
"""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()
lowerCAmelCase								=						logging.get_logger(__name__)






def    lowerCAmelCase_   (		snake_case_		:			Union[str, Any]							,    snake_case_		:			List[Any]=False			)   ->List[Any]:
			lowerCamelCase__	:     Union[str, Any]              =[]

			# 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"
						lowerCamelCase__	:     Optional[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    lowerCAmelCase_   (		snake_case_		:			List[str]							,    snake_case_		:			Optional[int]							,    snake_case_		:			Dict=False			)   ->Optional[Any]:

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






def    lowerCAmelCase_   (		snake_case_		:			List[str]			)   ->Union[str, Any]:
			lowerCamelCase__	:     Optional[int]              =['head.weight', 'head.bias']
			for k in ignore_keys:
						state_dict.pop(__UpperCAmelCase							,    __UpperCAmelCase			)






def    lowerCAmelCase_   (		snake_case_		:			int							,    snake_case_		:			List[Any]							,    snake_case_		:			List[Any]			)   ->Union[str, Any]:
			lowerCamelCase__	:     int              =dct.pop(__UpperCAmelCase			)
			lowerCamelCase__	:     List[Any]              =val






def    lowerCAmelCase_   (		)   ->Tuple:
			lowerCamelCase__	:     Dict              ='http://images.cocodataset.org/val2017/000000039769.jpg'
			lowerCamelCase__	:     Union[str, Any]              =Image.open(requests.get(__UpperCAmelCase							,    stream=__UpperCAmelCase			).raw			)
			return im






@torch.no_grad()
def    lowerCAmelCase_   (		snake_case_		:			str							,    snake_case_		:			Union[str, Any]							,    snake_case_		:			int=False			)   ->int:
			lowerCamelCase__	:     Any              =BitConfig(
			    global_padding='same'							,    layer_type='bottleneck'							,    depths=(3, 4, 9)							,    out_features=['stage3']							,    embedding_dynamic_padding=__UpperCAmelCase							,    )
			lowerCamelCase__	:     str              =ViTHybridConfig(backbone_config=__UpperCAmelCase							,    image_size=3_8_4							,    num_labels=1_0_0_0			)
			lowerCamelCase__	:     Dict              =False

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

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

			lowerCamelCase__	:     Optional[int]              ='huggingface/label-files'
			lowerCamelCase__	:     Any              ='imagenet-1k-id2label.json'
			lowerCamelCase__	:     Any              =json.load(open(hf_hub_download(__UpperCAmelCase							,    __UpperCAmelCase							,    repo_type='dataset'			)							,    'r'			)			)
			lowerCamelCase__	:     Optional[int]              ={int(__UpperCAmelCase			): v for k, v in idalabel.items()}
			lowerCamelCase__	:     Tuple              =idalabel
			lowerCamelCase__	:     List[str]              ={v: k for k, v in idalabel.items()}

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

			# create image processor
			lowerCamelCase__	:     Tuple              =create_transform(**resolve_data_config({}							,    model=__UpperCAmelCase			)			)
			lowerCamelCase__	:     List[Any]              =transform.transforms

			lowerCamelCase__	:     Union[str, Any]              ={
			    'bilinear': PILImageResampling.BILINEAR,
			    'bicubic': PILImageResampling.BICUBIC,
			    'nearest': PILImageResampling.NEAREST,
			}

			lowerCamelCase__	:     Optional[Any]              =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()							,    )

			lowerCamelCase__	:     Optional[int]              =prepare_img()
			lowerCamelCase__	:     Tuple              =transform(__UpperCAmelCase			).unsqueeze(0			)
			lowerCamelCase__	:     Dict              =processor(__UpperCAmelCase							,    return_tensors='pt'			).pixel_values

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

			# verify logits
			with torch.no_grad():
						lowerCamelCase__	:     Union[str, Any]              =model(__UpperCAmelCase			)
						lowerCamelCase__	:     Optional[Any]              =outputs.logits

			print('Predicted class:'							,    logits.argmax(-1			).item()			)
			if base_model:
						lowerCamelCase__	:     int              =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:
						lowerCamelCase__	:     Optional[int]              =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__":
							lowerCAmelCase								=						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."""
							)

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





'''simple docstring'''

# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a  :				int									=  abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					config.addinivalue_line(
					    '''markers''',      '''is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested'''	)
					config.addinivalue_line(
					    '''markers''',      '''is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested'''	)
					config.addinivalue_line('''markers''',      '''is_pipeline_test: mark test to run only when pipelines are tested'''	)
					config.addinivalue_line('''markers''',      '''is_staging_test: mark test to run only in the staging environment'''	)
					config.addinivalue_line('''markers''',      '''accelerate_tests: mark test that require accelerate'''	)
					config.addinivalue_line('''markers''',      '''tool_tests: mark the tool tests that are run on their specific schedule'''	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(__UpperCAmelCase	)


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

					'''simple docstring'''

					from transformers.testing_utils import pytest_terminal_summary_main

					snake_case_									=				terminalreporter.config.getoption('''--make-reports'''	)
					if make_reports:
										pytest_terminal_summary_main(__UpperCAmelCase,      id=__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					if exitstatus == 5:
										snake_case_									=				0


# Doctest custom flag to ignore output.
a  :				Union[str, Any]									=  doctest.register_optionflag('IGNORE_RESULT')

a  :				Optional[int]									=  doctest.OutputChecker






class 						a				(   _lowerCamelCase     ):


				def   A_ (		self    :					List[Any]       ,				lowercase_    :					int       ,				lowercase_    :					Tuple       ,				lowercase_    :					Optional[int]					):
									if IGNORE_RESULT & optionflags:
														return True
									return OutputChecker.check_output(self       ,				lowercase_       ,				lowercase_       ,				lowercase_					)







a  :				List[Any]									=  CustomOutputChecker
a  :				Optional[int]									=  HfDoctestModule

a  :				Tuple									=  HfDocTestParser

