
"""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														=		logging.get_logger(__name__)



def 				lowerCamelCase__		(      __snake_case,					__snake_case=False     )							->		str:


					"""simple docstring"""





					_UpperCamelCase										=  []

					# 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"
										_UpperCamelCase										=  [(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     )							->		int:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      __snake_case     )							->		Union[str, Any]:


					"""simple docstring"""





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



def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case     )							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  dct.pop(__snake_case     )
					_UpperCamelCase										=  val



def 				lowerCamelCase__		(      )							->		List[str]:


					"""simple docstring"""





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



@torch.no_grad()
def 				lowerCamelCase__		(      __snake_case,					__snake_case,					__snake_case=False     )							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase										=  BitConfig(
					    global_padding='''same''',					layer_type='''bottleneck''',					depths=(3, 4, 9),					out_features=['''stage3'''],					embedding_dynamic_padding=__snake_case,					)
					_UpperCamelCase										=  ViTHybridConfig(backbone_config=__snake_case,					image_size=3_84,					num_labels=10_00     )
					_UpperCamelCase										=  False

					# load original model from timm
					_UpperCamelCase										=  timm.create_model(__snake_case,					pretrained=__snake_case     )
					timm_model.eval()

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

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

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

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

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

					_UpperCamelCase										=  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(),					)

					_UpperCamelCase										=  prepare_img()
					_UpperCamelCase										=  transform(__snake_case     ).unsqueeze(0     )
					_UpperCamelCase										=  processor(__snake_case,					return_tensors='''pt'''     ).pixel_values

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

					# verify logits
					with torch.no_grad():
										_UpperCamelCase										=  model(__snake_case     )
										_UpperCamelCase										=  outputs.logits

					print('''Predicted class:''',					logits.argmax(-1     ).item()     )
					if base_model:
										_UpperCamelCase										=  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:
										_UpperCamelCase										=  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)








"""simple docstring"""





# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

import torch

from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
				from PIL import Image






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'dandelin/vilt-b32-finetuned-vqa'
		lowercase__      =				(
		    'This is a tool that answers a question about an image. It takes an input named `image` which should be the '
		    'image containing the information, as well as a `question` which should be the question in English. It '
		    'returns a text that is the answer to the question.'
		)
		lowercase__      =				'image_qa'
		lowercase__      =				AutoProcessor
		lowercase__      =				AutoModelForVisualQuestionAnswering

		lowercase__      =				['image', 'text']
		lowercase__      =				['text']



		def __init__( self    ,		*__a    ,		**__a)	->	int:






							'''simple docstring'''
							requires_backends(self    ,		['''vision'''])
							super().__init__(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		__a    ,		__a)	->	Dict:






							'''simple docstring'''
							return self.pre_processor(__a    ,		__a    ,		return_tensors='''pt''')



		def 						UpperCAmelCase							( self    ,		__a)	->	Tuple:






							'''simple docstring'''
							with torch.no_grad():
												return self.model(**__a).logits




		def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






							'''simple docstring'''
							_UpperCamelCase										=  outputs.argmax(-1).item()
							return self.model.config.idalabel[idx]






