
'''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()
lowercase			:				str    		=			logging.get_logger(__name__)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A       :   Dict					=						[]

    # 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       :   Dict					=						[(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_				(   snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



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







def 		lowerCAmelCase_				(   snake_case__						):




    '''simple docstring'''



    A       :   List[str]					=						['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(snake_case__		,				snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						dct.pop(snake_case__						)
    A       :   Tuple					=						val







def 		lowerCAmelCase_				(   ):




    '''simple docstring'''



    A       :   str					=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A       :   int					=						Image.open(requests.get(snake_case__		,				stream=snake_case__						).raw						)
    return im







@torch.no_grad()
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=False						):




    '''simple docstring'''



    A       :   List[str]					=						BitConfig(
        global_padding='''same'''		,				layer_type='''bottleneck'''		,				depths=(3, 4, 9)		,				out_features=['''stage3''']		,				embedding_dynamic_padding=snake_case__		,				)
    A       :   Tuple					=						ViTHybridConfig(backbone_config=snake_case__		,				image_size=384		,				num_labels=1000						)
    A       :   int					=						False

    # load original model from timm
    A       :   Optional[Any]					=						timm.create_model(snake_case__		,				pretrained=snake_case__						)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    A       :   Dict					=						timm_model.state_dict()
    if base_model:
        remove_classification_head_(snake_case__						)
    A       :   List[str]					=						create_rename_keys(snake_case__		,				snake_case__						)
    for src, dest in rename_keys:
        rename_key(snake_case__		,				snake_case__		,				snake_case__						)
    read_in_q_k_v(snake_case__		,				snake_case__		,				snake_case__						)

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

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

    # create image processor
    A       :   int					=						create_transform(**resolve_data_config({}		,				model=snake_case__						)						)
    A       :   List[str]					=						transform.transforms

    A       :   Tuple					=						{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

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

    A       :   Dict					=						prepare_img()
    A       :   Optional[int]					=						transform(snake_case__						).unsqueeze(0						)
    A       :   int					=						processor(snake_case__		,				return_tensors='''pt'''						).pixel_values

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

    # verify logits
    with torch.no_grad():
        A       :   List[Any]					=						model(snake_case__						)
        A       :   Optional[int]					=						outputs.logits

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

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

    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__":
      lowercase			:				int    		=			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.'
      )

      lowercase			:				Optional[Any]    		=			parser.parse_args()
      convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''

import logging
import os
from typing import List, TextIO, Union

from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask


lowerCamelCase							:   List[Any]  						=	logging.getLogger(__name__)
class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    Optional[Any]   ,						UpperCamelCase    :    Any=-1 ):

             '''simple docstring'''







             lowercase__      			= label_idx



      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    Union[Split, str] ):

             '''simple docstring'''







             if isinstance(UpperCamelCase   ,						UpperCamelCase ):
                    lowercase__      			= mode.value
             lowercase__      			= os.path.join(UpperCamelCase   ,						f"{mode}.txt" )
             lowercase__      			= 1
             lowercase__      			= []
             with open(UpperCamelCase   ,						encoding='''utf-8''' ) as f:
                    lowercase__      			= []
                    lowercase__      			= []
                    for line in f:
                           if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
                                  if words:
                                         examples.append(InputExample(guid=f"{mode}-{guid_index}"   ,						words=UpperCamelCase   ,						labels=UpperCamelCase ) )
                                         guid_index += 1
                                         lowercase__      			= []
                                         lowercase__      			= []
                           else:
                                  lowercase__      			= line.split(''' ''' )
                                  words.append(splits[0] )
                                  if len(UpperCamelCase ) > 1:
                                         labels.append(splits[self.label_idx].replace('''\n'''   ,						'''''' ) )
                                  else:
                                         # Examples could have no label for mode = "test"
                                         labels.append('''O''' )
                    if words:
                           examples.append(InputExample(guid=f"{mode}-{guid_index}"   ,						words=UpperCamelCase   ,						labels=UpperCamelCase ) )
             return examples



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    List ):

             '''simple docstring'''







             lowercase__      			= 0
             for line in test_input_reader:
                    if line.startswith('''-DOCSTART-''' ) or line == "" or line == "\n":
                           writer.write(UpperCamelCase )
                           if not preds_list[example_id]:
                                  example_id += 1
                    elif preds_list[example_id]:
                           lowercase__      			= line.split()[0] + ''' ''' + preds_list[example_id].pop(0 ) + '''\n'''
                           writer.write(UpperCamelCase )
                    else:
                           logger.warning('''Maximum sequence length exceeded: No prediction for \'%s\'.'''   ,						line.split()[0] )






      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    str ):

             '''simple docstring'''







             if path:
                    with open(UpperCamelCase   ,						'''r''' ) as f:
                           lowercase__      			= f.read().splitlines()
                    if "O" not in labels:
                           lowercase__      			= ['''O'''] + labels
                    return labels
             else:
                    return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def __init__(self    :    List[Any] ):

             '''simple docstring'''







             super().__init__(label_idx=-2 )






      def       UpperCamelCase__		(self    :    List[Any]   ,						UpperCamelCase    :    str ):

             '''simple docstring'''







             if path:
                    with open(UpperCamelCase   ,						'''r''' ) as f:
                           lowercase__      			= f.read().splitlines()
                    if "O" not in labels:
                           lowercase__      			= ['''O'''] + labels
                    return labels
             else:
                    return [
                        "O",
                        "B-ADVP",
                        "B-INTJ",
                        "B-LST",
                        "B-PRT",
                        "B-NP",
                        "B-SBAR",
                        "B-VP",
                        "B-ADJP",
                        "B-CONJP",
                        "B-PP",
                        "I-ADVP",
                        "I-INTJ",
                        "I-LST",
                        "I-PRT",
                        "I-NP",
                        "I-SBAR",
                        "I-VP",
                        "I-ADJP",
                        "I-CONJP",
                        "I-PP",
                    ]






class   __lowerCAmelCase						(lowercase_     ):





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    int   ,						UpperCamelCase    :    Union[Split, str] ):

             '''simple docstring'''







             if isinstance(UpperCamelCase   ,						UpperCamelCase ):
                    lowercase__      			= mode.value
             lowercase__      			= os.path.join(UpperCamelCase   ,						f"{mode}.txt" )
             lowercase__      			= 1
             lowercase__      			= []

             with open(UpperCamelCase   ,						encoding='''utf-8''' ) as f:
                    for sentence in parse_incr(UpperCamelCase ):
                           lowercase__      			= []
                           lowercase__      			= []
                           for token in sentence:
                                  words.append(token['''form'''] )
                                  labels.append(token['''upos'''] )
                           assert len(UpperCamelCase ) == len(UpperCamelCase )
                           if words:
                                  examples.append(InputExample(guid=f"{mode}-{guid_index}"   ,						words=UpperCamelCase   ,						labels=UpperCamelCase ) )
                                  guid_index += 1
             return examples



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    TextIO   ,						UpperCamelCase    :    List ):

             '''simple docstring'''







             lowercase__      			= 0
             for sentence in parse_incr(UpperCamelCase ):
                    lowercase__      			= preds_list[example_id]
                    lowercase__      			= ''''''
                    for token in sentence:
                           out += f"{token['form']} ({token['upos']}|{s_p.pop(0 )}) "
                    out += "\n"
                    writer.write(UpperCamelCase )
                    example_id += 1






      def       UpperCamelCase__		(self    :    Union[str, Any]   ,						UpperCamelCase    :    str ):

             '''simple docstring'''







             if path:
                    with open(UpperCamelCase   ,						'''r''' ) as f:
                           return f.read().splitlines()
             else:
                    return [
                        "ADJ",
                        "ADP",
                        "ADV",
                        "AUX",
                        "CCONJ",
                        "DET",
                        "INTJ",
                        "NOUN",
                        "NUM",
                        "PART",
                        "PRON",
                        "PROPN",
                        "PUNCT",
                        "SCONJ",
                        "SYM",
                        "VERB",
                        "X",
                    ]




