





import argparse
import json
from pathlib import Path

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

from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification







def    __lowerCamelCase					(							__a   :int    )		->				Tuple:






		"""simple docstring"""

		A__        =   SwinvaConfig()
		A__        =   swinva_name.split("""_"""    )

		A__        =   name_split[1]
		if "to" in name_split[3]:
				A__        =   int(name_split[3][-3:]    )
		else:
				A__        =   int(name_split[3]    )
		if "to" in name_split[2]:
				A__        =   int(name_split[2][-2:]    )
		else:
				A__        =   int(name_split[2][6:]    )

		if model_size == "tiny":
				A__        =   9_6
				A__        =   (2, 2, 6, 2)
				A__        =   (3, 6, 1_2, 2_4)
		elif model_size == "small":
				A__        =   9_6
				A__        =   (2, 2, 1_8, 2)
				A__        =   (3, 6, 1_2, 2_4)
		elif model_size == "base":
				A__        =   1_2_8
				A__        =   (2, 2, 1_8, 2)
				A__        =   (4, 8, 1_6, 3_2)
		else:
				A__        =   1_9_2
				A__        =   (2, 2, 1_8, 2)
				A__        =   (6, 1_2, 2_4, 4_8)

		if "to" in swinva_name:
				A__        =   (1_2, 1_2, 1_2, 6)

		if ("22k" in swinva_name) and ("to" not in swinva_name):
				A__        =   2_1_8_4_1
				A__        =   """huggingface/label-files"""
				A__        =   """imagenet-22k-id2label.json"""
				A__        =   json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type="""dataset"""    )    ,     """r"""    )    )
				A__        =   {int(__a    ): v for k, v in idalabel.items()}
				A__        =   idalabel
				A__        =   {v: k for k, v in idalabel.items()}

		else:
				A__        =   1_0_0_0
				A__        =   """huggingface/label-files"""
				A__        =   """imagenet-1k-id2label.json"""
				A__        =   json.load(open(hf_hub_download(__a    ,     __a    ,     repo_type="""dataset"""    )    ,     """r"""    )    )
				A__        =   {int(__a    ): v for k, v in idalabel.items()}
				A__        =   idalabel
				A__        =   {v: k for k, v in idalabel.items()}

		A__        =   img_size
		A__        =   num_classes
		A__        =   embed_dim
		A__        =   depths
		A__        =   num_heads
		A__        =   window_size

		return config







def    __lowerCamelCase					(							__a   :List[str]    )		->				Tuple:






		"""simple docstring"""

		if "patch_embed.proj" in name:
				A__        =   name.replace("""patch_embed.proj"""    ,     """embeddings.patch_embeddings.projection"""    )
		if "patch_embed.norm" in name:
				A__        =   name.replace("""patch_embed.norm"""    ,     """embeddings.norm"""    )
		if "layers" in name:
				A__        =   """encoder.""" + name
		if "attn.proj" in name:
				A__        =   name.replace("""attn.proj"""    ,     """attention.output.dense"""    )
		if "attn" in name:
				A__        =   name.replace("""attn"""    ,     """attention.self"""    )
		if "norm1" in name:
				A__        =   name.replace("""norm1"""    ,     """layernorm_before"""    )
		if "norm2" in name:
				A__        =   name.replace("""norm2"""    ,     """layernorm_after"""    )
		if "mlp.fc1" in name:
				A__        =   name.replace("""mlp.fc1"""    ,     """intermediate.dense"""    )
		if "mlp.fc2" in name:
				A__        =   name.replace("""mlp.fc2"""    ,     """output.dense"""    )
		if "q_bias" in name:
				A__        =   name.replace("""q_bias"""    ,     """query.bias"""    )
		if "k_bias" in name:
				A__        =   name.replace("""k_bias"""    ,     """key.bias"""    )
		if "v_bias" in name:
				A__        =   name.replace("""v_bias"""    ,     """value.bias"""    )
		if "cpb_mlp" in name:
				A__        =   name.replace("""cpb_mlp"""    ,     """continuous_position_bias_mlp"""    )
		if name == "norm.weight":
				A__        =   """layernorm.weight"""
		if name == "norm.bias":
				A__        =   """layernorm.bias"""

		if "head" in name:
				A__        =   name.replace("""head"""    ,     """classifier"""    )
		else:
				A__        =   """swinv2.""" + name

		return name







def    __lowerCamelCase					(							__a   :Tuple    ,     __a   :List[str]    )		->				int:






		"""simple docstring"""

		for key in orig_state_dict.copy().keys():
				A__        =   orig_state_dict.pop(__a    )

				if "mask" in key:
						continue
				elif "qkv" in key:
						A__        =   key.split("""."""    )
						A__        =   int(key_split[1]    )
						A__        =   int(key_split[3]    )
						A__        =   model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

						if "weight" in key:
								A__        =   val[:dim, :]
								A__        =   val[dim : dim * 2, :]
								A__        =   val[-dim:, :]
						else:
								A__        =   val[:dim]
								A__        =   val[
								    dim : dim * 2
								]
								A__        =   val[-dim:]
				else:
						A__        =   val

		return orig_state_dict







def    __lowerCamelCase					(							__a   :str    ,     __a   :Optional[Any]    )		->				Dict:






		"""simple docstring"""

		A__        =   timm.create_model(__a    ,     pretrained=__a    )
		timm_model.eval()

		A__        =   get_swinva_config(__a    )
		A__        =   SwinvaForImageClassification(__a    )
		model.eval()

		A__        =   convert_state_dict(timm_model.state_dict()    ,     __a    )
		model.load_state_dict(__a    )

		A__        =   """http://images.cocodataset.org/val2017/000000039769.jpg"""

		A__        =   AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swinva_name.replace("""_"""    ,     """-"""    )    )    )
		A__        =   Image.open(requests.get(__a    ,     stream=__a    ).raw    )
		A__        =   image_processor(images=__a    ,     return_tensors="""pt"""    )

		A__        =   timm_model(inputs["""pixel_values"""]    )
		A__        =   model(**__a    ).logits

		assert torch.allclose(__a    ,     __a    ,     atol=1E-3    )

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

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

		model.push_to_hub(
		    repo_path_or_name=Path(__a    ,     __a    )    ,     organization="""nandwalritik"""    ,     commit_message="""Add model"""    ,     )


if __name__ == "__main__":
					A    :					Tuple =      argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--swinv2_name''',
					    default='''swinv2_tiny_patch4_window8_256''',
					    type=str,
					    help='''Name of the Swinv2 timm model you\'d like to convert.''',
					)
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
					)

					A    :					Optional[int] =      parser.parse_args()
					convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)










import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
A    :					Tuple =      logging.getLogger(__name__)







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[str]    )		->				Tuple:






		"""simple docstring"""

		A__        =   np.argmax(__a    ,     axis=1    )
		return np.sum(outputs == labels    )







def    __lowerCamelCase					(							__a   :Tuple    )		->				Dict:






		"""simple docstring"""

		with open(__a    ,     encoding="""utf_8"""    ) as f:
				A__        =   csv.reader(__a    )
				A__        =   []
				next(__a    )  # skip the first line
				for line in tqdm(__a    ):
						output.append((""" """.join(line[1:5]    ), line[5], line[6], int(line[-1]    ) - 1)    )
		return output







def    __lowerCamelCase					(							__a   :Optional[int]    ,     __a   :List[Any]    ,     __a   :Dict    ,     __a   :Optional[Any]    ,     __a   :Optional[Any]    ,     __a   :int    )		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   []
		for dataset in encoded_datasets:
				A__        =   len(__a    )
				A__        =   np.zeros((n_batch, 2, input_len)    ,     dtype=np.intaa    )
				A__        =   np.zeros((n_batch, 2)    ,     dtype=np.intaa    )
				A__        =   np.full((n_batch, 2, input_len)    ,     fill_value=-1_0_0    ,     dtype=np.intaa    )
				A__        =   np.zeros((n_batch,)    ,     dtype=np.intaa    )
				for (
				    i,
				    (story, conta, conta, mc_label),
				) in enumerate(__a    ):
						A__        =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						A__        =   [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
						A__        =   with_conta
						A__        =   with_conta
						A__        =   len(__a    ) - 1
						A__        =   len(__a    ) - 1
						A__        =   with_conta
						A__        =   with_conta
						A__        =   mc_label
				A__        =   (input_ids, mc_token_ids, lm_labels, mc_labels)
				tensor_datasets.append(tuple(torch.tensor(__a    ) for t in all_inputs    )    )
		return tensor_datasets







def    __lowerCamelCase					(							)		->				Union[str, Any]:






		"""simple docstring"""

		A__        =   argparse.ArgumentParser()
		parser.add_argument("""--model_name"""    ,     type=__a    ,     default="""openai-gpt"""    ,     help="""pretrained model name"""    )
		parser.add_argument("""--do_train"""    ,     action="""store_true"""    ,     help="""Whether to run training."""    )
		parser.add_argument("""--do_eval"""    ,     action="""store_true"""    ,     help="""Whether to run eval on the dev set."""    )
		parser.add_argument(
		    """--output_dir"""    ,     default=__a    ,     type=__a    ,     required=__a    ,     help="""The output directory where the model predictions and checkpoints will be written."""    ,     )
		parser.add_argument("""--train_dataset"""    ,     type=__a    ,     default=""""""    )
		parser.add_argument("""--eval_dataset"""    ,     type=__a    ,     default=""""""    )
		parser.add_argument("""--seed"""    ,     type=__a    ,     default=4_2    )
		parser.add_argument("""--num_train_epochs"""    ,     type=__a    ,     default=3    )
		parser.add_argument("""--train_batch_size"""    ,     type=__a    ,     default=8    )
		parser.add_argument("""--eval_batch_size"""    ,     type=__a    ,     default=1_6    )
		parser.add_argument("""--adam_epsilon"""    ,     default=1E-8    ,     type=__a    ,     help="""Epsilon for Adam optimizer."""    )
		parser.add_argument("""--max_grad_norm"""    ,     type=__a    ,     default=1    )
		parser.add_argument(
		    """--max_steps"""    ,     default=-1    ,     type=__a    ,     help=(
		        """If > 0: set total number of training                         steps to perform. Override num_train_epochs."""
		    )    ,     )
		parser.add_argument(
		    """--gradient_accumulation_steps"""    ,     type=__a    ,     default=1    ,     help="""Number of updates steps to accumulate before                        performing a backward/update pass."""    ,     )
		parser.add_argument("""--learning_rate"""    ,     type=__a    ,     default=6.25E-5    )
		parser.add_argument("""--warmup_steps"""    ,     default=0    ,     type=__a    ,     help="""Linear warmup over warmup_steps."""    )
		parser.add_argument("""--lr_schedule"""    ,     type=__a    ,     default="""warmup_linear"""    )
		parser.add_argument("""--weight_decay"""    ,     type=__a    ,     default=0.01    )
		parser.add_argument("""--lm_coef"""    ,     type=__a    ,     default=0.9    )
		parser.add_argument("""--n_valid"""    ,     type=__a    ,     default=3_7_4    )

		parser.add_argument("""--server_ip"""    ,     type=__a    ,     default=""""""    ,     help="""Can be used for distant debugging."""    )
		parser.add_argument("""--server_port"""    ,     type=__a    ,     default=""""""    ,     help="""Can be used for distant debugging."""    )
		A__        =   parser.parse_args()
		print(__a    )

		if args.server_ip and args.server_port:
				# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
				import ptvsd

				print("""Waiting for debugger attach"""    )
				ptvsd.enable_attach(address=(args.server_ip, args.server_port)    ,     redirect_output=__a    )
				ptvsd.wait_for_attach()

		random.seed(args.seed    )
		np.random.seed(args.seed    )
		torch.manual_seed(args.seed    )
		torch.cuda.manual_seed_all(args.seed    )

		A__        =   torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""    )
		A__        =   torch.cuda.device_count()
		logger.info("""device: {}, n_gpu {}""".format(__a    ,     __a    )    )

		if not args.do_train and not args.do_eval:
				raise ValueError("""At least one of `do_train` or `do_eval` must be True."""    )

		if not os.path.exists(args.output_dir    ):
				os.makedirs(args.output_dir    )

		# Load tokenizer and model
		# This loading functions also add new tokens and embeddings called `special tokens`
		# These new embeddings will be fine-tuned on the RocStories dataset
		A__        =   ["""_start_""", """_delimiter_""", """_classify_"""]
		A__        =   OpenAIGPTTokenizer.from_pretrained(args.model_name    )
		tokenizer.add_tokens(__a    )
		A__        =   tokenizer.convert_tokens_to_ids(__a    )
		A__        =   OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name    )
		model.resize_token_embeddings(len(__a    )    )
		model.to(__a    )

		# Load and encode the datasets
		def tokenize_and_encode(__a   :Tuple    ):
				if isinstance(__a    ,     __a    ):
						return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(__a    )    )
				elif isinstance(__a    ,     __a    ):
						return obj
				return [tokenize_and_encode(__a    ) for o in obj]

		logger.info("""Encoding dataset..."""    )
		A__        =   load_rocstories_dataset(args.train_dataset    )
		A__        =   load_rocstories_dataset(args.eval_dataset    )
		A__        =   (train_dataset, eval_dataset)
		A__        =   tokenize_and_encode(__a    )

		# Compute the max input length for the Transformer
		A__        =   model.config.n_positions // 2 - 2
		A__        =   max(
		    len(story[:max_length]    ) + max(len(conta[:max_length]    )    ,     len(conta[:max_length]    )    ) + 3
		    for dataset in encoded_datasets
		    for story, conta, conta, _ in dataset    )
		A__        =   min(__a    ,     model.config.n_positions    )  # Max size of input for the pre-trained model

		# Prepare inputs tensors and dataloaders
		A__        =   pre_process_datasets(__a    ,     __a    ,     __a    ,     *__a    )
		A__      ,			A__        =   tensor_datasets[0], tensor_datasets[1]

		A__        =   TensorDataset(*__a    )
		A__        =   RandomSampler(__a    )
		A__        =   DataLoader(__a    ,     sampler=__a    ,     batch_size=args.train_batch_size    )

		A__        =   TensorDataset(*__a    )
		A__        =   SequentialSampler(__a    )
		A__        =   DataLoader(__a    ,     sampler=__a    ,     batch_size=args.eval_batch_size    )

		# Prepare optimizer
		if args.do_train:
				if args.max_steps > 0:
						A__        =   args.max_steps
						A__        =   args.max_steps // (len(__a    ) // args.gradient_accumulation_steps) + 1
				else:
						A__        =   len(__a    ) // args.gradient_accumulation_steps * args.num_train_epochs

				A__        =   list(model.named_parameters()    )
				A__        =   ["""bias""", """LayerNorm.bias""", """LayerNorm.weight"""]
				A__        =   [
				    {
				        """params""": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay    )],
				        """weight_decay""": args.weight_decay,
				    },
				    {"""params""": [p for n, p in param_optimizer if any(nd in n for nd in no_decay    )], """weight_decay""": 0.0},
				]
				A__        =   AdamW(__a    ,     lr=args.learning_rate    ,     eps=args.adam_epsilon    )
				A__        =   get_linear_schedule_with_warmup(
				    __a    ,     num_warmup_steps=args.warmup_steps    ,     num_training_steps=__a    )

		if args.do_train:
				A__      ,			A__      ,			A__        =   0, 0, None
				model.train()
				for _ in trange(int(args.num_train_epochs    )    ,     desc="""Epoch"""    ):
						A__        =   0
						A__        =   0
						A__        =   tqdm(__a    ,     desc="""Training"""    )
						for step, batch in enumerate(__a    ):
								A__        =   tuple(t.to(__a    ) for t in batch    )
								A__      ,			A__      ,			A__      ,			A__        =   batch
								A__        =   model(__a    ,     mc_token_ids=__a    ,     lm_labels=__a    ,     mc_labels=__a    )
								A__        =   args.lm_coef * losses[0] + losses[1]
								loss.backward()
								optimizer.step()
								scheduler.step()
								optimizer.zero_grad()
								tr_loss += loss.item()
								A__        =   (
								    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
								)
								nb_tr_steps += 1
								A__        =   """Training loss: {:.2e} lr: {:.2e}""".format(__a    ,     scheduler.get_lr()[0]    )

    # Save a trained model
		if args.do_train:
				# Save a trained model, configuration and tokenizer
				A__        =   model.module if hasattr(__a    ,     """module"""    ) else model  # Only save the model itself

				# If we save using the predefined names, we can load using `from_pretrained`
				A__        =   os.path.join(args.output_dir    ,     __a    )
				A__        =   os.path.join(args.output_dir    ,     __a    )

				torch.save(model_to_save.state_dict()    ,     __a    )
				model_to_save.config.to_json_file(__a    )
				tokenizer.save_vocabulary(args.output_dir    )

				# Load a trained model and vocabulary that you have fine-tuned
				A__        =   OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir    )
				A__        =   OpenAIGPTTokenizer.from_pretrained(args.output_dir    )
				model.to(__a    )

		if args.do_eval:
				model.eval()
				A__      ,			A__        =   0, 0
				A__      ,			A__        =   0, 0
				for batch in tqdm(__a    ,     desc="""Evaluating"""    ):
						A__        =   tuple(t.to(__a    ) for t in batch    )
						A__      ,			A__      ,			A__      ,			A__        =   batch
						with torch.no_grad():
								A__      ,			A__      ,			A__      ,			A__        =   model(
								    __a    ,     mc_token_ids=__a    ,     lm_labels=__a    ,     mc_labels=__a    )

						A__        =   mc_logits.detach().cpu().numpy()
						A__        =   mc_labels.to("""cpu"""    ).numpy()
						A__        =   accuracy(__a    ,     __a    )

						eval_loss += mc_loss.mean().item()
						eval_accuracy += tmp_eval_accuracy

						nb_eval_examples += input_ids.size(0    )
						nb_eval_steps += 1

				A__        =   eval_loss / nb_eval_steps
				A__        =   eval_accuracy / nb_eval_examples
				A__        =   tr_loss / nb_tr_steps if args.do_train else None
				A__        =   {"""eval_loss""": eval_loss, """eval_accuracy""": eval_accuracy, """train_loss""": train_loss}

				A__        =   os.path.join(args.output_dir    ,     """eval_results.txt"""    )
				with open(__a    ,     """w"""    ) as writer:
						logger.info("""***** Eval results *****"""    )
						for key in sorted(result.keys()    ):
								logger.info("""  %s = %s"""    ,     __a    ,     str(result[key]    )    )
								writer.write("""%s = %s\n""" % (key, str(result[key]    ))    )


if __name__ == "__main__":
					main()



