



import pickle

import numpy as np
from matplotlib import pyplot as plt



class 	lowerCamelCase      :







					'''simple docstring'''







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



					def    A__		(			self    ,	lowerCAmelCase     ):
							# save model dict with pickle
							UpperCAmelCase_						=				{
							    "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    ,	lowerCAmelCase     ):
							# read saved model
							with open(lowerCAmelCase    ,	"rb"     ) as f:
									UpperCAmelCase_						=				pickle.load(lowerCAmelCase     )  # noqa: S301

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



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



					def    A__		(			self    ,	lowerCAmelCase     ):
							return round(lowerCAmelCase    ,	3     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
							# convolution process
							UpperCAmelCase_						=				convs[0]
							UpperCAmelCase_						=				convs[1]
							UpperCAmelCase_						=				np.shape(lowerCAmelCase     )[0]
							# get the data slice of original image data, data_focus
							UpperCAmelCase_						=				[]
							for i_focus in range(0    ,	size_data - size_conv + 1    ,	lowerCAmelCase     ):
									for j_focus in range(0    ,	size_data - size_conv + 1    ,	lowerCAmelCase     ):
											UpperCAmelCase_						=				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
							UpperCAmelCase_						=				[]
							UpperCAmelCase_						=				int((size_data - size_conv) / conv_step + 1     )
							for i_map in range(lowerCAmelCase     ):
									UpperCAmelCase_						=				[]
									for i_focus in range(len(lowerCAmelCase     )     ):
											UpperCAmelCase_						=				(
											    np.sum(np.multiply(data_focus[i_focus]    ,	w_convs[i_map]     )     )
											    - thre_convs[i_map]
											)
											featuremap.append(self.sig(lowerCAmelCase     )     )
									UpperCAmelCase_						=				np.asmatrix(lowerCAmelCase     ).reshape(
									    lowerCAmelCase    ,	lowerCAmelCase     )
									data_featuremap.append(lowerCAmelCase     )

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



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase="average_pool"     ):
							# pooling process
							UpperCAmelCase_						=				len(featuremaps[0]     )
							UpperCAmelCase_						=				int(size_map / size_pooling     )
							UpperCAmelCase_						=				[]
							for i_map in range(len(lowerCAmelCase     )     ):
									UpperCAmelCase_						=				featuremaps[i_map]
									UpperCAmelCase_						=				[]
									for i_focus in range(0    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
											for j_focus in range(0    ,	lowerCAmelCase    ,	lowerCAmelCase     ):
													UpperCAmelCase_						=				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     )     )
									UpperCAmelCase_						=				np.asmatrix(lowerCAmelCase     ).reshape(lowerCAmelCase    ,	lowerCAmelCase     )
									featuremap_pooled.append(lowerCAmelCase     )
							return featuremap_pooled



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



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



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



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase    ,	lowerCAmelCase=bool     ):
							# model traning
							print("----------------------Start Training-------------------------"     )
							print((" - - Shape: Train_Data  ", np.shape(lowerCAmelCase     ))     )
							print((" - - Shape: Teach_Data  ", np.shape(lowerCAmelCase     ))     )
							UpperCAmelCase_						=				0
							UpperCAmelCase_						=				[]
							UpperCAmelCase_						=				1_0000
							while rp < n_repeat and mse >= error_accuracy:
									UpperCAmelCase_						=				0
									print(f'''-------------Learning Time {rp}--------------'''     )
									for p in range(len(lowerCAmelCase     )     ):
											# print('------------Learning Image: %d--------------'%p)
											UpperCAmelCase_						=				np.asmatrix(datas_train[p]     )
											UpperCAmelCase_						=				np.asarray(datas_teach[p]     )
											UpperCAmelCase_					,						UpperCAmelCase_						=				self.convolute(
											    lowerCAmelCase    ,	self.conva    ,	self.w_conva    ,	self.thre_conva    ,	conv_step=self.step_conva    ,	)
											UpperCAmelCase_						=				self.pooling(lowerCAmelCase    ,	self.size_poolinga     )
											UpperCAmelCase_						=				np.shape(lowerCAmelCase     )
											UpperCAmelCase_						=				self._expand(lowerCAmelCase     )
											UpperCAmelCase_						=				data_bp_input

											UpperCAmelCase_						=				np.dot(lowerCAmelCase    ,	self.vji.T     ) - self.thre_bpa
											UpperCAmelCase_						=				self.sig(lowerCAmelCase     )
											UpperCAmelCase_						=				np.dot(lowerCAmelCase    ,	self.wkj.T     ) - self.thre_bpa
											UpperCAmelCase_						=				self.sig(lowerCAmelCase     )

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

											UpperCAmelCase_						=				pd_i_all / (self.size_poolinga * self.size_poolinga)
											UpperCAmelCase_						=				pd_conva_pooled.T.getA().tolist()
											UpperCAmelCase_						=				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]     ):
													UpperCAmelCase_						=				self._expand_mat(pd_conva_all[k_conv]     )
													UpperCAmelCase_						=				self.rate_weight * np.dot(lowerCAmelCase    ,	lowerCAmelCase     )

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

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

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

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



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

							return data_conveda, data_pooleda


if __name__ == "__main__":
			pass






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
			from PIL import Image

			from ..image_utils import load_image

if is_tf_available():
			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
			import torch

			from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)



@add_end_docstrings(lowercase__  )
class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					def __init__(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							super().__init__(*lowerCAmelCase    ,	**lowerCAmelCase     )
							requires_backends(self    ,	"vision"     )
							self.check_model_type(
							    TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == "tf" else MODEL_FOR_VISION_2_SEQ_MAPPING     )



					def    A__		(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				{}
							UpperCAmelCase_						=				{}

							if prompt is not None:
									UpperCAmelCase_						=				prompt

							if generate_kwargs is not None:
									UpperCAmelCase_						=				generate_kwargs
							if max_new_tokens is not None:
									if "generate_kwargs" not in forward_kwargs:
											UpperCAmelCase_						=				{}
									if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
											raise ValueError(
											    "'max_new_tokens' is defined twice, once in 'generate_kwargs' and once as a direct parameter,"
											    " please use only one"     )
									UpperCAmelCase_						=				max_new_tokens
							return preprocess_params, forward_kwargs, {}



					def __call__(			self    ,	lowerCAmelCase    ,	**lowerCAmelCase     ):
							return super().__call__(lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							UpperCAmelCase_						=				load_image(lowerCAmelCase     )

							if prompt is not None:
									if not isinstance(lowerCAmelCase    ,	lowerCAmelCase     ):
											raise ValueError(
											    f'''Received an invalid text input, got - {type(lowerCAmelCase     )} - but expected a single string. '''
											    "Note also that one single text can be provided for conditional image to text generation."     )

									UpperCAmelCase_						=				self.model.config.model_type

									if model_type == "git":
											UpperCAmelCase_						=				self.image_processor(images=lowerCAmelCase    ,	return_tensors=self.framework     )
											UpperCAmelCase_						=				self.tokenizer(text=lowerCAmelCase    ,	add_special_tokens=lowerCAmelCase     ).input_ids
											UpperCAmelCase_						=				[self.tokenizer.cls_token_id] + input_ids
											UpperCAmelCase_						=				torch.tensor(lowerCAmelCase     ).unsqueeze(0     )
											model_inputs.update({"input_ids": input_ids}     )

									elif model_type == "pix2struct":
											UpperCAmelCase_						=				self.image_processor(images=lowerCAmelCase    ,	header_text=lowerCAmelCase    ,	return_tensors=self.framework     )

									elif model_type != "vision-encoder-decoder":
											# vision-encoder-decoder does not support conditional generation
											UpperCAmelCase_						=				self.image_processor(images=lowerCAmelCase    ,	return_tensors=self.framework     )
											UpperCAmelCase_						=				self.tokenizer(lowerCAmelCase    ,	return_tensors=self.framework     )
											model_inputs.update(lowerCAmelCase     )

									else:
											raise ValueError(f'''Model type {model_type} does not support conditional text generation'''     )

							else:
									UpperCAmelCase_						=				self.image_processor(images=lowerCAmelCase    ,	return_tensors=self.framework     )

							if self.model.config.model_type == "git" and prompt is None:
									UpperCAmelCase_						=				None

							return model_inputs



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase=None     ):
							# Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
							# pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
							if (
							    "input_ids" in model_inputs
							    and isinstance(model_inputs["input_ids"]    ,	lowerCAmelCase     )
							    and all(x is None for x in model_inputs["input_ids"]     )
							):
									UpperCAmelCase_						=				None

							if generate_kwargs is None:
									UpperCAmelCase_						=				{}
							# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
							#  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
							#  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
							#  in the `_prepare_model_inputs` method.
							UpperCAmelCase_						=				model_inputs.pop(self.model.main_input_name     )
							UpperCAmelCase_						=				self.model.generate(lowerCAmelCase    ,	**lowerCAmelCase    ,	**lowerCAmelCase     )
							return model_outputs



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				[]
							for output_ids in model_outputs:
									UpperCAmelCase_						=				{
									    "generated_text": self.tokenizer.decode(
									        lowerCAmelCase    ,	skip_special_tokens=lowerCAmelCase    ,	)
									}
									records.append(lowerCAmelCase     )
							return records

