

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
       from transformers import (
           FlaxBertModel,
           FlaxCLIPVisionModel,
           FlaxVisionTextDualEncoderModel,
           FlaxViTModel,
           VisionTextDualEncoderConfig,
           VisionTextDualEncoderProcessor,
       )
       from transformers.modeling_flax_pytorch_utils import (
           convert_pytorch_state_dict_to_flax,
           load_flax_weights_in_pytorch_model,
       )


if is_torch_available():
       import torch

       from transformers import VisionTextDualEncoderModel

if is_vision_available():
       from PIL import Image


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Union[str, Any]       ):





    """simple docstring"""


    if isinstance(_SCREAMING_SNAKE_CASE ,   collections.abc.Iterable       ):
        return x
    return (x, x)



@require_flax
class       __snake_case  :

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						np.abs((a - b)).max()
      self.assertLessEqual(_A						,		_A						,		f"""Difference between torch and flax is {diff} (>= {tol}).""")

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderConfig.from_vision_text_configs(_A						,		_A)

      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel(_A)

      SCREAMING_SNAKE_CASE_									=						model(input_ids=_A						,		pixel_values=_A						,		attention_mask=_A)

      self.assertEqual(output['text_embeds'].shape						,		(input_ids.shape[0], config.projection_dim))
      self.assertEqual(output['image_embeds'].shape						,		(pixel_values.shape[0], config.projection_dim))

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_vision_text_model(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						{'vision_model': vision_model, 'text_model': text_model}
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

      SCREAMING_SNAKE_CASE_									=						model(input_ids=_A						,		pixel_values=_A						,		attention_mask=_A)

      self.assertEqual(output['text_embeds'].shape						,		(input_ids.shape[0], model.config.projection_dim))
      self.assertEqual(output['image_embeds'].shape						,		(pixel_values.shape[0], model.config.projection_dim))

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_vision_text_model(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						{'vision_model': vision_model, 'text_model': text_model}
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

      SCREAMING_SNAKE_CASE_									=						model(input_ids=_A						,		pixel_values=_A						,		attention_mask=_A)
      SCREAMING_SNAKE_CASE_									=						output[0]

      with tempfile.TemporaryDirectory() as tmpdirname:
          model.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_pretrained(_A)

          SCREAMING_SNAKE_CASE_									=						model(input_ids=_A						,		pixel_values=_A						,		attention_mask=_A)
          SCREAMING_SNAKE_CASE_									=						after_output[0]
          SCREAMING_SNAKE_CASE_									=						np.amax(np.abs(out_a - out_a))
          self.assertLessEqual(_A						,		1E-3)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A=None						,		**_A):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_vision_text_model(_A						,		_A)
      SCREAMING_SNAKE_CASE_									=						{'vision_model': vision_model, 'text_model': text_model}
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_A)

      SCREAMING_SNAKE_CASE_									=						model(
          input_ids=_A						,		pixel_values=_A						,		attention_mask=_A						,		output_attentions=_A)

      SCREAMING_SNAKE_CASE_									=						output.vision_model_output.attentions
      self.assertEqual(len(_A)						,		vision_config.num_hidden_layers)

      # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
      SCREAMING_SNAKE_CASE_									=						to_atuple(vision_model.config.image_size)
      SCREAMING_SNAKE_CASE_									=						to_atuple(vision_model.config.patch_size)
      SCREAMING_SNAKE_CASE_									=						(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
      SCREAMING_SNAKE_CASE_									=						num_patches + 1
      self.assertEqual(vision_attentions[0].shape[-3:]						,		(vision_config.num_attention_heads, seq_len, seq_len))

      SCREAMING_SNAKE_CASE_									=						output.text_model_output.attentions
      self.assertEqual(len(_A)						,		text_config.num_hidden_layers)

      self.assertEqual(
          text_attentions[0].shape[-3:]						,		(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])						,		)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      pt_model.to(_A)
      pt_model.eval()

      # prepare inputs
      SCREAMING_SNAKE_CASE_									=						inputs_dict
      SCREAMING_SNAKE_CASE_									=						{k: torch.tensor(v.tolist()) for k, v in flax_inputs.items()}

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_									=						pt_model(**_A).to_tuple()

      SCREAMING_SNAKE_CASE_									=						fx_model(**_A).to_tuple()
      self.assertEqual(len(_A)						,		len(_A)						,		'Output lengths differ between Flax and PyTorch')
      for fx_output, pt_output in zip(fx_outputs[:4]						,		pt_outputs[:4]):
          self.assert_almost_equals(_A						,		pt_output.numpy()						,		4E-2)

      # PT -> Flax
      with tempfile.TemporaryDirectory() as tmpdirname:
          pt_model.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_pretrained(_A						,		from_pt=_A)

      SCREAMING_SNAKE_CASE_									=						fx_model_loaded(**_A).to_tuple()
      self.assertEqual(len(_A)						,		len(_A)						,		'Output lengths differ between Flax and PyTorch')
      for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]						,		pt_outputs[:4]):
          self.assert_almost_equals(_A						,		pt_output.numpy()						,		4E-2)

      # Flax -> PT
      with tempfile.TemporaryDirectory() as tmpdirname:
          fx_model.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderModel.from_pretrained(_A						,		from_flax=_A)

      pt_model_loaded.to(_A)
      pt_model_loaded.eval()

      with torch.no_grad():
          SCREAMING_SNAKE_CASE_									=						pt_model_loaded(**_A).to_tuple()

      self.assertEqual(len(_A)						,		len(_A)						,		'Output lengths differ between Flax and PyTorch')
      for fx_output, pt_output_loaded in zip(fx_outputs[:4]						,		pt_outputs_loaded[:4]):
          self.assert_almost_equals(_A						,		pt_output_loaded.numpy()						,		4E-2)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderConfig.from_vision_text_configs(_A						,		_A)

      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel(_A)

      SCREAMING_SNAKE_CASE_									=						convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,		_A)
      SCREAMING_SNAKE_CASE_									=						fx_state

      self.check_pt_flax_equivalence(_A						,		_A						,		_A)

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderConfig.from_vision_text_configs(_A						,		_A)

      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel(_A)

      SCREAMING_SNAKE_CASE_									=						load_flax_weights_in_pytorch_model(_A						,		fx_model.params)

      self.check_pt_flax_equivalence(_A						,		_A						,		_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_model_from_pretrained_configs(**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_vision_text_dual_encoder_from_pretrained(**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_save_load(**_A)

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      self.check_vision_text_output_attention(**_A)

  @is_pt_flax_cross_test
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_									=						config_inputs_dict.pop('vision_config')
      SCREAMING_SNAKE_CASE_									=						config_inputs_dict.pop('text_config')

      SCREAMING_SNAKE_CASE_									=						config_inputs_dict

      self.check_equivalence_pt_to_flax(_A						,		_A						,		_A)
      self.check_equivalence_flax_to_pt(_A						,		_A						,		_A)


  @slow
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.get_pretrained_model_and_inputs()

      SCREAMING_SNAKE_CASE_									=						model_a(**_A)
      SCREAMING_SNAKE_CASE_									=						outputs[0]

      with tempfile.TemporaryDirectory() as tmp_dirname:
          model_a.save_pretrained(_A)
          SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_pretrained(_A)

          SCREAMING_SNAKE_CASE_									=						model_a(**_A)
          SCREAMING_SNAKE_CASE_									=						after_outputs[0]
          SCREAMING_SNAKE_CASE_									=						np.amax(np.abs(out_a - out_a))
          self.assertLessEqual(_A						,		1E-5)






@require_flax
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
          'hf-internal-testing/tiny-random-vit'						,		'hf-internal-testing/tiny-bert'						,		vision_from_pt=_A						,		text_from_pt=_A						,		)
      SCREAMING_SNAKE_CASE_									=						13
      SCREAMING_SNAKE_CASE_									=						floats_tensor(
          [
              batch_size,
              model.config.vision_config.num_channels,
              model.config.vision_config.image_size,
              model.config.vision_config.image_size,
          ])
      SCREAMING_SNAKE_CASE_									=						ids_tensor([batch_size, 4]						,		model.config.text_config.vocab_size)
      SCREAMING_SNAKE_CASE_									=						random_attention_mask([batch_size, 4])
      SCREAMING_SNAKE_CASE_									=						{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

      return model, inputs

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						FlaxViTModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModel(_A)
      return vision_model, text_model


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxViTModelTester(self)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModelTester(self)
      SCREAMING_SNAKE_CASE_									=						vit_model_tester.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_									=						bert_model_tester.prepare_config_and_inputs()

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						vision_config_and_inputs

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						text_config_and_inputs

      # make sure that cross attention layers are added
      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": attention_mask,
          "input_ids": input_ids,
          "token_type_ids": token_type_ids,
      }






@require_torch
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
          'hf-internal-testing/tiny-random-clip'						,		'hf-internal-testing/tiny-bert'						,		vision_from_pt=_A						,		text_from_pt=_A						,		)
      SCREAMING_SNAKE_CASE_									=						13
      SCREAMING_SNAKE_CASE_									=						floats_tensor(
          [
              batch_size,
              model.config.vision_config.num_channels,
              model.config.vision_config.image_size,
              model.config.vision_config.image_size,
          ])
      SCREAMING_SNAKE_CASE_									=						ids_tensor([batch_size, 4]						,		model.config.text_config.vocab_size)
      SCREAMING_SNAKE_CASE_									=						random_attention_mask([batch_size, 4])
      SCREAMING_SNAKE_CASE_									=						{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

      return model, inputs

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						FlaxCLIPVisionModel(_A)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModel(_A)
      return vision_model, text_model


  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxCLIPVisionModelTester(self)
      SCREAMING_SNAKE_CASE_									=						FlaxBertModelTester(self)
      SCREAMING_SNAKE_CASE_									=						clip_model_tester.prepare_config_and_inputs()
      SCREAMING_SNAKE_CASE_									=						bert_model_tester.prepare_config_and_inputs()

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						vision_config_and_inputs

      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						text_config_and_inputs

      # make sure that cross attention layers are added
      return {
          "text_config": text_config,
          "vision_config": vision_config,
          "pixel_values": pixel_values,
          "attention_mask": attention_mask,
          "input_ids": input_ids,
          "token_type_ids": token_type_ids,
      }






@require_flax
@require_vision
class       __snake_case  (    unittest.TestCase       ):

  @slow
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						FlaxVisionTextDualEncoderModel.from_pretrained('clip-italian/clip-italian'						,		logit_scale_init_value=1.0)
      SCREAMING_SNAKE_CASE_									=						VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian')

      SCREAMING_SNAKE_CASE_									=						Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
      SCREAMING_SNAKE_CASE_									=						processor(
          text=['una foto di un gatto', 'una foto di un cane']						,		images=_A						,		padding=_A						,		return_tensors='np')

      SCREAMING_SNAKE_CASE_									=						model(**_A)

      # verify the logits
      self.assertEqual(outputs.logits_per_image.shape						,		(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
      self.assertEqual(
          outputs.logits_per_text.shape						,		(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])						,		)

      SCREAMING_SNAKE_CASE_									=						np.array([[1.2_2_8_4_7_2_7, 0.3_1_0_4_1_2_2]])

      self.assertTrue(np.allclose(outputs.logits_per_image						,		_A						,		atol=1E-3))





import pickle

import numpy as np
from matplotlib import pyplot as plt



class       __snake_case  :

  def __init__(					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=0.2						,		_A=0.2):
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						bp_numa
      SCREAMING_SNAKE_CASE_									=						conva_get[:2]
      SCREAMING_SNAKE_CASE_									=						conva_get[2]
      SCREAMING_SNAKE_CASE_									=						size_pa
      SCREAMING_SNAKE_CASE_									=						rate_w
      SCREAMING_SNAKE_CASE_									=						rate_t
      SCREAMING_SNAKE_CASE_									=						[
          np.mat(-1 * np.random.rand(self.conva[0]						,		self.conva[0]) + 0.5)
          for i in range(self.conva[1])
      ]
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						np.mat(-1 * np.random.rand(self.num_bpa						,		self.num_bpa) + 0.5)
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.conva[1]) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1
      SCREAMING_SNAKE_CASE_									=						-2 * np.random.rand(self.num_bpa) + 1

  def 						lowerCAmelCase__     (					self						,		_A):
      # save model dict with pickle
      SCREAMING_SNAKE_CASE_									=						{
          '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(_A						,		'wb') as f:
          pickle.dump(_A						,		_A)

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

  @classmethod
  def 						lowerCAmelCase__     (					cls						,		_A):
      # read saved model
      with open(_A						,		'rb') as f:
          SCREAMING_SNAKE_CASE_									=						pickle.load(_A)  # noqa: S301

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

  def 						lowerCAmelCase__     (					self						,		_A):
      return 1 / (1 + np.exp(-1 * x))

  def 						lowerCAmelCase__     (					self						,		_A):
      return round(_A						,		3)

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

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A="average_pool"):
      # pooling process
      SCREAMING_SNAKE_CASE_									=						len(featuremaps[0])
      SCREAMING_SNAKE_CASE_									=						int(size_map / size_pooling)
      SCREAMING_SNAKE_CASE_									=						[]
      for i_map in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						featuremaps[i_map]
          SCREAMING_SNAKE_CASE_									=						[]
          for i_focus in range(0						,		_A						,		_A):
              for j_focus in range(0						,		_A						,		_A):
                  SCREAMING_SNAKE_CASE_									=						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(_A))
                  elif pooling_type == "max_pooling":
                      # max pooling
                      map_pooled.append(np.max(_A))
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A).reshape(_A						,		_A)
          featuremap_pooled.append(_A)
      return featuremap_pooled

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding three dimension data to one dimension list
      SCREAMING_SNAKE_CASE_									=						[]
      for i in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.shape(data[i])
          SCREAMING_SNAKE_CASE_									=						data[i].reshape(1						,		shapes[0] * shapes[1])
          SCREAMING_SNAKE_CASE_									=						data_listed.getA().tolist()[0]
          data_expanded.extend(_A)
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      return data_expanded

  def 						lowerCAmelCase__     (					self						,		_A):
      # expanding matrix to one dimension list
      SCREAMING_SNAKE_CASE_									=						np.asarray(_A)
      SCREAMING_SNAKE_CASE_									=						np.shape(_A)
      SCREAMING_SNAKE_CASE_									=						data_mat.reshape(1						,		shapes[0] * shapes[1])
      return data_expanded

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

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A						,		_A						,		_A						,		_A=bool):
      # model traning
      print('----------------------Start Training-------------------------')
      print((' - - Shape: Train_Data  ', np.shape(_A)))
      print((' - - Shape: Teach_Data  ', np.shape(_A)))
      SCREAMING_SNAKE_CASE_									=						0
      SCREAMING_SNAKE_CASE_									=						[]
      SCREAMING_SNAKE_CASE_									=						10000
      while rp < n_repeat and mse >= error_accuracy:
          SCREAMING_SNAKE_CASE_									=						0
          print(f"""-------------Learning Time {rp}--------------""")
          for p in range(len(_A)):
              # print('------------Learning Image: %d--------------'%p)
              SCREAMING_SNAKE_CASE_									=						np.asmatrix(datas_train[p])
              SCREAMING_SNAKE_CASE_									=						np.asarray(datas_teach[p])
              SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
                  _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
              SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)
              SCREAMING_SNAKE_CASE_									=						np.shape(_A)
              SCREAMING_SNAKE_CASE_									=						self._expand(_A)
              SCREAMING_SNAKE_CASE_									=						data_bp_input

              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.vji.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)
              SCREAMING_SNAKE_CASE_									=						np.dot(_A						,		self.wkj.T) - self.thre_bpa
              SCREAMING_SNAKE_CASE_									=						self.sig(_A)

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

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

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

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

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

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

  def 						lowerCAmelCase__     (					self						,		_A):
      # model predict
      SCREAMING_SNAKE_CASE_									=						[]
      print('-------------------Start Testing-------------------------')
      print((' - - Shape: Test_Data  ', np.shape(_A)))
      for p in range(len(_A)):
          SCREAMING_SNAKE_CASE_									=						np.asmatrix(datas_test[p])
          SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
              _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
          SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)
          SCREAMING_SNAKE_CASE_									=						self._expand(_A)

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


  def 						lowerCAmelCase__     (					self						,		_A):
      # return the data of image after convoluting process so we can check it out
      SCREAMING_SNAKE_CASE_									=						np.asmatrix(_A)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						self.convolute(
          _A						,		self.conva						,		self.w_conva						,		self.thre_conva						,		conv_step=self.step_conva						,		)
      SCREAMING_SNAKE_CASE_									=						self.pooling(_A						,		self.size_poolinga)

      return data_conveda, data_pooleda


if __name__ == "__main__":
       pass


