
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  			=					logging.get_logger(__name__)




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					Any=False					):


				'''simple docstring'''
				lowercase_       =					[]

				# 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_       =					[(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 				a						(							snake_case__:					Union[str, Any]						,   snake_case__:					Optional[int]						,   snake_case__:					Optional[int]=False					):


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




def 				a						(							snake_case__:					Tuple					):


				'''simple docstring'''
				lowercase_       =					['''head.weight''', '''head.bias''']
				for k in ignore_keys:
								state_dict.pop(snake_case__						,   snake_case__					)




def 				a						(							snake_case__:					Optional[Any]						,   snake_case__:					int						,   snake_case__:					List[Any]					):


				'''simple docstring'''
				lowercase_       =					dct.pop(snake_case__					)
				lowercase_       =					val




def 				a						(							):


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




@torch.no_grad()
def 				a						(							snake_case__:					List[str]						,   snake_case__:					Optional[Any]						,   snake_case__:					Tuple=False					):


				'''simple docstring'''
				lowercase_       =					BitConfig(
				    global_padding='''same'''						,   layer_type='''bottleneck'''						,   depths=(3, 4, 9)						,   out_features=['''stage3''']						,   embedding_dynamic_padding=snake_case__						,   )
				lowercase_       =					ViTHybridConfig(backbone_config=snake_case__						,   image_size=384						,   num_labels=1_000					)
				lowercase_       =					False

				# load original model from timm
				lowercase_       =					timm.create_model(snake_case__						,   pretrained=snake_case__					)
				timm_model.eval()

				# load state_dict of original model, remove and rename some keys
				lowercase_       =					timm_model.state_dict()
				if base_model:
								remove_classification_head_(snake_case__					)
				lowercase_       =					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__					)

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

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

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

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

				lowercase_       =					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()						,   )

				lowercase_       =					prepare_img()
				lowercase_       =					transform(snake_case__					).unsqueeze(0					)
				lowercase_       =					processor(snake_case__						,   return_tensors='''pt'''					).pixel_values

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

				# verify logits
				with torch.no_grad():
								lowercase_       =					model(snake_case__					)
								lowercase_       =					outputs.logits

				print('''Predicted class:'''						,   logits.argmax(-1					).item()					)
				if base_model:
								lowercase_       =					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:
								lowercase_       =					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__":
						__a  			=					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  			=					parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






from __future__ import annotations

from collections.abc import MutableSequence
class  lowercase__:



	"""simple docstring"""




	def __init__(       self  :			Optional[Any]       ,							SCREAMING_SNAKE_CASE_  :			int       ,							SCREAMING_SNAKE_CASE_  :			MutableSequence[float]       )       ->     None:
					if len(SCREAMING_SNAKE_CASE_       ) != degree + 1:
									raise ValueError(
									    '''The number of coefficients should be equal to the degree + 1.'''       )

					lowercase_       =					list(SCREAMING_SNAKE_CASE_       )
					lowercase_       =					degree
	def __add__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:

					if self.degree > polynomial_a.degree:
									lowercase_       =					self.coefficients[:]
									for i in range(polynomial_a.degree + 1       ):
													coefficients[i] += polynomial_a.coefficients[i]
									return Polynomial(self.degree       ,							SCREAMING_SNAKE_CASE_       )
					else:
									lowercase_       =					polynomial_a.coefficients[:]
									for i in range(self.degree + 1       ):
													coefficients[i] += self.coefficients[i]
									return Polynomial(polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def __sub__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					return self + polynomial_a * Polynomial(0       ,							[-1]       )
	def __neg__(       self  :			int       )       ->     Polynomial:
					return Polynomial(self.degree       ,							[-c for c in self.coefficients]       )
	def __mul__(       self  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Polynomial       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + polynomial_a.degree + 1)
					for i in range(self.degree + 1       ):
									for j in range(polynomial_a.degree + 1       ):
													coefficients[i + j] += (
													    self.coefficients[i] * polynomial_a.coefficients[j]
													)

					return Polynomial(self.degree + polynomial_a.degree       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			int | float       )       ->     int | float:
					lowercase_       =					0
					for i in range(self.degree + 1       ):
									result += self.coefficients[i] * (substitution**i)
					return result
	def __str__(       self  :			Tuple       )       ->     str:
					lowercase_       =					''''''
					for i in range(self.degree       ,							-1       ,							-1       ):
									if self.coefficients[i] == 0:
													continue
									elif self.coefficients[i] > 0:
													if polynomial:
																	polynomial += " + "
									else:
													polynomial += " - "

									if i == 0:
													polynomial += str(abs(self.coefficients[i]       )       )
									elif i == 1:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x"
									else:
													polynomial += str(abs(self.coefficients[i]       )       ) + "x^" + str(SCREAMING_SNAKE_CASE_       )

					return polynomial
	def __repr__(       self  :			Optional[Any]       )       ->     str:
					return self.__str__()
	def 						_lowercase  (       self  :			int       )       ->     Polynomial:
					lowercase_       =					[0] * self.degree
					for i in range(self.degree       ):
									lowercase_       =					self.coefficients[i + 1] * (i + 1)
					return Polynomial(self.degree - 1       ,							SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			int | float = 0       )       ->     Polynomial:
					lowercase_       =					[0] * (self.degree + 2)
					lowercase_       =					constant
					for i in range(self.degree + 1       ):
									lowercase_       =					self.coefficients[i] / (i + 1)
					return Polynomial(self.degree + 1       ,							SCREAMING_SNAKE_CASE_       )
	def __eq__(       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					if not isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									return False

					if self.degree != polynomial_a.degree:
									return False

					for i in range(self.degree + 1       ):
									if self.coefficients[i] != polynomial_a.coefficients[i]:
													return False

					return True




	def __ne__(       self  :			List[str]       ,							SCREAMING_SNAKE_CASE_  :			object       )       ->     bool:
					return not self.__eq__(SCREAMING_SNAKE_CASE_       )




