

'''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()
__lowerCamelCase										=			logging.get_logger(__name__)
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__=False )	->	Optional[Any]:
       A_		    =					[]

       # 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"
              A_		    =					[(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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=False )	->	List[Any]:
       for i in range(config.num_hidden_layers ):
              if base_model:
                     A_		    =					""""""
              else:
                     A_		    =					"""vit."""
              # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
              A_		    =					state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
              A_		    =					state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
              # next, add query, keys and values (in that order) to the state dict
              A_		    =					in_proj_weight[
                  : config.hidden_size, :
              ]
              A_		    =					in_proj_bias[: config.hidden_size]
              A_		    =					in_proj_weight[
                  config.hidden_size : config.hidden_size * 2, :
              ]
              A_		    =					in_proj_bias[
                  config.hidden_size : config.hidden_size * 2
              ]
              A_		    =					in_proj_weight[
                  -config.hidden_size :, :
              ]
              A_		    =					in_proj_bias[-config.hidden_size :]
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	List[str]:
       A_		    =					["""head.weight""", """head.bias"""]
       for k in ignore_keys:
              state_dict.pop(UpperCAmelCase__,				UpperCAmelCase__ )
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ )	->	int:
       A_		    =					dct.pop(UpperCAmelCase__ )
       A_		    =					val
def 					UpperCAmelCase__			(					)	->	Dict:
       A_		    =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       A_		    =					Image.open(requests.get(UpperCAmelCase__,				stream=UpperCAmelCase__ ).raw )
       return im
@torch.no_grad()
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=False )	->	List[str]:
       A_		    =					BitConfig(
           global_padding="""same""",				layer_type="""bottleneck""",				depths=(3, 4, 9),				out_features=["""stage3"""],				embedding_dynamic_padding=UpperCAmelCase__,				)
       A_		    =					ViTHybridConfig(backbone_config=UpperCAmelCase__,				image_size=3_84,				num_labels=10_00 )
       A_		    =					False

       # load original model from timm
       A_		    =					timm.create_model(UpperCAmelCase__,				pretrained=UpperCAmelCase__ )
       timm_model.eval()

       # load state_dict of original model, remove and rename some keys
       A_		    =					timm_model.state_dict()
       if base_model:
              remove_classification_head_(UpperCAmelCase__ )
       A_		    =					create_rename_keys(UpperCAmelCase__,				UpperCAmelCase__ )
       for src, dest in rename_keys:
              rename_key(UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ )
       read_in_q_k_v(UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__ )

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

       # load HuggingFace model
       if vit_name[-5:] == "in21k":
              A_		    =					ViTHybridModel(UpperCAmelCase__ ).eval()
       else:
              A_		    =					ViTHybridForImageClassification(UpperCAmelCase__ ).eval()
       model.load_state_dict(UpperCAmelCase__ )

       # create image processor
       A_		    =					create_transform(**resolve_data_config({},				model=UpperCAmelCase__ ) )
       A_		    =					transform.transforms

       A_		    =					{
           """bilinear""": PILImageResampling.BILINEAR,
           """bicubic""": PILImageResampling.BICUBIC,
           """nearest""": PILImageResampling.NEAREST,
       }

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

       A_		    =					prepare_img()
       A_		    =					transform(UpperCAmelCase__ ).unsqueeze(0 )
       A_		    =					processor(UpperCAmelCase__,				return_tensors="""pt""" ).pixel_values

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

       # verify logits
       with torch.no_grad():
              A_		    =					model(UpperCAmelCase__ )
              A_		    =					outputs.logits

       print("""Predicted class:""",				logits.argmax(-1 ).item() )
       if base_model:
              A_		    =					timm_model.forward_features(UpperCAmelCase__ )
              assert timm_pooled_output.shape == outputs.pooler_output.shape
              assert torch.allclose(UpperCAmelCase__,				outputs.pooler_output,				atol=1e-3 )
       else:
              A_		    =					timm_model(UpperCAmelCase__ )
              assert timm_logits.shape == outputs.logits.shape
              assert torch.allclose(UpperCAmelCase__,				outputs.logits,				atol=1e-3 )
       print("""Looks ok!""" )

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

       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__":
      __lowerCamelCase										=			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.'''
      )

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







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
