"""simple docstring"""






import argparse
import os
import re


lowerCAmelCase__  :  List[Any]						     =	'''src/diffusers'''

# Pattern that looks at the indentation in a line.
lowerCAmelCase__  :  Union[str, Any]						     =	re.compile(r'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
lowerCAmelCase__  :  int						     =	re.compile(r'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
lowerCAmelCase__  :  Dict						     =	re.compile(r'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
lowerCAmelCase__  :  Union[str, Any]						     =	re.compile(r'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
lowerCAmelCase__  :  Optional[Any]						     =	re.compile(r'\[([^\]]+)\]')



def   a_						(					lowerCamelCase       ):
				UpperCAmelCase__             =							_re_indent.search(_a       )
				return "" if search is None else search.groups()[0]



def   a_						(					lowerCamelCase					,			lowerCamelCase=""					,			lowerCamelCase=None					,			lowerCamelCase=None       ):
				UpperCAmelCase__             =							0
				UpperCAmelCase__             =							code.split('\n'       )
				if start_prompt is not None:
								while not lines[index].startswith(_a       ):
												index += 1
								UpperCAmelCase__             =							['''\n'''.join(lines[:index]       )]
				else:
								UpperCAmelCase__             =							[]

				# We split into blocks until we get to the `end_prompt` (or the end of the block).
				UpperCAmelCase__             =							[lines[index]]
				index += 1
				while index < len(_a       ) and (end_prompt is None or not lines[index].startswith(_a       )):
								if len(lines[index]       ) > 0 and get_indent(lines[index]       ) == indent_level:
												if len(_a       ) > 0 and get_indent(current_block[-1]       ).startswith(indent_level + ' '       ):
																current_block.append(lines[index]       )
																blocks.append('\n'.join(_a       )       )
																if index < len(_a       ) - 1:
																				UpperCAmelCase__             =							[lines[index + 1]]
																				index += 1
																else:
																				UpperCAmelCase__             =							[]
												else:
																blocks.append('\n'.join(_a       )       )
																UpperCAmelCase__             =							[lines[index]]
								else:
												current_block.append(lines[index]       )
								index += 1

				# Adds current block if it's nonempty.
				if len(_a       ) > 0:
								blocks.append('\n'.join(_a       )       )

				# Add final block after end_prompt if provided.
				if end_prompt is not None and index < len(_a       ):
								blocks.append('\n'.join(lines[index:]       )       )

				return blocks



def   a_						(					lowerCamelCase       ):

				def _inner(lowerCamelCase       ):
								return key(_a       ).lower().replace('_'					,			''       )

				return _inner



def   a_						(					lowerCamelCase					,			lowerCamelCase=None       ):

				# If no key is provided, we use a noop.
				def noop(lowerCamelCase       ):
								return x

				if key is None:
								UpperCAmelCase__             =							noop
				# Constants are all uppercase, they go first.
				UpperCAmelCase__             =							[obj for obj in objects if key(_a       ).isupper()]
				# Classes are not all uppercase but start with a capital, they go second.
				UpperCAmelCase__             =							[obj for obj in objects if key(_a       )[0].isupper() and not key(_a       ).isupper()]
				# Functions begin with a lowercase, they go last.
				UpperCAmelCase__             =							[obj for obj in objects if not key(_a       )[0].isupper()]

				UpperCAmelCase__             =							ignore_underscore(_a       )
				return sorted(_a					,			key=_a       ) + sorted(_a					,			key=_a       ) + sorted(_a					,			key=_a       )



def   a_						(					lowerCamelCase       ):

				# This inner function sort imports between [ ].
				def _replace(lowerCamelCase       ):
								UpperCAmelCase__             =							match.groups()[0]
								if "," not in imports:
												return f'''[{imports}]'''
								UpperCAmelCase__             =							[part.strip().replace('"'					,			''       ) for part in imports.split(','       )]
								# We will have a final empty element if the line finished with a comma.
								if len(keys[-1]       ) == 0:
												UpperCAmelCase__             =							keys[:-1]
								return "[" + ", ".join([f'''\"{k}\"''' for k in sort_objects(_a       )]       ) + "]"

				UpperCAmelCase__             =							import_statement.split('\n'       )
				if len(_a       ) > 3:
								# Here we have to sort internal imports that are on several lines (one per name):
								# key: [
								#     "object1",
								#     "object2",
								#     ...
								# ]

								# We may have to ignore one or two lines on each side.
								UpperCAmelCase__             =							2 if lines[1].strip() == '''[''' else 1
								UpperCAmelCase__             =							[(i, _re_strip_line.search(_a       ).groups()[0]) for i, line in enumerate(lines[idx:-idx]       )]
								UpperCAmelCase__             =							sort_objects(_a					,			key=lambda lowerCamelCase       : x[1]       )
								UpperCAmelCase__             =							[lines[x[0] + idx] for x in sorted_indices]
								return "\n".join(lines[:idx] + sorted_lines + lines[-idx:]       )
				elif len(_a       ) == 3:
								# Here we have to sort internal imports that are on one separate line:
								# key: [
								#     "object1", "object2", ...
								# ]
								if _re_bracket_content.search(lines[1]       ) is not None:
												UpperCAmelCase__             =							_re_bracket_content.sub(_replace					,			lines[1]       )
								else:
												UpperCAmelCase__             =							[part.strip().replace('"'					,			''       ) for part in lines[1].split(','       )]
												# We will have a final empty element if the line finished with a comma.
												if len(keys[-1]       ) == 0:
																UpperCAmelCase__             =							keys[:-1]
												UpperCAmelCase__             =							get_indent(lines[1]       ) + ''', '''.join([f'''\"{k}\"''' for k in sort_objects(_a       )]       )
								return "\n".join(_a       )
				else:
								# Finally we have to deal with imports fitting on one line
								UpperCAmelCase__             =							_re_bracket_content.sub(_replace					,			_a       )
								return import_statement



def   a_						(					lowerCamelCase					,			lowerCamelCase=True       ):
				with open(_a					,			'r'       ) as f:
								UpperCAmelCase__             =							f.read()

				if "_import_structure" not in code:
								return

				# Blocks of indent level 0
				UpperCAmelCase__             =							split_code_in_indented_blocks(
				    _a					,			start_prompt='_import_structure = {'					,			end_prompt='if TYPE_CHECKING:'       )

				# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
				for block_idx in range(1					,			len(_a       ) - 1       ):
								# Check if the block contains some `_import_structure`s thingy to sort.
								UpperCAmelCase__             =							main_blocks[block_idx]
								UpperCAmelCase__             =							block.split('\n'       )

								# Get to the start of the imports.
								UpperCAmelCase__             =							0
								while line_idx < len(_a       ) and "_import_structure" not in block_lines[line_idx]:
												# Skip dummy import blocks
												if "import dummy" in block_lines[line_idx]:
																UpperCAmelCase__             =							len(_a       )
												else:
																line_idx += 1
								if line_idx >= len(_a       ):
												continue

								# Ignore beginning and last line: they don't contain anything.
								UpperCAmelCase__             =							'''\n'''.join(block_lines[line_idx:-1]       )
								UpperCAmelCase__             =							get_indent(block_lines[1]       )
								# Slit the internal block into blocks of indent level 1.
								UpperCAmelCase__             =							split_code_in_indented_blocks(_a					,			indent_level=_a       )
								# We have two categories of import key: list or _import_structure[key].append/extend
								UpperCAmelCase__             =							_re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
								# Grab the keys, but there is a trap: some lines are empty or just comments.
								UpperCAmelCase__             =							[(pattern.search(_a       ).groups()[0] if pattern.search(_a       ) is not None else None) for b in internal_blocks]
								# We only sort the lines with a key.
								UpperCAmelCase__             =							[(i, key) for i, key in enumerate(_a       ) if key is not None]
								UpperCAmelCase__             =							[x[0] for x in sorted(_a					,			key=lambda lowerCamelCase       : x[1]       )]

								# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
								UpperCAmelCase__             =							0
								UpperCAmelCase__             =							[]
								for i in range(len(_a       )       ):
												if keys[i] is None:
																reordered_blocks.append(internal_blocks[i]       )
												else:
																UpperCAmelCase__             =							sort_objects_in_import(internal_blocks[sorted_indices[count]]       )
																reordered_blocks.append(_a       )
																count += 1

        # And we put our main block back together with its first and last line.
								UpperCAmelCase__             =							'''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]]       )

				if code != "\n".join(_a       ):
								if check_only:
												return True
								else:
												print(f'''Overwriting {file}.'''       )
												with open(_a					,			'w'       ) as f:
																f.write('\n'.join(_a       )       )



def   a_						(					lowerCamelCase=True       ):
				UpperCAmelCase__             =							[]
				for root, _, files in os.walk(_a       ):
								if "__init__.py" in files:
												UpperCAmelCase__             =							sort_imports(os.path.join(_a					,			'__init__.py'       )					,			check_only=_a       )
												if result:
																UpperCAmelCase__             =							[os.path.join(_a					,			'__init__.py'       )]
				if len(_a       ) > 0:
								raise ValueError(f'''Would overwrite {len(_a       )} files, run `make style`.'''       )


if __name__ == "__main__":
						lowerCAmelCase__  :  Any						     =	argparse.ArgumentParser()
						parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
						lowerCAmelCase__  :  Dict						     =	parser.parse_args()

						sort_imports_in_all_inits(check_only=args.check_only)








"""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()
lowercase__    :  Dict							    =	logging.get_logger(__name__)




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




def __lowercase			(							_a    ):
    snake_case_ :     Dict				=	['''head.weight''', '''head.bias''']
    for k in ignore_keys:
        state_dict.pop(_a		,			_a    )




def __lowercase			(							_a		,			_a		,			_a    ):
    snake_case_ :     Union[str, Any]				=	dct.pop(_a    )
    snake_case_ :     Union[str, Any]				=	val




def __lowercase			(							):
    snake_case_ :     Any				=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    snake_case_ :     Tuple				=	Image.open(requests.get(_a		,			stream=_a    ).raw    )
    return im




@torch.no_grad()
def __lowercase			(							_a		,			_a		,			_a=False    ):
    snake_case_ :     str				=	BitConfig(
        global_padding='''same'''		,			layer_type='''bottleneck'''		,			depths=(3, 4, 9)		,			out_features=['''stage3''']		,			embedding_dynamic_padding=_a		,			)
    snake_case_ :     Tuple				=	ViTHybridConfig(backbone_config=_a		,			image_size=384		,			num_labels=1_000    )
    snake_case_ :     int				=	False

    # load original model from timm
    snake_case_ :     str				=	timm.create_model(_a		,			pretrained=_a    )
    timm_model.eval()

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

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

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

    # create image processor
    snake_case_ :     Optional[Any]				=	create_transform(**resolve_data_config({}		,			model=_a    )    )
    snake_case_ :     List[Any]				=	transform.transforms

    snake_case_ :     Optional[Any]				=	{
        '''bilinear''': PILImageResampling.BILINEAR,
        '''bicubic''': PILImageResampling.BICUBIC,
        '''nearest''': PILImageResampling.NEAREST,
    }

    snake_case_ :     List[Any]				=	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()		,			)

    snake_case_ :     Optional[int]				=	prepare_img()
    snake_case_ :     Optional[int]				=	transform(_a    ).unsqueeze(0    )
    snake_case_ :     int				=	processor(_a		,			return_tensors='''pt'''    ).pixel_values

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

    # verify logits
    with torch.no_grad():
        snake_case_ :     List[str]				=	model(_a    )
        snake_case_ :     Any				=	outputs.logits

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

     lowercase__    :  Any							    =	parser.parse_args()
     convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)





