


import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor






def 						a__			(				A__				):
			if "cls_token" in name:
						SCREAMING_SNAKE_CASE_		:     int					=					name.replace('cls_token',      'vit.embeddings.cls_token'				)
			if "mask_token" in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('mask_token',      'decoder.mask_token'				)
			if "decoder_pos_embed" in name:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					name.replace('decoder_pos_embed',      'decoder.decoder_pos_embed'				)
			if "pos_embed" in name and "decoder" not in name:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					name.replace('pos_embed',      'vit.embeddings.position_embeddings'				)
			if "patch_embed.proj" in name:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					name.replace('patch_embed.proj',      'vit.embeddings.patch_embeddings.projection'				)
			if "patch_embed.norm" in name:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					name.replace('patch_embed.norm',      'vit.embeddings.norm'				)
			if "decoder_blocks" in name:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					name.replace('decoder_blocks',      'decoder.decoder_layers'				)
			if "blocks" in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('blocks',      'vit.encoder.layer'				)
			if "attn.proj" in name:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					name.replace('attn.proj',      'attention.output.dense'				)
			if "attn" in name:
						SCREAMING_SNAKE_CASE_		:     Any					=					name.replace('attn',      'attention.self'				)
			if "norm1" in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('norm1',      'layernorm_before'				)
			if "norm2" in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('norm2',      'layernorm_after'				)
			if "mlp.fc1" in name:
						SCREAMING_SNAKE_CASE_		:     Any					=					name.replace('mlp.fc1',      'intermediate.dense'				)
			if "mlp.fc2" in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('mlp.fc2',      'output.dense'				)
			if "decoder_embed" in name:
						SCREAMING_SNAKE_CASE_		:     Tuple					=					name.replace('decoder_embed',      'decoder.decoder_embed'				)
			if "decoder_norm" in name:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					name.replace('decoder_norm',      'decoder.decoder_norm'				)
			if "decoder_pred" in name:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					name.replace('decoder_pred',      'decoder.decoder_pred'				)
			if "norm.weight" in name and "decoder" not in name:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					name.replace('norm.weight',      'vit.layernorm.weight'				)
			if "norm.bias" in name and "decoder" not in name:
						SCREAMING_SNAKE_CASE_		:     str					=					name.replace('norm.bias',      'vit.layernorm.bias'				)

			return name






def 						a__			(				A__,      A__				):
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					orig_state_dict.pop(A__				)

						if "qkv" in key:
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					key.split('.'				)
									SCREAMING_SNAKE_CASE_		:     int					=					int(key_split[1]				)
									if "decoder_blocks" in key:
												SCREAMING_SNAKE_CASE_		:     List[str]					=					config.decoder_hidden_size
												SCREAMING_SNAKE_CASE_		:     Dict					=					'decoder.decoder_layers.'
												if "weight" in key:
															SCREAMING_SNAKE_CASE_		:     Tuple					=					val[:dim, :]
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					val[dim : dim * 2, :]
															SCREAMING_SNAKE_CASE_		:     List[str]					=					val[-dim:, :]
												elif "bias" in key:
															SCREAMING_SNAKE_CASE_		:     Optional[int]					=					val[:dim]
															SCREAMING_SNAKE_CASE_		:     int					=					val[dim : dim * 2]
															SCREAMING_SNAKE_CASE_		:     Tuple					=					val[-dim:]
									else:
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					config.hidden_size
												SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'vit.encoder.layer.'
												if "weight" in key:
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					val[:dim, :]
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					val[dim : dim * 2, :]
															SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					val[-dim:, :]
												elif "bias" in key:
															SCREAMING_SNAKE_CASE_		:     Tuple					=					val[:dim]
															SCREAMING_SNAKE_CASE_		:     Any					=					val[dim : dim * 2]
															SCREAMING_SNAKE_CASE_		:     Any					=					val[-dim:]

						else:
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					val

			return orig_state_dict






def 						a__			(				A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[str]					=					ViTMAEConfig()
			if "large" in checkpoint_url:
						SCREAMING_SNAKE_CASE_		:     Any					=					1_0_2_4
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					4_0_9_6
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					2_4
						SCREAMING_SNAKE_CASE_		:     List[str]					=					1_6
			elif "huge" in checkpoint_url:
						SCREAMING_SNAKE_CASE_		:     Optional[int]					=					1_4
						SCREAMING_SNAKE_CASE_		:     int					=					1_2_8_0
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					5_1_2_0
						SCREAMING_SNAKE_CASE_		:     Any					=					3_2
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					1_6

			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					ViTMAEForPreTraining(A__				)

			SCREAMING_SNAKE_CASE_		:     str					=					torch.hub.load_state_dict_from_url(A__,      map_location='cpu'				)['model']

			SCREAMING_SNAKE_CASE_		:     int					=					ViTMAEImageProcessor(size=config.image_size				)

			SCREAMING_SNAKE_CASE_		:     str					=					convert_state_dict(A__,      A__				)

			model.load_state_dict(A__				)
			model.eval()

			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'

			SCREAMING_SNAKE_CASE_		:     Any					=					Image.open(requests.get(A__,      stream=A__				).raw				)
			SCREAMING_SNAKE_CASE_		:     str					=					ViTMAEImageProcessor(size=config.image_size				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image_processor(images=A__,      return_tensors='pt'				)

			# forward pass
			torch.manual_seed(2				)
			SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					model(**A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					outputs.logits

			if "large" in checkpoint_url:
						SCREAMING_SNAKE_CASE_		:     Any					=					torch.tensor(
						    [[-0.73_09, -0.71_28, -1.01_69], [-1.01_61, -0.90_58, -1.18_78], [-1.04_78, -0.94_11, -1.19_11]]				)
			elif "huge" in checkpoint_url:
						SCREAMING_SNAKE_CASE_		:     List[Any]					=					torch.tensor(
						    [[-1.15_99, -0.91_99, -1.22_21], [-1.19_52, -0.92_69, -1.23_07], [-1.21_43, -0.93_37, -1.22_62]]				)
			else:
						SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					torch.tensor(
						    [[-0.91_92, -0.84_81, -1.12_59], [-1.13_49, -1.00_34, -1.25_99], [-1.17_57, -1.04_29, -1.27_26]]				)

			# verify logits
			assert torch.allclose(logits[0, :3, :3],      A__,      atol=1E-4				)

			print(F'''Saving model to {pytorch_dump_folder_path}'''				)
			model.save_pretrained(A__				)

			print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
			image_processor.save_pretrained(A__				)


if __name__ == "__main__":
				lowerCAmelCase__			:      str								=argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--checkpoint_url',
				    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
				    type=str,
				    help='URL of the checkpoint you\'d like to convert.',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
				)

				lowerCAmelCase__			:      str								=parser.parse_args()
				convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)







"""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
a		:       Optional[Any]     						=       False
a		:       str     						=       False



def     __magic_name__		(					UpperCamelCase     :		Namespace     )     ->						Optional[int]:
    return TrainCommand(UpperCamelCase     )







class     lowercase(_lowercase       ):



     @staticmethod
     def 			lowercase__  (							__SCREAMING_SNAKE_CASE   )		->     List[Any]:







         """simple docstring"""



         a__		=			parser.add_parser('train'     ,				help='CLI tool to train a model on a task.'   )

         train_parser.add_argument(
             '--train_data'     ,				type=__SCREAMING_SNAKE_CASE     ,				required=__SCREAMING_SNAKE_CASE     ,				help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'     ,				)
         train_parser.add_argument(
             '--column_label'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=0     ,				help='Column of the dataset csv file with example labels.'   )
         train_parser.add_argument(
             '--column_text'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1     ,				help='Column of the dataset csv file with example texts.'   )
         train_parser.add_argument(
             '--column_id'     ,				type=__SCREAMING_SNAKE_CASE     ,				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=__SCREAMING_SNAKE_CASE     ,				default=''     ,				help='path to validation dataset.'   )
         train_parser.add_argument(
             '--validation_split'     ,				type=__SCREAMING_SNAKE_CASE     ,				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=__SCREAMING_SNAKE_CASE     ,				default='./'     ,				help='path to saved the trained model.'   )

         train_parser.add_argument(
             '--task'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='text_classification'     ,				help='Task to train the model on.'   )
         train_parser.add_argument(
             '--model'     ,				type=__SCREAMING_SNAKE_CASE     ,				default='bert-base-uncased'     ,				help='Model\'s name or path to stored model.'   )
         train_parser.add_argument('--train_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3_2     ,				help='Batch size for training.'   )
         train_parser.add_argument('--valid_batch_size'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=6_4     ,				help='Batch size for validation.'   )
         train_parser.add_argument('--learning_rate'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=3e-5     ,				help='Learning rate.'   )
         train_parser.add_argument('--adam_epsilon'     ,				type=__SCREAMING_SNAKE_CASE     ,				default=1e-08     ,				help='Epsilon for Adam optimizer.'   )
         train_parser.set_defaults(func=__SCREAMING_SNAKE_CASE   )



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE   )		->     Union[str, Any]:







         """simple docstring"""



         a__		=			logging.get_logger('transformers-cli/training'   )

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

         os.makedirs(args.output     ,				exist_ok=__SCREAMING_SNAKE_CASE   )
         a__		=			args.output

         a__		=			args.column_label
         a__		=			args.column_text
         a__		=			args.column_id

         self.logger.info(f'Loading {args.task} pipeline for {args.model}'   )
         if args.task == "text_classification":
             a__		=			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}'   )
         a__		=			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     ,				)
         a__		=			None
         if args.validation_data:
             self.logger.info(f'Loading validation dataset from {args.validation_data}'   )
             a__		=			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     ,				)

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



     def 			lowercase__  (							self   )		->     Tuple:







         """simple docstring"""



         if self.framework == "tf":
             return self.run_tf()
         return self.run_torch()



     def 			lowercase__  (							self   )		->     Any:







         """simple docstring"""



         raise NotImplementedError




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         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   )


