




"""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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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






def       lowerCamelCase					(a_						:List[Any]				,				a_						:int=False)     ->							Union[str, Any]:
       lowercase		:Any       							=				[]

       # fmt: off
       # stem:
       rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token'''))
       rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings'''))

       rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''))
       rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''))

       # backbone
       rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight'''))
       rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight'''))
       rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias'''))

       for stage_idx in range(len(config.backbone_config.depths)):
              for layer_idx in range(config.backbone_config.depths[stage_idx]):
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight"""))
                     rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias"""))

              rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight"""))
              rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight"""))
              rename_keys.append((F"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", F"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias"""))

       # transformer encoder
       for i in range(config.num_hidden_layers):
              # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
              rename_keys.append((F"""blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight"""))
              rename_keys.append((F"""blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias"""))
              rename_keys.append((F"""blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight"""))
              rename_keys.append((F"""blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias"""))
              rename_keys.append((F"""blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight"""))
              rename_keys.append((F"""blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias"""))
              rename_keys.append((F"""blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight"""))
              rename_keys.append((F"""blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias"""))
              rename_keys.append((F"""blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight"""))
              rename_keys.append((F"""blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias"""))

       if base_model:
              # layernorm + pooler
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''layernorm.weight'''),
                      ('''norm.bias''', '''layernorm.bias'''),
                      ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                      ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
                  ])

              # if just the base model, we should remove "vit" from all keys that start with "vit"
              lowercase		:str       							=				[(pair[0], pair[1][4:]) if pair[1].startswith('''vit''') else pair for pair in rename_keys]
       else:
              # layernorm + classification head
              rename_keys.extend(
                  [
                      ('''norm.weight''', '''vit.layernorm.weight'''),
                      ('''norm.bias''', '''vit.layernorm.bias'''),
                      ('''head.weight''', '''classifier.weight'''),
                      ('''head.bias''', '''classifier.bias'''),
                  ])
       # fmt: on

       return rename_keys






def       lowerCamelCase					(a_						:Tuple				,				a_						:Optional[int]				,				a_						:str=False)     ->							Dict:
       for i in range(config.num_hidden_layers):
              if base_model:
                     lowercase		:int       							=				""
              else:
                     lowercase		:Dict       							=				"vit."
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              lowercase		:int       							=				state_dict.pop(F"""blocks.{i}.attn.qkv.weight""")
              lowercase		:Any       							=				state_dict.pop(F"""blocks.{i}.attn.qkv.bias""")
              # next, add query, keys and values (in that order) to the state dict
              lowercase		:str       							=				in_proj_weight[
                  : config.hidden_size, :
              ]
              lowercase		:int       							=				in_proj_bias[: config.hidden_size]
              lowercase		:Optional[Any]       							=				in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              lowercase		:Any       							=				in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              lowercase		:Tuple       							=				in_proj_weight[
                  -config.hidden_size :, :
              ]
              lowercase		:Any       							=				in_proj_bias[-config.hidden_size :]






def       lowerCamelCase					(a_						:List[str])     ->							Tuple:
       lowercase		:str       							=				["head.weight", "head.bias"]
       for k in ignore_keys:
              state_dict.pop(_lowerCAmelCase				,				_lowerCAmelCase)






def       lowerCamelCase					(a_						:Tuple				,				a_						:int				,				a_						:List[str])     ->							Any:
       lowercase		:Any       							=				dct.pop(_lowerCAmelCase)
       lowercase		:Union[str, Any]       							=				val






def       lowerCamelCase					()     ->							Any:
       lowercase		:Optional[int]       							=				"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowercase		:str       							=				Image.open(requests.get(_lowerCAmelCase				,				stream=_lowerCAmelCase).raw)
       return im






@torch.no_grad()
def       lowerCamelCase					(a_						:Union[str, Any]				,				a_						:Any				,				a_						:Dict=False)     ->							List[str]:
       lowercase		:Any       							=				BitConfig(
           global_padding='''same'''				,				layer_type='''bottleneck'''				,				depths=(3, 4, 9)				,				out_features=['''stage3''']				,				embedding_dynamic_padding=_lowerCAmelCase				,				)
       lowercase		:List[Any]       							=				ViTHybridConfig(backbone_config=_lowerCAmelCase				,				image_size=384				,				num_labels=1000)
       lowercase		:Tuple       							=				False

       # load original model from timm
       lowercase		:int       							=				timm.create_model(_lowerCAmelCase				,				pretrained=_lowerCAmelCase)
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       lowercase		:Optional[Any]       							=				timm_model.state_dict()
       if base_model:
              remove_classification_head_(_lowerCAmelCase)
       lowercase		:Any       							=				create_rename_keys(_lowerCAmelCase				,				_lowerCAmelCase)
       for src, dest in rename_keys:
              rename_key(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase)
       read_in_q_k_v(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase)

       lowercase		:Union[str, Any]       							=				"huggingface/label-files"
       lowercase		:List[str]       							=				"imagenet-1k-id2label.json"
       lowercase		:int       							=				json.load(open(hf_hub_download(_lowerCAmelCase				,				_lowerCAmelCase				,				repo_type='''dataset''')				,				'''r'''))
       lowercase		:List[Any]       							=				{int(_lowerCAmelCase): v for k, v in idalabel.items()}
       lowercase		:Tuple       							=				idalabel
       lowercase		:Optional[Any]       							=				{v: k for k, v in idalabel.items()}

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              lowercase		:Optional[int]       							=				ViTHybridModel(_lowerCAmelCase).eval()
       else:
              lowercase		:Any       							=				ViTHybridForImageClassification(_lowerCAmelCase).eval()
       model.load_state_dict(_lowerCAmelCase)

       # create image processor
       lowercase		:Dict       							=				create_transform(**resolve_data_config({}				,				model=_lowerCAmelCase))
       lowercase		:Union[str, Any]       							=				transform.transforms

       lowercase		:List[Any]       							=				{
           "bilinear": PILImageResampling.BILINEAR,
           "bicubic": PILImageResampling.BICUBIC,
           "nearest": PILImageResampling.NEAREST,
       }

       lowercase		:Tuple       							=				ViTHybridImageProcessor(
           do_resize=_lowerCAmelCase				,				size={'''shortest_edge''': timm_transforms[0].size}				,				resample=pillow_resamplings[timm_transforms[0].interpolation.value]				,				do_center_crop=_lowerCAmelCase				,				crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}				,				do_normalize=_lowerCAmelCase				,				image_mean=timm_transforms[-1].mean.tolist()				,				image_std=timm_transforms[-1].std.tolist()				,				)

       lowercase		:Tuple       							=				prepare_img()
       lowercase		:Dict       							=				transform(_lowerCAmelCase).unsqueeze(0)
       lowercase		:Tuple       							=				processor(_lowerCAmelCase				,				return_tensors='''pt''').pixel_values

       # verify pixel values
       assert torch.allclose(_lowerCAmelCase				,				_lowerCAmelCase)

       # verify logits
       with torch.no_grad():
              lowercase		:Optional[Any]       							=				model(_lowerCAmelCase)
              lowercase		:str       							=				outputs.logits

       print('''Predicted class:'''				,				logits.argmax(-1).item())
       if base_model:
              lowercase		:Union[str, Any]       							=				timm_model.forward_features(_lowerCAmelCase)
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(_lowerCAmelCase				,				outputs.pooler_output				,				atol=1E-3)
       else:
              lowercase		:List[str]       							=				timm_model(_lowerCAmelCase)
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(_lowerCAmelCase				,				outputs.logits				,				atol=1E-3)
       print('''Looks ok!''')

       if pytorch_dump_folder_path is not None:
              Path(_lowerCAmelCase).mkdir(exist_ok=_lowerCAmelCase)
              print(F"""Saving model {vit_name} to {pytorch_dump_folder_path}""")
              model.save_pretrained(_lowerCAmelCase)
              print(F"""Saving processor to {pytorch_dump_folder_path}""")
              processor.save_pretrained(_lowerCAmelCase)

       if push_to_hub:
              print(F"""Pushing model and processor to the hub {vit_name}""")
              model.push_to_hub(F"""ybelkada/{vit_name}""")
              processor.push_to_hub(F"""ybelkada/{vit_name}""")


if __name__ == "__main__":
 UpperCAmelCase      		=			argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--vit_name''',
     default='''vit_base_r50_s16_384''',
     type=str,
     help='''Name of the hybrid ViT 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.'''
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether to upload the model to the HuggingFace hub.'''
 )

 UpperCAmelCase      		=			parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""



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


UpperCAmelCase      		=			[
    # 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       lowerCamelCase					(a_						:Union[str, Any])     ->							Optional[Any]:
       for pegasus_name, hf_name in PATTERNS:
              lowercase		:List[Any]       							=				k.replace(a_				,				a_)
       return k






def       lowerCamelCase					(a_						:dict				,				a_						:dict)     ->							PegasusForConditionalGeneration:
       lowercase		:Union[str, Any]       							=				DEFAULTS.copy()
       cfg_kwargs.update(a_)
       lowercase		:Optional[Any]       							=				PegasusConfig(**a_)
       lowercase		:int       							=				PegasusForConditionalGeneration(a_)
       lowercase		:Optional[int]       							=				torch_model.model.state_dict()
       lowercase		:Tuple       							=				{}
       for k, v in tf_weights.items():
              lowercase		:Dict       							=				rename_state_dict_key(a_)
              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		:Union[str, Any]       							=				v.T
              lowercase		:List[str]       							=				torch.tensor(a_				,				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		:Optional[int]       							=				torch.zeros_like(mapping['''shared.weight'''][cfg.pad_token_id + 1])
       lowercase		:Union[str, Any]       							=				mapping['''shared.weight''']
       lowercase		:Dict       							=				mapping['''shared.weight''']
       lowercase		:int       							=				{k: torch.zeros_like(a_) for k, v in sd.items() if k.endswith('''bias''') and k not in mapping}
       mapping.update(**a_)
       lowercase							,  lowercase		:Tuple       							=				torch_model.model.load_state_dict(a_				,				strict=a_)
       lowercase		:Dict       							=				[
           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       lowerCamelCase					(a_						:Union[str, Any]="./ckpt/aeslc/model.ckpt-32000")     ->							Dict:
       lowercase		:Any       							=				tf.train.list_variables(a_)
       lowercase		:str       							=				{}
       lowercase		:Optional[Any]       							=				['''Adafactor''', '''global_step''']
       for name, shape in tqdm(a_				,				desc='''converting tf checkpoint to dict'''):
              lowercase		:str       							=				any(pat in name for pat in ignore_name)
              if skip_key:
                     continue
              lowercase		:str       							=				tf.train.load_variable(a_				,				a_)
              lowercase		:Dict       							=				array
       return tf_weights






def       lowerCamelCase					(a_						:str				,				a_						:str)     ->							List[str]:
       # save tokenizer first
       lowercase		:List[str]       							=				Path(a_).parent.name
       lowercase		:List[Any]       							=				task_specific_params[F"""summarization_{dataset}"""]['''max_position_embeddings''']
       lowercase		:List[Any]       							=				PegasusTokenizer.from_pretrained('''sshleifer/pegasus'''				,				model_max_length=a_)
       assert tok.model_max_length == desired_max_model_length
       tok.save_pretrained(a_)

       # convert model
       lowercase		:Any       							=				get_tf_weights_as_numpy(a_)
       lowercase		:str       							=				task_specific_params[F"""summarization_{dataset}"""]
       if dataset == "large":
              lowercase		:int       							=				task_specific_params
       lowercase		:List[Any]       							=				convert_pegasus(a_				,				a_)
       torch_model.save_pretrained(a_)
       lowercase		:Union[str, Any]       							=				torch_model.state_dict()
       sd.pop('''model.decoder.embed_positions.weight''')
       sd.pop('''model.encoder.embed_positions.weight''')
       torch.save(a_				,				Path(a_) / '''pytorch_model.bin''')


if __name__ == "__main__":
 UpperCAmelCase      		=			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.''')
 UpperCAmelCase      		=			parser.parse_args()
 if args.save_dir is None:
  UpperCAmelCase      		=			Path(args.tf_ckpt_path).parent.name
  UpperCAmelCase      		=			os.path.join('''pegasus''', dataset)
 convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)

