


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()
snake_case_						:			Any													=							logging.get_logger(__name__)







def 						lowerCamelCase(   a__	,a__=False):
				_SCREAMING_SNAKE_CASE			  =[]

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







def 						lowerCamelCase(   a__):
				_SCREAMING_SNAKE_CASE			  =['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(a__	,a__)







def 						lowerCamelCase(   a__	,a__	,a__):
				_SCREAMING_SNAKE_CASE			  =dct.pop(a__)
				_SCREAMING_SNAKE_CASE			  =val







def 						lowerCamelCase(   ):
				_SCREAMING_SNAKE_CASE			  ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
				_SCREAMING_SNAKE_CASE			  =Image.open(requests.get(a__	,stream=a__).raw)
				return im







@torch.no_grad()
def 						lowerCamelCase(   a__	,a__	,a__=False):
				_SCREAMING_SNAKE_CASE			  =BitConfig(
				    global_padding='''same'''	,layer_type='''bottleneck'''	,depths=(3, 4, 9)	,out_features=['''stage3''']	,embedding_dynamic_padding=a__	,)
				_SCREAMING_SNAKE_CASE			  =ViTHybridConfig(backbone_config=a__	,image_size=384	,num_labels=1000)
				_SCREAMING_SNAKE_CASE			  =False

				# load original model from timm
				_SCREAMING_SNAKE_CASE			  =timm.create_model(a__	,pretrained=a__)
				timm_model.eval()

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

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

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

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

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

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

				_SCREAMING_SNAKE_CASE			  =prepare_img()
				_SCREAMING_SNAKE_CASE			  =transform(a__).unsqueeze(0)
				_SCREAMING_SNAKE_CASE			  =processor(a__	,return_tensors='''pt''').pixel_values

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

				# verify logits
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE			  =model(a__)
								_SCREAMING_SNAKE_CASE			  =outputs.logits

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

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

				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__":
							snake_case_						:			str													=							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.'''
							)

							snake_case_						:			Any													=							parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union

import pandas as pd
import pyarrow as pa

import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal


snake_case_						:			Any													=							datasets.utils.logging.get_logger(__name__)

snake_case_						:			List[Any]													=							['''names''', '''prefix''']
snake_case_						:			Dict													=							['''warn_bad_lines''', '''error_bad_lines''', '''mangle_dupe_cols''']
snake_case_						:			Dict													=							['''encoding_errors''', '''on_bad_lines''']
snake_case_						:			Optional[Any]													=							['''date_format''']

@dataclass
class 						A__							(		datasets.BuilderConfig  ):
						UpperCAmelCase             =						","
						UpperCAmelCase             =						None
						UpperCAmelCase             =						"infer"
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						True
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						False
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True
						UpperCAmelCase             =						False
						UpperCAmelCase             =						True
						UpperCAmelCase             =						None
						UpperCAmelCase             =						"."
						UpperCAmelCase             =						None
						UpperCAmelCase             =						'"'
						UpperCAmelCase             =						0
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						None
						UpperCAmelCase             =						True
						UpperCAmelCase             =						True
						UpperCAmelCase             =						0
						UpperCAmelCase             =						True
						UpperCAmelCase             =						False
						UpperCAmelCase             =						None
						UpperCAmelCase             =						10000
						UpperCAmelCase             =						None
						UpperCAmelCase             =						"strict"
						UpperCAmelCase             =						"error"
						UpperCAmelCase             =						None




						def 							__UpperCamelCase				(       self					:      int  )							->     Union[str, Any]:


										"""simple docstring"""





										if self.delimiter is not None:
														_SCREAMING_SNAKE_CASE			  =self.delimiter
										if self.column_names is not None:
														_SCREAMING_SNAKE_CASE			  =self.column_names




						@property
						def 							__UpperCamelCase				(       self					:      Dict  )							->     List[Any]:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  ={
										    '''sep''': self.sep,
										    '''header''': self.header,
										    '''names''': self.names,
										    '''index_col''': self.index_col,
										    '''usecols''': self.usecols,
										    '''prefix''': self.prefix,
										    '''mangle_dupe_cols''': self.mangle_dupe_cols,
										    '''engine''': self.engine,
										    '''converters''': self.converters,
										    '''true_values''': self.true_values,
										    '''false_values''': self.false_values,
										    '''skipinitialspace''': self.skipinitialspace,
										    '''skiprows''': self.skiprows,
										    '''nrows''': self.nrows,
										    '''na_values''': self.na_values,
										    '''keep_default_na''': self.keep_default_na,
										    '''na_filter''': self.na_filter,
										    '''verbose''': self.verbose,
										    '''skip_blank_lines''': self.skip_blank_lines,
										    '''thousands''': self.thousands,
										    '''decimal''': self.decimal,
										    '''lineterminator''': self.lineterminator,
										    '''quotechar''': self.quotechar,
										    '''quoting''': self.quoting,
										    '''escapechar''': self.escapechar,
										    '''comment''': self.comment,
										    '''encoding''': self.encoding,
										    '''dialect''': self.dialect,
										    '''error_bad_lines''': self.error_bad_lines,
										    '''warn_bad_lines''': self.warn_bad_lines,
										    '''skipfooter''': self.skipfooter,
										    '''doublequote''': self.doublequote,
										    '''memory_map''': self.memory_map,
										    '''float_precision''': self.float_precision,
										    '''chunksize''': self.chunksize,
										    '''encoding_errors''': self.encoding_errors,
										    '''on_bad_lines''': self.on_bad_lines,
										    '''date_format''': self.date_format,
										}

										# some kwargs must not be passed if they don't have a default value
										# some others are deprecated and we can also not pass them if they are the default value
										for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
														if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig()				,    _a  ):
																		del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 2.0 new arguments
										if not (datasets.config.PANDAS_VERSION.major >= 2):
														for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
																		del pd_read_csv_kwargs[pd_read_csv_parameter]

        # Remove 1.3 new arguments
										if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
														for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
																		del pd_read_csv_kwargs[pd_read_csv_parameter]

										return pd_read_csv_kwargs

class 						A__							(		datasets.ArrowBasedBuilder  ):
						UpperCAmelCase             =						CsvConfig




						def 							__UpperCamelCase				(       self					:      Optional[int]  )							->     int:


										"""simple docstring"""





										return datasets.DatasetInfo(features=self.config.features  )




						def 							__UpperCamelCase				(       self					:      Dict				,    _a					:      str  )							->     List[str]:


										"""simple docstring"""





										if not self.config.data_files:
														raise ValueError(f"At least one data file must be specified, but got data_files={self.config.data_files}"  )
										_SCREAMING_SNAKE_CASE			  =dl_manager.download_and_extract(self.config.data_files  )
										if isinstance(_a				,    (str, list, tuple)  ):
														_SCREAMING_SNAKE_CASE			  =data_files
														if isinstance(_a				,    _a  ):
																		_SCREAMING_SNAKE_CASE			  =[files]
														_SCREAMING_SNAKE_CASE			  =[dl_manager.iter_files(_a  ) for file in files]
														return [datasets.SplitGenerator(name=datasets.Split.TRAIN				,    gen_kwargs={'''files''': files}  )]
										_SCREAMING_SNAKE_CASE			  =[]
										for split_name, files in data_files.items():
														if isinstance(_a				,    _a  ):
																		_SCREAMING_SNAKE_CASE			  =[files]
														_SCREAMING_SNAKE_CASE			  =[dl_manager.iter_files(_a  ) for file in files]
														splits.append(datasets.SplitGenerator(name=_a				,    gen_kwargs={'''files''': files}  )  )
										return splits




						def 							__UpperCamelCase				(       self					:      Tuple				,    _a					:      pa.Table  )							->     pa.Table:


										"""simple docstring"""





										if self.config.features is not None:
														_SCREAMING_SNAKE_CASE			  =self.config.features.arrow_schema
														if all(not require_storage_cast(_a  ) for feature in self.config.features.values()  ):
																		# cheaper cast
																		_SCREAMING_SNAKE_CASE			  =pa.Table.from_arrays([pa_table[field.name] for field in schema]				,    schema=_a  )
														else:
																		# more expensive cast; allows str <-> int/float or str to Audio for example
																		_SCREAMING_SNAKE_CASE			  =table_cast(_a				,    _a  )
										return pa_table




						def 							__UpperCamelCase				(       self					:      str				,    _a					:      Union[str, Any]  )							->     int:


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE			  =self.config.features.arrow_schema if self.config.features else None
										# dtype allows reading an int column as str
										_SCREAMING_SNAKE_CASE			  =(
										    {
										        name: dtype.to_pandas_dtype() if not require_storage_cast(_a  ) else object
										        for name, dtype, feature in zip(schema.names				,    schema.types				,    self.config.features.values()  )
										    }
										    if schema is not None
										    else None
										)
										for file_idx, file in enumerate(itertools.chain.from_iterable(_a  )  ):
														_SCREAMING_SNAKE_CASE			  =pd.read_csv(_a				,    iterator=_a				,    dtype=_a				,    **self.config.pd_read_csv_kwargs  )
														try:
																		for batch_idx, df in enumerate(_a  ):
																						_SCREAMING_SNAKE_CASE			  =pa.Table.from_pandas(_a  )
																						# Uncomment for debugging (will print the Arrow table size and elements)
																						# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																						# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																						yield (file_idx, batch_idx), self._cast_table(_a  )
														except ValueError as e:
																		logger.error(f"Failed to read file '{file}' with error {type(_a  )}: {e}"  )
																		raise