


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()
__UpperCamelCase     		=							logging.get_logger(__name__)
def 							UpperCamelCase_(					_A :Dict					,  _A :str=False	)->		int:
			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 							UpperCamelCase_(					_A :List[str]					,  _A :Optional[int]					,  _A :Dict=False	)->		List[str]:
			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 							UpperCamelCase_(					_A :str	)->		List[Any]:
			UpperCamelCase__       =							["head.weight", "head.bias"]
			for k in ignore_keys:
						state_dict.pop(_A					,  _A	)
def 							UpperCamelCase_(					_A :Tuple					,  _A :Dict					,  _A :Optional[Any]	)->		List[Any]:
			UpperCamelCase__       =							dct.pop(_A	)
			UpperCamelCase__       =							val
def 							UpperCamelCase_(					)->		List[Any]:
			UpperCamelCase__       =							"http://images.cocodataset.org/val2017/000000039769.jpg"
			UpperCamelCase__       =							Image.open(requests.get(_A					,  stream=_A	).raw	)
			return im
@torch.no_grad()
def 							UpperCamelCase_(					_A :List[Any]					,  _A :List[Any]					,  _A :int=False	)->		int:
			UpperCamelCase__       =							BitConfig(
			    global_padding="same"					,  layer_type="bottleneck"					,  depths=(3, 4, 9)					,  out_features=["stage3"]					,  embedding_dynamic_padding=_A					,  )
			UpperCamelCase__       =							ViTHybridConfig(backbone_config=_A					,  image_size=3_84					,  num_labels=10_00	)
			UpperCamelCase__       =							False

			# load original model from timm
			UpperCamelCase__       =							timm.create_model(_A					,  pretrained=_A	)
			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_(_A	)
			UpperCamelCase__       =							create_rename_keys(_A					,  _A	)
			for src, dest in rename_keys:
						rename_key(_A					,  _A					,  _A	)
			read_in_q_k_v(_A					,  _A					,  _A	)

			UpperCamelCase__       =							"huggingface/label-files"
			UpperCamelCase__       =							"imagenet-1k-id2label.json"
			UpperCamelCase__       =							json.load(open(hf_hub_download(_A					,  _A					,  repo_type="dataset"	)					,  "r"	)	)
			UpperCamelCase__       =							{int(_A	): 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(_A	).eval()
			else:
						UpperCamelCase__       =							ViTHybridForImageClassification(_A	).eval()
			model.load_state_dict(_A	)

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

			UpperCamelCase__       =							{
			    "bilinear": PILImageResampling.BILINEAR,
			    "bicubic": PILImageResampling.BICUBIC,
			    "nearest": PILImageResampling.NEAREST,
			}

			UpperCamelCase__       =							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()					,  )

			UpperCamelCase__       =							prepare_img()
			UpperCamelCase__       =							transform(_A	).unsqueeze(0	)
			UpperCamelCase__       =							processor(_A					,  return_tensors="pt"	).pixel_values

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

			# verify logits
			with torch.no_grad():
						UpperCamelCase__       =							model(_A	)
						UpperCamelCase__       =							outputs.logits

			print("Predicted class:"					,  logits.argmax(-1	).item()	)
			if base_model:
						UpperCamelCase__       =							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:
						UpperCamelCase__       =							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__":
						__UpperCamelCase     		=							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.'
						)

						__UpperCamelCase     		=							parser.parse_args()
						convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)



import argparse
from collections import defaultdict

import yaml


_lowercase:	List[Any]       			=		'''docs/source/en/_toctree.yml'''






def _lowerCamelCase  (		snake_case  ):
							_lowerCAmelCase							=							defaultdict(snake_case  )
							_lowerCAmelCase							=							[]
							_lowerCAmelCase							=							[]
							for doc in doc_list:
														if "local" in doc:
																					counts[doc["local"]] += 1

														if doc["title"].lower() == "overview":
																					overview_doc.append({'local': doc['local'], 'title': doc['title']}  )
														else:
																					new_doc_list.append(snake_case  )

							_lowerCAmelCase							=							new_doc_list
							_lowerCAmelCase							=							[key for key, value in counts.items() if value > 1]

							_lowerCAmelCase							=							[]
							for duplicate_key in duplicates:
														_lowerCAmelCase							=							list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}  )
														if len(snake_case  ) > 1:
																					raise ValueError(
																					    F'{duplicate_key} is present several times in the documentation table of content at '
																					    '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
																					    'others.'  )
														# Only add this once
														new_doc.append({'local': duplicate_key, 'title': titles[0]}  )

							# Add none duplicate-keys
							new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]  )
							_lowerCAmelCase							=							sorted(snake_case     , key=lambda snake_case  : s["title"].lower()  )

							# "overview" gets special treatment and is always first
							if len(snake_case  ) > 1:
														raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'  )

							overview_doc.extend(snake_case  )

							# Sort
							return overview_doc






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[scheduler_idx]["title"] != "Schedulers":
														scheduler_idx += 1

							_lowerCAmelCase							=							api_doc[scheduler_idx]['sections']
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							_lowerCAmelCase							=							False
							if new_scheduler_doc != scheduler_doc:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_scheduler_doc

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )






def _lowerCamelCase  (		snake_case=False  ):
							with open(snake_case     , encoding='utf-8'  ) as f:
														_lowerCAmelCase							=							yaml.safe_load(f.read()  )

							# Get to the API doc
							_lowerCAmelCase							=							0
							while content[api_idx]["title"] != "API":
														api_idx += 1
							_lowerCAmelCase							=							content[api_idx]['sections']

							# Then to the model doc
							_lowerCAmelCase							=							0
							while api_doc[pipeline_idx]["title"] != "Pipelines":
														pipeline_idx += 1

							_lowerCAmelCase							=							False
							_lowerCAmelCase							=							api_doc[pipeline_idx]['sections']
							_lowerCAmelCase							=							[]

							# sort sub pipeline docs
							for pipeline_doc in pipeline_docs:
														if "section" in pipeline_doc:
																					_lowerCAmelCase							=							pipeline_doc['section']
																					_lowerCAmelCase							=							clean_doc_toc(snake_case  )
																					if overwrite:
																												_lowerCAmelCase							=							new_sub_pipeline_doc
														new_pipeline_docs.append(snake_case  )

							# sort overall pipeline doc
							_lowerCAmelCase							=							clean_doc_toc(snake_case  )

							if new_pipeline_docs != pipeline_docs:
														_lowerCAmelCase							=							True
														if overwrite:
																					_lowerCAmelCase							=							new_pipeline_docs

							if diff:
														if overwrite:
																					_lowerCAmelCase							=							api_doc
																					with open(snake_case     , 'w'     , encoding='utf-8'  ) as f:
																												f.write(yaml.dump(snake_case     , allow_unicode=snake_case  )  )
														else:
																					raise ValueError(
																					    'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'  )


if __name__ == "__main__":
				_lowercase:	Tuple       			=		argparse.ArgumentParser()
				parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
				_lowercase:	Optional[Any]       			=		parser.parse_args()

				check_scheduler_doc(args.fix_and_overwrite)
				check_pipeline_doc(args.fix_and_overwrite)






