
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__   :					Optional[int]												=				logging.get_logger(__name__)

def lowercase_						(     _snake_case     ,_snake_case=False		):
							SCREAMING_SNAKE_CASE__		: str     =     []

							# 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__		: Any     =     [(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 lowercase_						(     _snake_case     ,_snake_case     ,_snake_case=False		):





							for i in range(config.num_hidden_layers		):
														if base_model:
																					SCREAMING_SNAKE_CASE__		: Optional[int]     =     """"""
														else:
																					SCREAMING_SNAKE_CASE__		: str     =     """vit."""
														# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''		)
														SCREAMING_SNAKE_CASE__		: List[Any]     =     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__		: Union[str, Any]     =     in_proj_weight[
														    : config.hidden_size, :
														]
														SCREAMING_SNAKE_CASE__		: List[str]     =     in_proj_bias[: config.hidden_size]
														SCREAMING_SNAKE_CASE__		: str     =     in_proj_weight[
														    config.hidden_size : config.hidden_size * 2, :
														]
														SCREAMING_SNAKE_CASE__		: Any     =     in_proj_bias[
														    config.hidden_size : config.hidden_size * 2
														]
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     in_proj_weight[
														    -config.hidden_size :, :
														]
														SCREAMING_SNAKE_CASE__		: Dict     =     in_proj_bias[-config.hidden_size :]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ["""head.weight""", """head.bias"""]
							for k in ignore_keys:
														state_dict.pop(__snake_case     ,__snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Any     =     dct.pop(__snake_case		)
							SCREAMING_SNAKE_CASE__		: List[Any]     =     val

def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Tuple     =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     Image.open(requests.get(__snake_case     ,stream=__snake_case		).raw		)
							return im

@torch.no_grad()
def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case=False		):
							SCREAMING_SNAKE_CASE__		: List[str]     =     BitConfig(
							    global_padding="""same"""     ,layer_type="""bottleneck"""     ,depths=(3, 4, 9)     ,out_features=["""stage3"""]     ,embedding_dynamic_padding=__snake_case     ,)
							SCREAMING_SNAKE_CASE__		: Dict     =     ViTHybridConfig(backbone_config=__snake_case     ,image_size=384     ,num_labels=1_000		)
							SCREAMING_SNAKE_CASE__		: str     =     False

							# load original model from timm
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     timm.create_model(__snake_case     ,pretrained=__snake_case		)
							timm_model.eval()

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

							SCREAMING_SNAKE_CASE__		: int     =     """huggingface/label-files"""
							SCREAMING_SNAKE_CASE__		: int     =     """imagenet-1k-id2label.json"""
							SCREAMING_SNAKE_CASE__		: int     =     json.load(open(hf_hub_download(__snake_case     ,__snake_case     ,repo_type="""dataset"""		)     ,"""r"""		)		)
							SCREAMING_SNAKE_CASE__		: Optional[Any]     =     {int(__snake_case		): v for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     idalabel
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     {v: k for k, v in idalabel.items()}

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

							# create image processor
							SCREAMING_SNAKE_CASE__		: Tuple     =     create_transform(**resolve_data_config({}     ,model=__snake_case		)		)
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     transform.transforms

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

							SCREAMING_SNAKE_CASE__		: int     =     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()     ,)

							SCREAMING_SNAKE_CASE__		: Dict     =     prepare_img()
							SCREAMING_SNAKE_CASE__		: Any     =     transform(__snake_case		).unsqueeze(0		)
							SCREAMING_SNAKE_CASE__		: Optional[int]     =     processor(__snake_case     ,return_tensors="""pt"""		).pixel_values

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

							# verify logits
							with torch.no_grad():
														SCREAMING_SNAKE_CASE__		: Optional[int]     =     model(__snake_case		)
														SCREAMING_SNAKE_CASE__		: Tuple     =     outputs.logits

							print("""Predicted class:"""     ,logits.argmax(-1		).item()		)
							if base_model:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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:
														SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     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__":
					UpperCAmelCase__   :					Optional[int]												=				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__   :					Union[str, Any]												=				parser.parse_args()
					convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)






"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def lowercase_						(     _snake_case     ,_snake_case=7		):
							SCREAMING_SNAKE_CASE__		: Dict     =     None
							if token is not None:
														SCREAMING_SNAKE_CASE__		: str     =     {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

							# The id of a workflow (not of a workflow run)
							SCREAMING_SNAKE_CASE__		: List[str]     =     """636036"""

							SCREAMING_SNAKE_CASE__		: Any     =     f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
							# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
							url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

							SCREAMING_SNAKE_CASE__		: str     =     requests.get(_snake_case     ,headers=_snake_case		).json()

							return result["workflow_runs"]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     get_daily_ci_runs(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     workflow_run["""id"""]
																					break

							return workflow_run_id

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_last_daily_ci_runs(_snake_case		)
							if workflow_run_id is not None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     get_artifacts_links(worflow_run_id=_snake_case     ,token=_snake_case		)
														for artifact_name in artifact_names:
																					if artifact_name in artifacts_links:
																												SCREAMING_SNAKE_CASE__		: Tuple     =     artifacts_links[artifact_name]
																												download_artifact(
																												    artifact_name=_snake_case     ,artifact_url=_snake_case     ,output_dir=_snake_case     ,token=_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							get_last_daily_ci_artifacts(_snake_case     ,_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     {}
							for artifact_name in artifact_names:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(_snake_case     ,f'''{artifact_name}.zip'''		)
														if os.path.isfile(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     {}
																					with zipfile.ZipFile(_snake_case		) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_snake_case		):
																																										# read the file
																																										with z.open(_snake_case		) as f:
																																																	SCREAMING_SNAKE_CASE__		: List[Any]     =     f.read().decode("""UTF-8"""		)

							return results




