


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__):
				_SCREAMING_SNAKE_CASE      	=SwinvaConfig()
				_SCREAMING_SNAKE_CASE      	=swinva_name.split('''_''')

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

				if model_size == "tiny":
								_SCREAMING_SNAKE_CASE      	=96
								_SCREAMING_SNAKE_CASE      	=(2, 2, 6, 2)
								_SCREAMING_SNAKE_CASE      	=(3, 6, 12, 24)
				elif model_size == "small":
								_SCREAMING_SNAKE_CASE      	=96
								_SCREAMING_SNAKE_CASE      	=(2, 2, 18, 2)
								_SCREAMING_SNAKE_CASE      	=(3, 6, 12, 24)
				elif model_size == "base":
								_SCREAMING_SNAKE_CASE      	=128
								_SCREAMING_SNAKE_CASE      	=(2, 2, 18, 2)
								_SCREAMING_SNAKE_CASE      	=(4, 8, 16, 32)
				else:
								_SCREAMING_SNAKE_CASE      	=192
								_SCREAMING_SNAKE_CASE      	=(2, 2, 18, 2)
								_SCREAMING_SNAKE_CASE      	=(6, 12, 24, 48)

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

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

				else:
								_SCREAMING_SNAKE_CASE      	=1000
								_SCREAMING_SNAKE_CASE      	='''huggingface/label-files'''
								_SCREAMING_SNAKE_CASE      	='''imagenet-1k-id2label.json'''
								_SCREAMING_SNAKE_CASE      	=json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,SCREAMING_SNAKE_CASE_						,repo_type='''dataset''')						,'''r'''))
								_SCREAMING_SNAKE_CASE      	={int(SCREAMING_SNAKE_CASE_): v for k, v in idalabel.items()}
								_SCREAMING_SNAKE_CASE      	=idalabel
								_SCREAMING_SNAKE_CASE      	={v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE      	=img_size
				_SCREAMING_SNAKE_CASE      	=num_classes
				_SCREAMING_SNAKE_CASE      	=embed_dim
				_SCREAMING_SNAKE_CASE      	=depths
				_SCREAMING_SNAKE_CASE      	=num_heads
				_SCREAMING_SNAKE_CASE      	=window_size

				return config







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

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

				return name







def     lowerCamelCase( a__						,a__):
				for key in orig_state_dict.copy().keys():
								_SCREAMING_SNAKE_CASE      	=orig_state_dict.pop(SCREAMING_SNAKE_CASE_)

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

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

				return orig_state_dict







def     lowerCamelCase( a__						,a__):
				_SCREAMING_SNAKE_CASE      	=timm.create_model(SCREAMING_SNAKE_CASE_						,pretrained=SCREAMING_SNAKE_CASE_)
				timm_model.eval()

				_SCREAMING_SNAKE_CASE      	=get_swinva_config(SCREAMING_SNAKE_CASE_)
				_SCREAMING_SNAKE_CASE      	=SwinvaForImageClassification(SCREAMING_SNAKE_CASE_)
				model.eval()

				_SCREAMING_SNAKE_CASE      	=convert_state_dict(timm_model.state_dict()						,SCREAMING_SNAKE_CASE_)
				model.load_state_dict(SCREAMING_SNAKE_CASE_)

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

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

				_SCREAMING_SNAKE_CASE      	=timm_model(inputs['''pixel_values'''])
				_SCREAMING_SNAKE_CASE      	=model(**SCREAMING_SNAKE_CASE_).logits

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

				print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}")
				model.save_pretrained(SCREAMING_SNAKE_CASE_)

				print(f"Saving image processor to {pytorch_dump_folder_path}")
				image_processor.save_pretrained(SCREAMING_SNAKE_CASE_)

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


if __name__ == "__main__":
							snake_case_    :      str				    =				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.'''
							)

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

import argparse
import os
from pathlib import Path
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params


A		: Any								=     [
    # replace left string with right string to get the relevant state_dict key (identical state dict to bart)
    ['''memory_attention''', '''encoder_attn'''],
    ['''attention''', '''attn'''],
    ['''/''', '''.'''],
    ['''.LayerNorm.gamma''', '''_layer_norm.weight'''],
    ['''.LayerNorm.beta''', '''_layer_norm.bias'''],
    ['''r.layer_''', '''r.layers.'''],
    ['''output_proj''', '''out_proj'''],
    ['''ffn.dense_1.''', '''fc2.'''],
    ['''ffn.dense.''', '''fc1.'''],
    ['''ffn_layer_norm''', '''final_layer_norm'''],
    ['''kernel''', '''weight'''],
    ['''encoder_layer_norm.''', '''encoder.layer_norm.'''],
    ['''decoder_layer_norm.''', '''decoder.layer_norm.'''],
    ['''embeddings.weights''', '''shared.weight'''],
]




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Union[str, Any]       )  ->     List[Any]:
						for pegasus_name, hf_name in PATTERNS:
												_lowercase					       =					k.replace(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						return k




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	dict		,   SCREAMING_SNAKE_CASE_	:	dict       )  ->     PegasusForConditionalGeneration:
						_lowercase					       =					DEFAULTS.copy()
						cfg_kwargs.update(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusConfig(**SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					PegasusForConditionalGeneration(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.model.state_dict()
						_lowercase					       =					{}
						for k, v in tf_weights.items():
												_lowercase					       =					rename_state_dict_key(SCREAMING_SNAKE_CASE_       )
												if new_k not in sd:
																		raise ValueError(f"""could not find new key {new_k} in state dict. (converted from {k})"""       )

												if "dense" in k or "proj" in new_k:
																		_lowercase					       =					v.T
												_lowercase					       =					torch.tensor(SCREAMING_SNAKE_CASE_		,   dtype=sd[new_k].dtype       )
												assert v.shape == sd[new_k].shape, f"""{new_k}, {k}, {v.shape}, {sd[new_k].shape}"""
						# make sure embedding.padding_idx is respected
						_lowercase					       =					torch.zeros_like(mapping["""shared.weight"""][cfg.pad_token_id + 1]       )
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					mapping["""shared.weight"""]
						_lowercase					       =					{k: torch.zeros_like(SCREAMING_SNAKE_CASE_       ) for k, v in sd.items() if k.endswith("""bias"""       ) and k not in mapping}
						mapping.update(**SCREAMING_SNAKE_CASE_       )
						_lowercase       ,						_lowercase					       =					torch_model.model.load_state_dict(SCREAMING_SNAKE_CASE_		,   strict=SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					[
						    k for k in missing if k not in ["""encoder.embed_positions.weight""", """decoder.embed_positions.weight"""]
						]
						assert unexpected_missing == [], f"""no matches found for the following torch keys {unexpected_missing}"""
						assert extra == [], f"""no matches found for the following tf keys {extra}"""
						return torch_model




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	Optional[Any]="./ckpt/aeslc/model.ckpt-32000"       )  ->     Dict:
						_lowercase					       =					tf.train.list_variables(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					{}
						_lowercase					       =					["""Adafactor""", """global_step"""]
						for name, shape in tqdm(SCREAMING_SNAKE_CASE_		,   desc="""converting tf checkpoint to dict"""       ):
												_lowercase					       =					any(pat in name for pat in ignore_name       )
												if skip_key:
																		continue
												_lowercase					       =					tf.train.load_variable(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
												_lowercase					       =					array
						return tf_weights




def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str		,   SCREAMING_SNAKE_CASE_	:	str       )  ->     str:
						# save tokenizer first
						_lowercase					       =					Path(SCREAMING_SNAKE_CASE_       ).parent.name
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]["""max_position_embeddings"""]
						_lowercase					       =					PegasusTokenizer.from_pretrained("""sshleifer/pegasus"""		,   model_max_length=SCREAMING_SNAKE_CASE_       )
						assert tok.model_max_length == desired_max_model_length
						tok.save_pretrained(SCREAMING_SNAKE_CASE_       )

						# convert model
						_lowercase					       =					get_tf_weights_as_numpy(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					task_specific_params[f"""summarization_{dataset}"""]
						if dataset == "large":
												_lowercase					       =					task_specific_params
						_lowercase					       =					convert_pegasus(SCREAMING_SNAKE_CASE_		,   SCREAMING_SNAKE_CASE_       )
						torch_model.save_pretrained(SCREAMING_SNAKE_CASE_       )
						_lowercase					       =					torch_model.state_dict()
						sd.pop("""model.decoder.embed_positions.weight"""       )
						sd.pop("""model.encoder.embed_positions.weight"""       )
						torch.save(SCREAMING_SNAKE_CASE_		,   Path(SCREAMING_SNAKE_CASE_       ) / """pytorch_model.bin"""       )


if __name__ == "__main__":
		A		: List[str]								=     argparse.ArgumentParser()
		# Required parameters
		parser.add_argument('''tf_ckpt_path''', type=str, help='''passed to tf.train.list_variables''')
		parser.add_argument('''save_dir''', default=None, type=str, help='''Path to the output PyTorch model.''')
		A		: str								=     parser.parse_args()
		if args.save_dir is None:
				A		: Tuple								=     Path(args.tf_ckpt_path).parent.name
				A		: List[Any]								=     os.path.join('''pegasus''', dataset)
		convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)