




import pickle

import numpy as np
from matplotlib import pyplot as plt


class 							UpperCamelCase_       :

    '''simple docstring'''






    def __init__(     self :      List[Any]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Dict=0.2   ,					UpperCAmelCase__ :      Optional[Any]=0.2)		->List[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 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Tuple)		->int:





           '''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(UpperCAmelCase__   ,					'''wb''') as f:
                  pickle.dump(UpperCAmelCase__   ,					UpperCAmelCase__)

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


    @classmethod
    def 			SCREAMING_SNAKE_CASE				(     cls :      Dict   ,					UpperCAmelCase__ :      str)		->Optional[Any]:





           '''simple docstring'''

           with open(UpperCAmelCase__   ,					'''rb''') as f:
                  A__ 					=       pickle.load(UpperCAmelCase__)  # 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(UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__   ,					UpperCAmelCase__)
           # 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 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      List[str])		->List[str]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      List[str]   ,					UpperCAmelCase__ :      Tuple)		->List[Any]:





           '''simple docstring'''

           return round(UpperCAmelCase__   ,					3)


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      str   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Tuple)		->List[str]:





           '''simple docstring'''

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

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Optional[Any]   ,					UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      Optional[int]="average_pool")		->int:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      Optional[Any])		->List[Any]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Tuple   ,					UpperCAmelCase__ :      Tuple)		->Dict:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Optional[int]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      List[Any]   ,					UpperCAmelCase__ :      Any   ,					UpperCAmelCase__ :      Tuple)		->Union[str, Any]:





           '''simple docstring'''

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


    def 			SCREAMING_SNAKE_CASE				(     self :      int   ,					UpperCAmelCase__ :      List[str]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Dict   ,					UpperCAmelCase__ :      Union[str, Any]   ,					UpperCAmelCase__ :      Tuple   ,					UpperCAmelCase__ :      Dict=bool)		->Dict:





           '''simple docstring'''

           print('''----------------------Start Training-------------------------''')
           print((''' - - Shape: Train_Data  ''', np.shape(UpperCAmelCase__)))
           print((''' - - Shape: Teach_Data  ''', np.shape(UpperCAmelCase__)))
           A__ 					=       0
           A__ 					=       []
           A__ 					=       10_000
           while rp < n_repeat and mse >= error_accuracy:
                  A__ 					=       0
                  print(f"""-------------Learning Time {rp}--------------""")
                  for p in range(len(UpperCAmelCase__)):
                         # print('------------Learning Image: %d--------------'%p)
                         A__ 					=       np.asmatrix(datas_train[p])
                         A__ 					=       np.asarray(datas_teach[p])
                         A__					,				A__ 					=       self.convolute(
                             UpperCAmelCase__   ,					self.conva   ,					self.w_conva   ,					self.thre_conva   ,					conv_step=self.step_conva   ,					)
                         A__ 					=       self.pooling(UpperCAmelCase__   ,					self.size_poolinga)
                         A__ 					=       np.shape(UpperCAmelCase__)
                         A__ 					=       self._expand(UpperCAmelCase__)
                         A__ 					=       data_bp_input

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

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

                                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(UpperCAmelCase__)

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

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


    def 			SCREAMING_SNAKE_CASE				(     self :      Dict   ,					UpperCAmelCase__ :      List[Any])		->str:





           '''simple docstring'''

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

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





    def 			SCREAMING_SNAKE_CASE				(     self :      Union[str, Any]   ,					UpperCAmelCase__ :      Tuple)		->int:





           '''simple docstring'''

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

           return data_conveda, data_pooleda


if __name__ == "__main__":
  pass







import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import datasets
import evaluate
import numpy as np
from datasets import load_dataset

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version


# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")

lowerCamelCase		       =      logging.getLogger(__name__)


@dataclass
class 				_a						:





							'''simple docstring'''

							A				:Optional[int] =       field(
							    default=1_28       ,  metadata={
							        "help": (
							            "The maximum total input sequence length after tokenization. Sequences longer "
							            "than this will be truncated, sequences shorter will be padded."
							        )
							    }       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Overwrite the cached preprocessed datasets or not."}			)
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Whether to pad all samples to `max_seq_length`. "
							            "If False, will pad the samples dynamically when batching to the maximum length in the batch."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of training examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
							            "value if set."
							        )
							    }       ,  )
							A				:Optional[int] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "For debugging purposes or quicker training, truncate the number of prediction examples to this "
							            "value if set."
							        )
							    }       ,  )





@dataclass
class 				_a						:





							'''simple docstring'''

							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}			)
							A				:str =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Evaluation language. Also train language if `train_language` is set to None."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Train language if it is different from the evaluation language."}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained config name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}			)
							A				:Optional[str] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}       ,  )
							A				:Optional[bool] =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}       ,  )
							A				:str =       field(
							    default="main"       ,  metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={
							        "help": (
							            "Will use the token generated when running `huggingface-cli login` (necessary to use this script "
							            "with private models)."
							        )
							    }       ,  )
							A				:bool =       field(
							    default=SCREAMING_SNAKE_CASE       ,  metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."}       ,  )






def     SCREAMING_SNAKE_CASE(      )	->						int:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				a__	:							List[str]      						= HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				a__						,   a__						,   a__	:							Optional[int]      						= parser.parse_args_into_dataclasses()

				# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
				# information sent is the one passed as arguments along with your Python/PyTorch versions.
				send_example_telemetry("run_xnli"	,				__UpperCamelCase )

				# Setup logging
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"	,				datefmt="%m/%d/%Y %H:%M:%S"	,				handlers=[logging.StreamHandler(sys.stdout )]	,				)

				if training_args.should_log:
								# The default of training_args.log_level is passive, so we set log level at info here to have that default.
								transformers.utils.logging.set_verbosity_info()

				a__	:							int      						= training_args.get_process_log_level()
				logger.setLevel(__UpperCamelCase )
				datasets.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.set_verbosity(__UpperCamelCase )
				transformers.utils.logging.enable_default_handler()
				transformers.utils.logging.enable_explicit_format()

				# Log on each process the small summary:
				logger.warning(
				    F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
				    + F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
				logger.info(F'Training/evaluation parameters {training_args}' )

				# Detecting last checkpoint.
				a__	:							Union[str, Any]      						= None
				if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
								a__	:							Dict      						= get_last_checkpoint(training_args.output_dir )
								if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
												raise ValueError(
												    F'Output directory ({training_args.output_dir}) already exists and is not empty. '
												    "Use --overwrite_output_dir to overcome." )
								elif last_checkpoint is not None:
												logger.info(
												    F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
												    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )

    # Set seed before initializing model.
				set_seed(training_args.seed )

				# In distributed training, the load_dataset function guarantees that only one local process can concurrently
				# download the dataset.
				# Downloading and loading xnli dataset from the hub.
				if training_args.do_train:
								if model_args.train_language is None:
												a__	:							Any      						= load_dataset(
												    "xnli"	,				model_args.language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								else:
												a__	:							List[str]      						= load_dataset(
												    "xnli"	,				model_args.train_language	,				split="train"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							List[Any]      						= train_dataset.features["label"].names

				if training_args.do_eval:
								a__	:							Tuple      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="validation"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							int      						= eval_dataset.features["label"].names

				if training_args.do_predict:
								a__	:							Dict      						= load_dataset(
								    "xnli"	,				model_args.language	,				split="test"	,				cache_dir=model_args.cache_dir	,				use_auth_token=True if model_args.use_auth_token else None	,				)
								a__	:							Optional[Any]      						= predict_dataset.features["label"].names

				# Labels
				a__	:							List[Any]      						= len(__UpperCamelCase )

				# Load pretrained model and tokenizer
				# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.
				a__	:							List[Any]      						= AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path	,				num_labels=__UpperCamelCase	,				idalabel={str(__UpperCamelCase ): label for i, label in enumerate(__UpperCamelCase )}	,				labelaid={label: i for i, label in enumerate(__UpperCamelCase )}	,				finetuning_task="xnli"	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Union[str, Any]      						= AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path	,				do_lower_case=model_args.do_lower_case	,				cache_dir=model_args.cache_dir	,				use_fast=model_args.use_fast_tokenizer	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				)
				a__	:							Optional[int]      						= AutoModelForSequenceClassification.from_pretrained(
				    model_args.model_name_or_path	,				from_tf=bool(".ckpt" in model_args.model_name_or_path )	,				config=__UpperCamelCase	,				cache_dir=model_args.cache_dir	,				revision=model_args.model_revision	,				use_auth_token=True if model_args.use_auth_token else None	,				ignore_mismatched_sizes=model_args.ignore_mismatched_sizes	,				)

				# Preprocessing the datasets
				# Padding strategy
				if data_args.pad_to_max_length:
								a__	:							Dict      						= "max_length"
				else:
								# We will pad later, dynamically at batch creation, to the max sequence length in each batch
								a__	:							Optional[Any]      						= False

				def preprocess_function(__UpperCamelCase ):
								# Tokenize the texts
								return tokenizer(
								    examples["premise"]	,				examples["hypothesis"]	,				padding=__UpperCamelCase	,				max_length=data_args.max_seq_length	,				truncation=__UpperCamelCase	,				)

				if training_args.do_train:
								if data_args.max_train_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_train_samples )
												a__	:							Tuple      						= train_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="train dataset map pre-processing" ):
												a__	:							Optional[int]      						= train_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on train dataset"	,				)
								# Log a few random samples from the training set:
								for index in random.sample(range(len(__UpperCamelCase ) )	,				3 ):
												logger.info(F'Sample {index} of the training set: {train_dataset[index]}.' )

				if training_args.do_eval:
								if data_args.max_eval_samples is not None:
												a__	:							Optional[int]      						= min(len(__UpperCamelCase )	,				data_args.max_eval_samples )
												a__	:							Optional[Any]      						= eval_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="validation dataset map pre-processing" ):
												a__	:							List[Any]      						= eval_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on validation dataset"	,				)

				if training_args.do_predict:
								if data_args.max_predict_samples is not None:
												a__	:							int      						= min(len(__UpperCamelCase )	,				data_args.max_predict_samples )
												a__	:							List[str]      						= predict_dataset.select(range(__UpperCamelCase ) )
								with training_args.main_process_first(desc="prediction dataset map pre-processing" ):
												a__	:							Union[str, Any]      						= predict_dataset.map(
												    __UpperCamelCase	,				batched=__UpperCamelCase	,				load_from_cache_file=not data_args.overwrite_cache	,				desc="Running tokenizer on prediction dataset"	,				)

    # Get the metric function
				a__	:							List[Any]      						= evaluate.load("xnli" )

				# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
				# predictions and label_ids field) and has to return a dictionary string to float.
				def compute_metrics(__UpperCamelCase ):
								a__	:							Dict      						= p.predictions[0] if isinstance(p.predictions	,				__UpperCamelCase ) else p.predictions
								a__	:							Any      						= np.argmax(__UpperCamelCase	,				axis=1 )
								return metric.compute(predictions=__UpperCamelCase	,				references=p.label_ids )

				# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
				if data_args.pad_to_max_length:
								a__	:							List[Any]      						= default_data_collator
				elif training_args.fpaa:
								a__	:							List[Any]      						= DataCollatorWithPadding(__UpperCamelCase	,				pad_to_multiple_of=8 )
				else:
								a__	:							Tuple      						= None

				# Initialize our Trainer
				a__	:							List[str]      						= Trainer(
				    model=__UpperCamelCase	,				args=__UpperCamelCase	,				train_dataset=train_dataset if training_args.do_train else None	,				eval_dataset=eval_dataset if training_args.do_eval else None	,				compute_metrics=__UpperCamelCase	,				tokenizer=__UpperCamelCase	,				data_collator=__UpperCamelCase	,				)

				# Training
				if training_args.do_train:
								a__	:							str      						= None
								if training_args.resume_from_checkpoint is not None:
												a__	:							int      						= training_args.resume_from_checkpoint
								elif last_checkpoint is not None:
												a__	:							Optional[int]      						= last_checkpoint
								a__	:							Any      						= trainer.train(resume_from_checkpoint=__UpperCamelCase )
								a__	:							Tuple      						= train_result.metrics
								a__	:							Union[str, Any]      						= (
								    data_args.max_train_samples if data_args.max_train_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Optional[int]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.save_model()  # Saves the tokenizer too for easy upload

								trainer.log_metrics("train"	,				__UpperCamelCase )
								trainer.save_metrics("train"	,				__UpperCamelCase )
								trainer.save_state()

				# Evaluation
				if training_args.do_eval:
								logger.info("*** Evaluate ***" )
								a__	:							Optional[int]      						= trainer.evaluate(eval_dataset=__UpperCamelCase )

								a__	:							Tuple      						= data_args.max_eval_samples if data_args.max_eval_samples is not None else len(__UpperCamelCase )
								a__	:							List[Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("eval"	,				__UpperCamelCase )
								trainer.save_metrics("eval"	,				__UpperCamelCase )

				# Prediction
				if training_args.do_predict:
								logger.info("*** Predict ***" )
								a__						,   a__						,   a__	:							int      						= trainer.predict(__UpperCamelCase	,				metric_key_prefix="predict" )

								a__	:							int      						= (
								    data_args.max_predict_samples if data_args.max_predict_samples is not None else len(__UpperCamelCase )
								)
								a__	:							Union[str, Any]      						= min(__UpperCamelCase	,				len(__UpperCamelCase ) )

								trainer.log_metrics("predict"	,				__UpperCamelCase )
								trainer.save_metrics("predict"	,				__UpperCamelCase )

								a__	:							Dict      						= np.argmax(__UpperCamelCase	,				axis=1 )
								a__	:							str      						= os.path.join(training_args.output_dir	,				"predictions.txt" )
								if trainer.is_world_process_zero():
												with open(__UpperCamelCase	,				"w" ) as writer:
																writer.write("index\tprediction\n" )
																for index, item in enumerate(__UpperCamelCase ):
																				a__	:							int      						= label_list[item]
																				writer.write(F'{index}\t{item}\n' )


if __name__ == "__main__":
						main()


