




'''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()
a  :				List[Any]									=  logging.get_logger(__name__)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase=False	)		->					List[Any]:

					'''simple docstring'''

					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"
										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 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=False	)		->					Optional[Any]:

					'''simple docstring'''

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


def 		__magic_name__    (	__UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				['''head.weight''', '''head.bias''']
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase	)		->					Union[str, Any]:

					'''simple docstring'''

					snake_case_									=				dct.pop(__UpperCAmelCase	)
					snake_case_									=				val


def 		__magic_name__    (	)		->					Tuple:

					'''simple docstring'''

					snake_case_									=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					snake_case_									=				Image.open(requests.get(__UpperCAmelCase,      stream=__UpperCAmelCase	).raw	)
					return im


@torch.no_grad()
def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase,      __UpperCAmelCase=False	)		->					int:

					'''simple docstring'''

					snake_case_									=				BitConfig(
					    global_padding='''same''',      layer_type='''bottleneck''',      depths=(3, 4, 9),      out_features=['''stage3'''],      embedding_dynamic_padding=__UpperCAmelCase,      )
					snake_case_									=				ViTHybridConfig(backbone_config=__UpperCAmelCase,      image_size=384,      num_labels=1000	)
					snake_case_									=				False

					# load original model from timm
					snake_case_									=				timm.create_model(__UpperCAmelCase,      pretrained=__UpperCAmelCase	)
					timm_model.eval()

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

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

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

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

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

					snake_case_									=				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(),      )

					snake_case_									=				prepare_img()
					snake_case_									=				transform(__UpperCAmelCase	).unsqueeze(0	)
					snake_case_									=				processor(__UpperCAmelCase,      return_tensors='''pt'''	).pixel_values

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

					# verify logits
					with torch.no_grad():
										snake_case_									=				model(__UpperCAmelCase	)
										snake_case_									=				outputs.logits

					print('''Predicted class:''',      logits.argmax(-1	).item()	)
					if base_model:
										snake_case_									=				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:
										snake_case_									=				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__":
							a  :				Optional[Any]									=  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.'
							)

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







'''simple docstring'''

import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
							from .tokenization_ta import TaTokenizer
else:
							a  :				Dict									=  None


a  :				List[Any]									=  logging.get_logger(__name__)

a  :				List[Any]									=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

a  :				str									=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
a  :				List[Any]									=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}






class 						a				(   _lowerCamelCase     ):
				snake_case_      =		VOCAB_FILES_NAMES
				snake_case_      =		PRETRAINED_VOCAB_FILES_MAP
				snake_case_      =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				snake_case_      =		["input_ids", "attention_mask"]
				snake_case_      =		TaTokenizer

				snake_case_      =		[]


				def __init__(		self    :					List[Any]       ,				lowercase_    :					int=None       ,				lowercase_    :					Dict=None       ,				lowercase_    :					Dict="</s>"       ,				lowercase_    :					List[Any]="<unk>"       ,				lowercase_    :					int="<pad>"       ,				lowercase_    :					int=100       ,				lowercase_    :					List[Any]=None       ,				**lowercase_    :					List[str]       ,				):
									# Add extra_ids to the special token list
									if extra_ids > 0 and additional_special_tokens is None:
														snake_case_									=				[F"<extra_id_{i}>" for i in range(lowercase_					)]
									elif extra_ids > 0 and additional_special_tokens is not None:
														# Check that we have the right number of extra special tokens
														snake_case_									=				len(set(filter(lambda lowercase_					: bool('''extra_id_''' in str(lowercase_					)					)       ,				lowercase_					)					)					)
														if extra_tokens != extra_ids:
																			raise ValueError(
																			    F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																			    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																			    ''' tokens'''					)

									super().__init__(
									    lowercase_       ,				tokenizer_file=lowercase_       ,				eos_token=lowercase_       ,				unk_token=lowercase_       ,				pad_token=lowercase_       ,				extra_ids=lowercase_       ,				additional_special_tokens=lowercase_       ,				**lowercase_       ,				)

									snake_case_									=				vocab_file
									snake_case_									=				False if not self.vocab_file else True
									snake_case_									=				extra_ids


				@staticmethod
				def   A_ (		lowercase_    :					Optional[int]       ,				lowercase_    :					List[str]       ,				lowercase_    :					int					):
									if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
														snake_case_									=				TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
														if init_max_model_length is not None and init_max_model_length != max_model_length:
																			return init_max_model_length
														elif init_max_model_length is None:
																			warnings.warn(
																			    '''This tokenizer was incorrectly instantiated with a model max length of'''
																			    F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																			    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																			    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																			    F" {pretrained_model_name_or_path} automatically truncating your input to"
																			    F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																			    F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																			    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																			    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''       ,				lowercase_       ,				)

									return max_model_length


				def   A_ (		self    :					Optional[Any]       ,				lowercase_    :					str       ,				lowercase_    :					Optional[str] = None					):
									if not self.can_save_slow_tokenizer:
														raise ValueError(
														    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
														    '''tokenizer.'''					)

									if not os.path.isdir(lowercase_					):
														logger.error(F"Vocabulary path ({save_directory}) should be a directory"					)
														return
									snake_case_									=				os.path.join(
									    lowercase_       ,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)

									if os.path.abspath(self.vocab_file					) != os.path.abspath(lowercase_					):
														copyfile(self.vocab_file       ,				lowercase_					)
														logger.info(F"Copy vocab file to {out_vocab_file}"					)

									return (out_vocab_file,)


				def   A_ (		self    :					Optional[int]       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				token_ids_a + [self.eos_token_id]
									if token_ids_a is None:
														return self.prefix_tokens + token_ids_a
									else:
														snake_case_									=				token_ids_a + [self.eos_token_id]
														return self.prefix_tokens + token_ids_a + token_ids_a


				def   A_ (		self    :					int       ,				lowercase_    :					List[int]       ,				lowercase_    :					Optional[List[int]] = None					):
									snake_case_									=				[self.eos_token_id]

									if token_ids_a is None:
														return len(token_ids_a + eos					) * [0]
									return len(token_ids_a + eos + token_ids_a + eos					) * [0]


				def   A_ (		self    :					Dict					):
									return list(
									    set(filter(lambda lowercase_					: bool(re.search(R'''<extra_id_\d+>'''       ,				lowercase_					)					) is not None       ,				self.additional_special_tokens					)					)					)


				def   A_ (		self    :					Any					):
									return [self.convert_tokens_to_ids(lowercase_					) for token in self.get_sentinel_tokens()]

