameter
					A__     =  model_dic.get("""w_conv1"""  )
					A__     =  model_dic.get("""wkj"""  )
					A__     =  model_dic.get("""vji"""  )
					A__     =  model_dic.get("""thre_conv1"""  )
					A__     =  model_dic.get("""thre_bp2"""  )
					A__     =  model_dic.get("""thre_bp3"""  )
					return conv_ins






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       int  )      ->      int:







					"""simple docstring"""






					return 1 / (1 + np.exp(-1 * x  ))






			def   a_			(     self  :       Optional[int]				,      __lowerCAmelCase  :       Any  )      ->      Optional[Any]:







					"""simple docstring"""






					return round(__lowerCAmelCase				,      3  )






			def   a_			(     self  :       str				,      __lowerCAmelCase  :       Optional[int]				,      __lowerCAmelCase  :       str				,      __lowerCAmelCase  :       Optional[int]				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       int  )      ->      List[str]:







					"""simple docstring"""






					A__     =  convs[0]
					A__     =  convs[1]
					A__     =  np.shape(__lowerCAmelCase  )[0]
					# get the data slice of original image data, data_focus
					A__     =  []
					for i_focus in range(0				,      size_data - size_conv + 1				,      __lowerCAmelCase  ):
							for j_focus in range(0				,      size_data - size_conv + 1				,      __lowerCAmelCase  ):
									A__     =  data[
									    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
									]
									data_focus.append(__lowerCAmelCase  )
        # calculate the feature map of every single kernel, and saved as list of matrix
					A__     =  []
					A__     =  int((size_data - size_conv) / conv_step + 1  )
					for i_map in range(__lowerCAmelCase  ):
							A__     =  []
							for i_focus in range(len(__lowerCAmelCase  )  ):
									A__     =  (
									    np.sum(np.multiply(data_focus[i_focus]				,      w_convs[i_map]  )  )
									    - thre_convs[i_map]
									)
									featuremap.append(self.sig(__lowerCAmelCase  )  )
							A__     =  np.asmatrix(__lowerCAmelCase  ).reshape(
							    __lowerCAmelCase				,      __lowerCAmelCase  )
							data_featuremap.append(__lowerCAmelCase  )

					# expanding the data slice to One dimenssion
					A__     =  []
					for each_focus in data_focus:
							focusa_list.extend(self.Expand_Mat(__lowerCAmelCase  )  )
					A__     =  np.asarray(__lowerCAmelCase  )
					return focus_list, data_featuremap






			def   a_			(     self  :       List[Any]				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]="average_pool"  )      ->      Dict:







					"""simple docstring"""






					A__     =  len(featuremaps[0]  )
					A__     =  int(size_map / size_pooling  )
					A__     =  []
					for i_map in range(len(__lowerCAmelCase  )  ):
							A__     =  featuremaps[i_map]
							A__     =  []
							for i_focus in range(0				,      __lowerCAmelCase				,      __lowerCAmelCase  ):
									for j_focus in range(0				,      __lowerCAmelCase				,      __lowerCAmelCase  ):
											A__     =  feature_map[
											    i_focus : i_focus + size_pooling,
											    j_focus : j_focus + size_pooling,
											]
											if pooling_type == "average_pool":
													# average pooling
													map_pooled.append(np.average(__lowerCAmelCase  )  )
											elif pooling_type == "max_pooling":
													# max pooling
													map_pooled.append(np.max(__lowerCAmelCase  )  )
							A__     =  np.asmatrix(__lowerCAmelCase  ).reshape(__lowerCAmelCase				,      __lowerCAmelCase  )
							featuremap_pooled.append(__lowerCAmelCase  )
					return featuremap_pooled






			def   a_			(     self  :       Tuple				,      __lowerCAmelCase  :       Union[str, Any]  )      ->      List[Any]:







					"""simple docstring"""






					A__     =  []
					for i in range(len(__lowerCAmelCase  )  ):
							A__     =  np.shape(data[i]  )
							A__     =  data[i].reshape(1				,      shapes[0] * shapes[1]  )
							A__     =  data_listed.getA().tolist()[0]
							data_expanded.extend(__lowerCAmelCase  )
					A__     =  np.asarray(__lowerCAmelCase  )
					return data_expanded






			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  np.asarray(__lowerCAmelCase  )
					A__     =  np.shape(__lowerCAmelCase  )
					A__     =  data_mat.reshape(1				,      shapes[0] * shapes[1]  )
					return data_expanded






			def   a_			(     self  :       Dict				,      __lowerCAmelCase  :       Any				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       Dict				,      __lowerCAmelCase  :       Dict  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  []
					A__     =  0
					for i_map in range(__lowerCAmelCase  ):
							A__     =  np.ones((size_map, size_map)  )
							for i in range(0				,      __lowerCAmelCase				,      __lowerCAmelCase  ):
									for j in range(0				,      __lowerCAmelCase				,      __lowerCAmelCase  ):
											A__     =  pd_pool[
											    i_pool
											]
											A__     =  i_pool + 1
							A__     =  np.multiply(
							    __lowerCAmelCase				,      np.multiply(out_map[i_map]				,      (1 - out_map[i_map])  )  )
							pd_all.append(__lowerCAmelCase  )
					return pd_all






			def   a_			(     self  :       Any				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       List[str]				,      __lowerCAmelCase  :       int				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       Union[str, Any]				,      __lowerCAmelCase  :       Optional[Any]=bool  )      ->      Union[str, Any]:







					"""simple docstring"""






					print("""----------------------Start Training-------------------------"""  )
					print((""" - - Shape: Train_Data  """, np.shape(__lowerCAmelCase  ))  )
					print((""" - - Shape: Teach_Data  """, np.shape(__lowerCAmelCase  ))  )
					A__     =  0
					A__     =  []
					A__     =  1_00_00
					while rp < n_repeat and mse >= error_accuracy:
							A__     =  0
							print(f'-------------Learning Time {rp}--------------'  )
							for p in range(len(__lowerCAmelCase  )  ):
									# print('------------Learning Image: %d--------------'%p)
									A__     =  np.asmatrix(datas_train[p]  )
									A__     =  np.asarray(datas_teach[p]  )
									A__ ,     A__     =  self.convolute(
									    __lowerCAmelCase				,      self.conva				,      self.w_conva				,      self.thre_conva				,      conv_step=self.step_conva				,      )
									A__     =  self.pooling(__lowerCAmelCase				,      self.size_poolinga  )
									A__     =  np.shape(__lowerCAmelCase  )
									A__     =  self._expand(__lowerCAmelCase  )
									A__     =  data_bp_input

									A__     =  np.dot(__lowerCAmelCase				,      self.vji.T  ) - self.thre_bpa
									A__     =  self.sig(__lowerCAmelCase  )
									A__     =  np.dot(__lowerCAmelCase				,      self.wkj.T  ) - self.thre_bpa
									A__     =  self.sig(__lowerCAmelCase  )

									# --------------Model Leaning ------------------------
									# calculate error and gradient---------------
									A__     =  np.multiply(
									    (data_teach - bp_outa)				,      np.multiply(__lowerCAmelCase				,      (1 - bp_outa)  )  )
									A__     =  np.multiply(
									    np.dot(__lowerCAmelCase				,      self.wkj  )				,      np.multiply(__lowerCAmelCase				,      (1 - bp_outa)  )  )
									A__     =  np.dot(__lowerCAmelCase				,      self.vji  )

									A__     =  pd_i_all / (self.size_poolinga * self.size_poolinga)
									A__     =  pd_conva_pooled.T.getA().tolist()
									A__     =  self._calculate_gradient_from_pool(
									    __lowerCAmelCase				,      __lowerCAmelCase				,      shape_featuremapa[0]				,      shape_featuremapa[1]				,      self.size_poolinga				,      )
									# weight and threshold learning process---------
									# convolution layer
									for k_conv in range(self.conva[1]  ):
											A__     =  self._expand_mat(pd_conva_all[k_conv]  )
											A__     =  self.rate_weight * np.dot(__lowerCAmelCase				,      __lowerCAmelCase  )

											A__     =  self.w_conva[k_conv] + delta_w.reshape(
											    (self.conva[0], self.conva[0])  )

											A__     =  (
											    self.thre_conva[k_conv]
											    - np.sum(pd_conva_all[k_conv]  ) * self.rate_thre
											)
									# all connected layer
									A__     =  self.wkj + pd_k_all.T * bp_outa * self.rate_weight
									A__     =  self.vji + pd_j_all.T * bp_outa * self.rate_weight
									A__     =  self.thre_bpa - pd_k_all * self.rate_thre
									A__     =  self.thre_bpa - pd_j_all * self.rate_thre
									# calculate the sum error of all single image
									A__     =  np.sum(abs(data_teach - bp_outa  )  )
									error_count += errors
									# print('   ----Teach      ',data_teach)
									# print('   ----BP_output  ',bp_out3)
							A__     =  rp + 1
							A__     =  error_count / patterns
							all_mse.append(__lowerCAmelCase  )

					def draw_error():
							A__     =  [error_accuracy for i in range(int(n_repeat * 1.2  )  )]
							plt.plot(__lowerCAmelCase				,      """+-"""  )
							plt.plot(__lowerCAmelCase				,      """r--"""  )
							plt.xlabel("""Learning Times"""  )
							plt.ylabel("""All_mse"""  )
							plt.grid(__lowerCAmelCase				,      alpha=0.5  )
							plt.show()

					print("""------------------Training Complished---------------------"""  )
					print((""" - - Training epoch: """, rp, f'     - - Mse: {mse:.6f}')  )
					if draw_e:
							draw_error()
					return mse






			def   a_			(     self  :       Union[str, Any]				,      __lowerCAmelCase  :       Union[str, Any]  )      ->      Dict:







					"""simple docstring"""






					A__     =  []
					print("""-------------------Start Testing-------------------------"""  )
					print((""" - - Shape: Test_Data  """, np.shape(__lowerCAmelCase  ))  )
					for p in range(len(__lowerCAmelCase  )  ):
							A__     =  np.asmatrix(datas_test[p]  )
							A__ ,     A__     =  self.convolute(
							    __lowerCAmelCase				,      self.conva				,      self.w_conva				,      self.thre_conva				,      conv_step=self.step_conva				,      )
							A__     =  self.pooling(__lowerCAmelCase				,      self.size_poolinga  )
							A__     =  self._expand(__lowerCAmelCase  )

							A__     =  data_bp_input
							A__     =  bp_outa * self.vji.T - self.thre_bpa
							A__     =  self.sig(__lowerCAmelCase  )
							A__     =  bp_outa * self.wkj.T - self.thre_bpa
							A__     =  self.sig(__lowerCAmelCase  )
							produce_out.extend(bp_outa.getA().tolist()  )
					A__     =  [list(map(self.do_round				,      __lowerCAmelCase  )  ) for each in produce_out]
					return np.asarray(__lowerCAmelCase  )







			def   a_			(     self  :       List[str]				,      __lowerCAmelCase  :       str  )      ->      Dict:







					"""simple docstring"""






					A__     =  np.asmatrix(__lowerCAmelCase  )
					A__ ,     A__     =  self.convolute(
					    __lowerCAmelCase				,      self.conva				,      self.w_conva				,      self.thre_conva				,      conv_step=self.step_conva				,      )
					A__     =  self.pooling(__lowerCAmelCase				,      self.size_poolinga  )

					return data_conveda, data_pooleda


if __name__ == "__main__":
					pass










import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import CLIPImageProcessor, CLIPProcessor


@require_vision
class     A     (unittest.TestCase       ):
			'''simple docstring'''




			def   a_			(     self  :       Optional[int]  )      ->      Dict:







					"""simple docstring"""






					A__     =  tempfile.mkdtemp()

					# fmt: off
					A__     =  ["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
					# fmt: on
					A__     =  dict(zip(__lowerCAmelCase				,      range(len(__lowerCAmelCase  )  )  )  )
					A__     =  ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
					A__     =  {"""unk_token""": """<unk>"""}

					A__     =  os.path.join(self.tmpdirname				,      VOCAB_FILES_NAMES["""vocab_file"""]  )
					A__     =  os.path.join(self.tmpdirname				,      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  )  )

					A__     =  {
					    """do_resize""": True,
					    """size""": 20,
					    """do_center_crop""": True,
					    """crop_size""": 18,
					    """do_normalize""": True,
					    """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
					    """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
					}
					A__     =  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  :       str				,      **__lowerCAmelCase  :       Optional[int]  )      ->      str:







					"""simple docstring"""






					return CLIPTokenizer.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Dict				,      **__lowerCAmelCase  :       List[Any]  )      ->      Optional[int]:







					"""simple docstring"""






					return CLIPTokenizerFast.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       List[Any]				,      **__lowerCAmelCase  :       List[Any]  )      ->      Tuple:







					"""simple docstring"""






					return CLIPImageProcessor.from_pretrained(self.tmpdirname				,      **__lowerCAmelCase  )






			def   a_			(     self  :       Optional[Any]  )      ->      str:







					"""simple docstring"""






					shutil.rmtree(self.tmpdirname  )






			def   a_			(     self  :       int  )      ->      Tuple:







					"""simple docstring"""






					A__     =  [np.random.randint(2_55				,      size=(3, 30, 4_00)				,      dtype=np.uinta  )]

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

					return image_inputs






			def   a_			(     self  :       List[str]  )      ->      Optional[int]:







					"""simple docstring"""






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

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )
					processor_slow.save_pretrained(self.tmpdirname  )
					A__     =  CLIPProcessor.from_pretrained(self.tmpdirname				,      use_fast=__lowerCAmelCase  )

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )
					processor_fast.save_pretrained(self.tmpdirname  )
					A__     =  CLIPProcessor.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  :       int  )      ->      Any:







					"""simple docstring"""






					A__     =  CLIPProcessor(tokenizer=self.get_tokenizer()				,      image_processor=self.get_image_processor()  )
					processor.save_pretrained(self.tmpdirname  )

					A__     =  self.get_tokenizer(bos_token="""(BOS)"""				,      eos_token="""(EOS)"""  )
					A__     =  self.get_image_processor(do_normalize=__lowerCAmelCase				,      padding_value=1.0  )

					A__     =  CLIPProcessor.from_pretrained(
					    self.tmpdirname				,      bos_token="""(BOS)"""				,      eos_token="""(EOS)"""				,      do_normalize=__lowerCAmelCase				,      padding_value=1.0  )

					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[Any]  )      ->      Union[str, Any]:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  self.prepare_image_inputs()

					A__     =  image_processor(__lowerCAmelCase				,      return_tensors="""np"""  )
					A__     =  processor(images=__lowerCAmelCase				,      return_tensors="""np"""  )

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






			def   a_			(     self  :       Union[str, Any]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  """lower newer"""

					A__     =  processor(text=__lowerCAmelCase  )

					A__     =  tokenizer(__lowerCAmelCase  )

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






			def   a_			(     self  :       str  )      ->      Optional[Any]:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  """lower newer"""
					A__     =  self.prepare_image_inputs()

					A__     =  processor(text=__lowerCAmelCase				,      images=__lowerCAmelCase  )

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

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






			def   a_			(     self  :       Optional[int]  )      ->      Tuple:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

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

					A__     =  processor.batch_decode(__lowerCAmelCase  )
					A__     =  tokenizer.batch_decode(__lowerCAmelCase  )

					self.assertListEqual(__lowerCAmelCase				,      __lowerCAmelCase  )







			def   a_			(     self  :       Dict  )      ->      str:







					"""simple docstring"""






					A__     =  self.get_image_processor()
					A__     =  self.get_tokenizer()

					A__     =  CLIPProcessor(tokenizer=__lowerCAmelCase				,      image_processor=__lowerCAmelCase  )

					A__     =  """lower newer"""
					A__     =  self.prepare_image_inputs()

					A__     =  processor(text=__lowerCAmelCase				,      images=__lowerCAmelCase  )

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



