





'''simple docstring'''







import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
_lowercase       						=			False
_lowercase       						=			False

def    __UpperCamelCase	(	a    : Namespace     ) ->Union[str, Any]:
 return TrainCommand(a     )






class 			_lowercase						(       __a					):







     @staticmethod
     def     UpperCamelCase			( A__    ) ->	Any:
      snake_case				=			parser.add_parser('''train'''    ,   help='''CLI tool to train a model on a task.'''    )

      train_parser.add_argument(
          '''--train_data'''    ,   type=A__    ,   required=A__    ,   help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''    ,   )
      train_parser.add_argument(
          '''--column_label'''    ,   type=A__    ,   default=0    ,   help='''Column of the dataset csv file with example labels.'''    )
      train_parser.add_argument(
          '''--column_text'''    ,   type=A__    ,   default=1    ,   help='''Column of the dataset csv file with example texts.'''    )
      train_parser.add_argument(
          '''--column_id'''    ,   type=A__    ,   default=2    ,   help='''Column of the dataset csv file with example ids.'''    )
      train_parser.add_argument(
          '''--skip_first_row'''    ,   action='''store_true'''    ,   help='''Skip the first row of the csv file (headers).'''    )

      train_parser.add_argument('''--validation_data'''    ,   type=A__    ,   default=''''''    ,   help='''path to validation dataset.'''    )
      train_parser.add_argument(
          '''--validation_split'''    ,   type=A__    ,   default=0.1    ,   help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.'''    ,   )

      train_parser.add_argument('''--output'''    ,   type=A__    ,   default='''./'''    ,   help='''path to saved the trained model.'''    )

      train_parser.add_argument(
          '''--task'''    ,   type=A__    ,   default='''text_classification'''    ,   help='''Task to train the model on.'''    )
      train_parser.add_argument(
          '''--model'''    ,   type=A__    ,   default='''bert-base-uncased'''    ,   help='''Model\'s name or path to stored model.'''    )
      train_parser.add_argument('''--train_batch_size'''    ,   type=A__    ,   default=32    ,   help='''Batch size for training.'''    )
      train_parser.add_argument('''--valid_batch_size'''    ,   type=A__    ,   default=64    ,   help='''Batch size for validation.'''    )
      train_parser.add_argument('''--learning_rate'''    ,   type=A__    ,   default=3e-5    ,   help='''Learning rate.'''    )
      train_parser.add_argument('''--adam_epsilon'''    ,   type=A__    ,   default=1e-08    ,   help='''Epsilon for Adam optimizer.'''    )
      train_parser.set_defaults(func=A__    )







     def __init__( self    ,   A__    ) ->	Any:
      snake_case				=			logging.get_logger('''transformers-cli/training'''    )

      snake_case				=			'''tf''' if is_tf_available() else '''torch'''

      os.makedirs(args.output    ,   exist_ok=A__    )
      snake_case				=			args.output

      snake_case				=			args.column_label
      snake_case				=			args.column_text
      snake_case				=			args.column_id

      self.logger.info(F"""Loading {args.task} pipeline for {args.model}"""    )
      if args.task == "text_classification":
       snake_case				=			TextClassificationPipeline.from_pretrained(args.model    )
      elif args.task == "token_classification":
       raise NotImplementedError
      elif args.task == "question_answering":
       raise NotImplementedError

      self.logger.info(F"""Loading dataset from {args.train_data}"""    )
      snake_case				=			Processor.create_from_csv(
          args.train_data    ,   column_label=args.column_label    ,   column_text=args.column_text    ,   column_id=args.column_id    ,   skip_first_row=args.skip_first_row    ,   )
      snake_case				=			None
      if args.validation_data:
       self.logger.info(F"""Loading validation dataset from {args.validation_data}"""    )
       snake_case				=			Processor.create_from_csv(
           args.validation_data    ,   column_label=args.column_label    ,   column_text=args.column_text    ,   column_id=args.column_id    ,   skip_first_row=args.skip_first_row    ,   )

      snake_case				=			args.validation_split
      snake_case				=			args.train_batch_size
      snake_case				=			args.valid_batch_size
      snake_case				=			args.learning_rate
      snake_case				=			args.adam_epsilon







     def     UpperCamelCase			( self    ) ->	Any:
      if self.framework == "tf":
       return self.run_tf()
      return self.run_torch()







     def     UpperCamelCase			( self    ) ->	List[str]:
      raise NotImplementedError







     def     UpperCamelCase			( self    ) ->	List[str]:
      self.pipeline.fit(
          self.train_dataset    ,   validation_data=self.valid_dataset    ,   validation_split=self.validation_split    ,   learning_rate=self.learning_rate    ,   adam_epsilon=self.adam_epsilon    ,   train_batch_size=self.train_batch_size    ,   valid_batch_size=self.valid_batch_size    ,   )

      # Save trained pipeline
      self.pipeline.save_pretrained(self.output    )








'''simple docstring'''







import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase       						=			logging.get_logger(__name__)

def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[int]					,   a    : Dict					,   a    : Dict     ) ->Union[str, Any]:
 snake_case				=			original_name.split('''.'''     )[0]
 snake_case				=			key.split('''.'''     )
 snake_case				=			int(key_list[key_list.index(a     ) - 2]     )
 snake_case				=			int(key_list[key_list.index(a     ) - 1]     )
 snake_case				=			orig_block_num - offset

 snake_case				=			key.replace(f"""{orig_block_num}.{layer_num}.{original_name}"""					,   f"""block.{new_block_num}.{layer_num}.{new_name}"""     )
 return key

def    __UpperCamelCase	(	a    : Tuple     ) ->Dict:
 snake_case				=			OrderedDict()
 snake_case				,		snake_case				=			0, 0
 for key, value in state_dict.items():
  if key.startswith('''network'''     ):
   snake_case				=			key.replace('''network'''					,   '''poolformer.encoder'''     )
  if "proj" in key:
   # Works for the first embedding as well as the internal embedding layers
   if key.endswith('''bias'''     ) and "patch_embed" not in key:
    patch_emb_offset += 1
   snake_case				=			key[: key.find('''proj'''     )]
   snake_case				=			key.replace(a					,   f"""patch_embeddings.{total_embed_found}."""     )
   snake_case				=			key.replace('''proj'''					,   '''projection'''     )
   if key.endswith('''bias'''     ):
    total_embed_found += 1
  if "patch_embeddings" in key:
   snake_case				=			'''poolformer.encoder.''' + key
  if "mlp.fc1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc1'''					,   '''output.conv1'''     )
  if "mlp.fc2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''mlp.fc2'''					,   '''output.conv2'''     )
  if "norm1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm1'''					,   '''before_norm'''     )
  if "norm2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''norm2'''					,   '''after_norm'''     )
  if "layer_scale_1" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_1'''					,   '''layer_scale_1'''     )
  if "layer_scale_2" in key:
   snake_case				=			replace_key_with_offset(a					,   a					,   '''layer_scale_2'''					,   '''layer_scale_2'''     )
  if "head" in key:
   snake_case				=			key.replace('''head'''					,   '''classifier'''     )
  snake_case				=			value
 return new_state_dict

def    __UpperCamelCase	(	) ->Optional[int]:
 snake_case				=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
 snake_case				=			Image.open(requests.get(a					,   stream=a     ).raw     )

 return image

@torch.no_grad()
def    __UpperCamelCase	(	a    : Dict					,   a    : Optional[Any]					,   a    : Tuple     ) ->List[str]:
 snake_case				=			PoolFormerConfig()

 # set attributes based on model_name
 snake_case				=			'''huggingface/label-files'''
 snake_case				=			model_name[-3:]
 snake_case				=			1000
 snake_case				=			'''imagenet-1k-id2label.json'''
 snake_case				=			(1, 1000)

 # set config attributes
 snake_case				=			json.load(open(hf_hub_download(a					,   a					,   repo_type='''dataset'''     )					,   '''r'''     )     )
 snake_case				=			{int(a     ): v for k, v in idalabel.items()}
 snake_case				=			idalabel
 snake_case				=			{v: k for k, v in idalabel.items()}
 if size == "s12":
  snake_case				=			[2, 2, 6, 2]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s24":
  snake_case				=			[4, 4, 12, 4]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			0.9
 elif size == "s36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[64, 128, 320, 512]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.9
 elif size == "m36":
  snake_case				=			[6, 6, 18, 6]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 elif size == "m48":
  snake_case				=			[8, 8, 24, 8]
  snake_case				=			[96, 192, 384, 768]
  snake_case				=			4.0
  snake_case				=			1e-6
  snake_case				=			0.95
 else:
  raise ValueError(f"""Size {size} not supported"""     )

 # load image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )

 # Prepare image
 snake_case				=			prepare_img()
 snake_case				=			image_processor(images=a					,   return_tensors='''pt'''     ).pixel_values

 logger.info(f"""Converting model {model_name}..."""     )

 # load original state dict
 snake_case				=			torch.load(a					,   map_location=torch.device('''cpu'''     )     )

 # rename keys
 snake_case				=			rename_keys(a     )

 # create HuggingFace model and load state dict
 snake_case				=			PoolFormerForImageClassification(a     )
 model.load_state_dict(a     )
 model.eval()

 # Define image processor
 snake_case				=			PoolFormerImageProcessor(crop_pct=a     )
 snake_case				=			image_processor(images=prepare_img()					,   return_tensors='''pt'''     ).pixel_values

 # forward pass
 snake_case				=			model(a     )
 snake_case				=			outputs.logits

 # define expected logit slices for different models
 if size == "s12":
  snake_case				=			torch.tensor([-0.3045, -0.6758, -0.4869]     )
 elif size == "s24":
  snake_case				=			torch.tensor([0.4402, -0.1374, -0.8045]     )
 elif size == "s36":
  snake_case				=			torch.tensor([-0.6080, -0.5133, -0.5898]     )
 elif size == "m36":
  snake_case				=			torch.tensor([0.3952, 0.2263, -1.2668]     )
 elif size == "m48":
  snake_case				=			torch.tensor([0.1167, -0.0656, -0.3423]     )
 else:
  raise ValueError(f"""Size {size} not supported"""     )

 # verify logits
 assert logits.shape == expected_shape
 assert torch.allclose(logits[0, :3]					,   a					,   atol=1e-2     )

 # finally, save model and image processor
 logger.info(f"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""     )
 Path(a     ).mkdir(exist_ok=a     )
 model.save_pretrained(a     )
 print(f"""Saving image processor to {pytorch_dump_folder_path}"""     )
 image_processor.save_pretrained(a     )


if __name__ == "__main__":
   _lowercase       						=			argparse.ArgumentParser()

   parser.add_argument(
       '--model_name',
       default='poolformer_s12',
       type=str,
       help='Name of the model you\'d like to convert.',
   )
   parser.add_argument(
       '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
   )
   _lowercase       						=			parser.parse_args()
   convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)

