
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List

import git
import torch
from torch.utils.data import Dataset

from transformers import BartTokenizer, RagTokenizer, TaTokenizer






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     Union[str, Any]	,      snake_case__   :     Any	,      snake_case__   :     int	,      snake_case__   :     Dict=True	,      snake_case__   :     List[Any]="pt"     ):
	A						   =   {'add_prefix_space': True} if isinstance(snake_case__	,      snake_case__     ) and not line.startswith(' '     ) else {}
	A						   =   padding_side
	return tokenizer(
	    [line]	,      max_length=snake_case__	,      padding='max_length' if pad_to_max_length else None	,      truncation=snake_case__	,      return_tensors=snake_case__	,      add_special_tokens=snake_case__	,      **snake_case__	,      )






def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     Tuple	,      snake_case__   :     List[str]=None	,      ):
	A						   =   input_ids.ne(snake_case__     ).any(dim=0     )
	if attention_mask is None:
		return input_ids[:, keep_column_mask]
	else:
		return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])







class 				lowerCAmelCase_				(      a__    ):



						'''simple docstring'''


						def __init__( self     :      str  ,A_     :      List[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      Dict  ,A_     :      Tuple="train"  ,A_     :      Tuple=None  ,A_     :      Dict=None  ,A_     :      Dict=None  ,A_     :      int=""  ,)     ->   str:
							super().__init__()
							A						   =   Path(lowerCamelCase_      ).joinpath(type_path + '.source'      )
							A						   =   Path(lowerCamelCase_      ).joinpath(type_path + '.target'      )
							A						   =   self.get_char_lens(self.src_file      )
							A						   =   max_source_length
							A						   =   max_target_length
							assert min(self.src_lens      ) > 0, F'found empty line in {self.src_file}'
							A						   =   tokenizer
							A						   =   prefix
							if n_obs is not None:
								A						   =   self.src_lens[:n_obs]
							A						   =   src_lang
							A						   =   tgt_lang


						def __len__( self     :      Any      )     ->   Tuple:
							return len(self.src_lens      )


						def __getitem__( self     :      Optional[Any]  ,A_     :      Union[str, Any]      )     ->   Dict[str, torch.Tensor]:
							A						   =   index + 1  # linecache starts at 1
							A						   =   self.prefix + linecache.getline(str(self.src_file      )  ,lowerCamelCase_      ).rstrip('\n'      )
							A						   =   linecache.getline(str(self.tgt_file      )  ,lowerCamelCase_      ).rstrip('\n'      )
							assert source_line, F'empty source line for index {index}'
							assert tgt_line, F'empty tgt line for index {index}'

							# Need to add eos token manually for T5
							if isinstance(self.tokenizer  ,lowerCamelCase_      ):
								source_line += self.tokenizer.eos_token
								tgt_line += self.tokenizer.eos_token

							# Pad source and target to the right
							A						   =   (
							    self.tokenizer.question_encoder if isinstance(self.tokenizer  ,lowerCamelCase_      ) else self.tokenizer
							)
							A						   =   self.tokenizer.generator if isinstance(self.tokenizer  ,lowerCamelCase_      ) else self.tokenizer

							A						   =   encode_line(lowerCamelCase_  ,lowerCamelCase_  ,self.max_source_length  ,'right'      )
							A						   =   encode_line(lowerCamelCase_  ,lowerCamelCase_  ,self.max_target_length  ,'right'      )

							A						   =   source_inputs['input_ids'].squeeze()
							A						   =   target_inputs['input_ids'].squeeze()
							A						   =   source_inputs['attention_mask'].squeeze()
							return {
							    "input_ids": source_ids,
							    "attention_mask": src_mask,
							    "decoder_input_ids": target_ids,
							}


						@staticmethod
						def 						_SCREAMING_SNAKE_CASE      ( A_     :      Union[str, Any]      )     ->   Dict:
							return [len(lowerCamelCase_      ) for x in Path(lowerCamelCase_      ).open().readlines()]




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]  ,A_     :      str      )     ->   Dict[str, torch.Tensor]:
							A						   =   torch.stack([x['input_ids'] for x in batch]      )
							A						   =   torch.stack([x['attention_mask'] for x in batch]      )
							A						   =   torch.stack([x['decoder_input_ids'] for x in batch]      )
							A						   =   (
							    self.tokenizer.generator.pad_token_id
							    if isinstance(self.tokenizer  ,lowerCamelCase_      )
							    else self.tokenizer.pad_token_id
							)
							A						   =   (
							    self.tokenizer.question_encoder.pad_token_id
							    if isinstance(self.tokenizer  ,lowerCamelCase_      )
							    else self.tokenizer.pad_token_id
							)
							A						   =   trim_batch(lowerCamelCase_  ,lowerCamelCase_      )
							A   ,      A						   =   trim_batch(lowerCamelCase_  ,lowerCamelCase_  ,attention_mask=lowerCamelCase_      )
							A						   =   {
							    'input_ids': source_ids,
							    'attention_mask': source_mask,
							    'decoder_input_ids': y,
							}
							return batch

_lowercase							   =				getLogger(__name__)






def       _snake_case				(		snake_case__   :     Dict     ):
	return list(itertools.chain.from_iterable(snake_case__     )     )






def       _snake_case				(		snake_case__   :     Optional[int]     ):
	A						   =   get_git_info()
	save_json(snake_case__	,      os.path.join(snake_case__	,      'git_log.json'     )     )






def       _snake_case				(		snake_case__   :     Any	,      snake_case__   :     Optional[Any]	,      snake_case__   :     Union[str, Any]=4	,      **snake_case__   :     List[str]     ):
	with open(snake_case__	,      'w'     ) as f:
		json.dump(snake_case__	,      snake_case__	,      indent=snake_case__	,      **snake_case__     )






def       _snake_case				(		snake_case__   :     List[str]     ):
	with open(snake_case__     ) as f:
		return json.load(snake_case__     )






def       _snake_case				(		):
	A						   =   git.Repo(search_parent_directories=snake_case__     )
	A						   =   {
	    'repo_id': str(snake_case__     ),
	    'repo_sha': str(repo.head.object.hexsha     ),
	    'repo_branch': str(repo.active_branch     ),
	    'hostname': str(socket.gethostname()     ),
	}
	return repo_infos






def       _snake_case				(		snake_case__   :     List[str]	,      snake_case__   :     Any     ):
	return list(map(snake_case__	,      snake_case__     )     )






def       _snake_case				(		snake_case__   :     Union[str, Any]	,      snake_case__   :     List[str]     ):
	with open(snake_case__	,      'wb'     ) as f:
		return pickle.dump(snake_case__	,      snake_case__     )






def       _snake_case				(		snake_case__   :     Dict     ):

	def remove_articles(snake_case__   :     Optional[Any]     ):
		return re.sub(r'\b(a|an|the)\b'	,      ' '	,      snake_case__     )

	def white_space_fix(snake_case__   :     Union[str, Any]     ):
		return " ".join(text.split()     )

	def remove_punc(snake_case__   :     int     ):
		A						   =   set(string.punctuation     )
		return "".join(ch for ch in text if ch not in exclude     )

	def lower(snake_case__   :     Union[str, Any]     ):
		return text.lower()

	return white_space_fix(remove_articles(remove_punc(lower(snake_case__     )     )     )     )






def       _snake_case				(		snake_case__   :     Dict	,      snake_case__   :     List[Any]     ):
	A						   =   normalize_answer(snake_case__     ).split()
	A						   =   normalize_answer(snake_case__     ).split()
	A						   =   Counter(snake_case__     ) & Counter(snake_case__     )
	A						   =   sum(common.values()     )
	if num_same == 0:
		return 0
	A						   =   1.0 * num_same / len(snake_case__     )
	A						   =   1.0 * num_same / len(snake_case__     )
	A						   =   (2 * precision * recall) / (precision + recall)
	return fa






def       _snake_case				(		snake_case__   :     str	,      snake_case__   :     Optional[int]     ):
	return normalize_answer(snake_case__     ) == normalize_answer(snake_case__     )






def       _snake_case				(		snake_case__   :     List[str]	,      snake_case__   :     str     ):
	assert len(snake_case__     ) == len(snake_case__     )
	A						   =   0
	for hypo, pred in zip(snake_case__	,      snake_case__     ):
		em += exact_match_score(snake_case__	,      snake_case__     )
	if len(snake_case__     ) > 0:
		em /= len(snake_case__     )
	return {"em": em}






def       _snake_case				(		snake_case__   :     Any     ):
	return model_prefix.startswith('rag'     )






def       _snake_case				(		snake_case__   :     Optional[Any]	,      snake_case__   :     Optional[int]	,      snake_case__   :     str     ):
	A						   =   {p: p for p in extra_params}
	# T5 models don't have `dropout` param, they have `dropout_rate` instead
	A						   =   'dropout_rate'
	for p in extra_params:
		if getattr(snake_case__	,      snake_case__	,      snake_case__     ):
			if not hasattr(snake_case__	,      snake_case__     ) and not hasattr(snake_case__	,      equivalent_param[p]     ):
				logger.info('config doesn\'t have a `{}` attribute'.format(snake_case__     )     )
				delattr(snake_case__	,      snake_case__     )
				continue
			A						   =   p if hasattr(snake_case__	,      snake_case__     ) else equivalent_param[p]
			setattr(snake_case__	,      snake_case__	,      getattr(snake_case__	,      snake_case__     )     )
			delattr(snake_case__	,      snake_case__     )
	return hparams, config

"""simple docstring"""


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       _snake_case				(		snake_case__   :     int     ):
	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						   =   96
		A						   =   (2, 2, 6, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "small":
		A						   =   96
		A						   =   (2, 2, 18, 2)
		A						   =   (3, 6, 12, 24)
	elif model_size == "base":
		A						   =   128
		A						   =   (2, 2, 18, 2)
		A						   =   (4, 8, 16, 32)
	else:
		A						   =   192
		A						   =   (2, 2, 18, 2)
		A						   =   (6, 12, 24, 48)

	if "to" in swinva_name:
		A						   =   (12, 12, 12, 6)

	if ("22k" in swinva_name) and ("to" not in swinva_name):
		A						   =   2_1841
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-22k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): v for k, v in idalabel.items()}
		A						   =   idalabel
		A						   =   {v: k for k, v in idalabel.items()}

	else:
		A						   =   1000
		A						   =   'huggingface/label-files'
		A						   =   'imagenet-1k-id2label.json'
		A						   =   json.load(open(hf_hub_download(snake_case__	,      snake_case__	,      repo_type='dataset'     )	,      'r'     )     )
		A						   =   {int(snake_case__     ): 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       _snake_case				(		snake_case__   :     List[Any]     ):
	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       _snake_case				(		snake_case__   :     List[Any]	,      snake_case__   :     List[Any]     ):
	for key in orig_state_dict.copy().keys():
		A						   =   orig_state_dict.pop(snake_case__     )

		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       _snake_case				(		snake_case__   :     Optional[int]	,      snake_case__   :     Tuple     ):
	A						   =   timm.create_model(snake_case__	,      pretrained=snake_case__     )
	timm_model.eval()

	A						   =   get_swinva_config(snake_case__     )
	A						   =   SwinvaForImageClassification(snake_case__     )
	model.eval()

	A						   =   convert_state_dict(timm_model.state_dict()	,      snake_case__     )
	model.load_state_dict(snake_case__     )

	A						   =   'http://images.cocodataset.org/val2017/000000039769.jpg'

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

	A						   =   timm_model(inputs['pixel_values']     )
	A						   =   model(**snake_case__     ).logits

	assert torch.allclose(snake_case__	,      snake_case__	,      atol=1e-3     )

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

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

	model.push_to_hub(
	    repo_path_or_name=Path(snake_case__	,      snake_case__     )	,      organization='nandwalritik'	,      commit_message='Add model'	,      )


if __name__ == "__main__":
		_lowercase							   =				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.'''
		)

		_lowercase							   =				parser.parse_args()
		convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)