






"""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 transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=False					)					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    []
    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')					)

    # projection layer + position embeddings
    rename_keys.extend(
        [
            ('cls_token', 'vit.embeddings.cls_token'),
            ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
            ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
            ('pos_embed', 'vit.embeddings.position_embeddings'),
        ]					)

    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'),
            ]					)

    return rename_keys







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=False					)					->	Tuple:
    """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_( SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    UpperCamelCase__         =    dct.pop(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    val







def   lowerCAmelCase_( )					->	int:
    """simple docstring"""

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







@torch.no_grad()
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
    """simple docstring"""

    UpperCamelCase__         =    ViTConfig()
    UpperCamelCase__         =    False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        UpperCamelCase__         =    True
        UpperCamelCase__         =    int(vit_name[-12:-10]					)
        UpperCamelCase__         =    int(vit_name[-9:-6]					)
    else:
        UpperCamelCase__         =    10_00
        UpperCamelCase__         =    'huggingface/label-files'
        UpperCamelCase__         =    'imagenet-1k-id2label.json'
        UpperCamelCase__         =    json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	repo_type='dataset'					) ,	'r'					)					)
        UpperCamelCase__         =    {int(SCREAMING_SNAKE_CASE					): v for k, v in idalabel.items()}
        UpperCamelCase__         =    idalabel
        UpperCamelCase__         =    {v: k for k, v in idalabel.items()}
        UpperCamelCase__         =    int(vit_name[-6:-4]					)
        UpperCamelCase__         =    int(vit_name[-3:]					)
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith('tiny'					):
            UpperCamelCase__         =    1_92
            UpperCamelCase__         =    7_68
            UpperCamelCase__         =    12
            UpperCamelCase__         =    3
        elif vit_name[9:].startswith('small'					):
            UpperCamelCase__         =    3_84
            UpperCamelCase__         =    15_36
            UpperCamelCase__         =    12
            UpperCamelCase__         =    6
        else:
            pass
    else:
        if vit_name[4:].startswith('small'					):
            UpperCamelCase__         =    7_68
            UpperCamelCase__         =    23_04
            UpperCamelCase__         =    8
            UpperCamelCase__         =    8
        elif vit_name[4:].startswith('base'					):
            pass
        elif vit_name[4:].startswith('large'					):
            UpperCamelCase__         =    10_24
            UpperCamelCase__         =    40_96
            UpperCamelCase__         =    24
            UpperCamelCase__         =    16
        elif vit_name[4:].startswith('huge'					):
            UpperCamelCase__         =    12_80
            UpperCamelCase__         =    51_20
            UpperCamelCase__         =    32
            UpperCamelCase__         =    16

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

    # load HuggingFace model
    if vit_name[-5:] == "in21k":
        UpperCamelCase__         =    ViTModel(SCREAMING_SNAKE_CASE					).eval()
    else:
        UpperCamelCase__         =    ViTForImageClassification(SCREAMING_SNAKE_CASE					).eval()
    model.load_state_dict(SCREAMING_SNAKE_CASE					)

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        UpperCamelCase__         =    DeiTImageProcessor(size=config.image_size					)
    else:
        UpperCamelCase__         =    ViTImageProcessor(size=config.image_size					)
    UpperCamelCase__         =    image_processor(images=prepare_img() ,	return_tensors='pt'					)
    UpperCamelCase__         =    encoding['pixel_values']
    UpperCamelCase__         =    model(SCREAMING_SNAKE_CASE					)

    if base_model:
        UpperCamelCase__         =    timm_model.forward_features(SCREAMING_SNAKE_CASE					)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(SCREAMING_SNAKE_CASE ,	outputs.pooler_output ,	atol=1E-3					)
    else:
        UpperCamelCase__         =    timm_model(SCREAMING_SNAKE_CASE					)
        assert timm_logits.shape == outputs.logits.shape
        assert torch.allclose(SCREAMING_SNAKE_CASE ,	outputs.logits ,	atol=1E-3					)

    Path(SCREAMING_SNAKE_CASE					).mkdir(exist_ok=SCREAMING_SNAKE_CASE					)
    print(F'Saving model {vit_name} to {pytorch_dump_folder_path}'					)
    model.save_pretrained(SCREAMING_SNAKE_CASE					)
    print(F'Saving image processor to {pytorch_dump_folder_path}'					)
    image_processor.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: List[Any]=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      """--vit_name""",
      default="""vit_base_patch16_224""",
      type=str,
      help="""Name of the 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."""
  )

  A__					: Dict=	parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)












"""simple docstring"""





import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
A__					: Any=	"""src/diffusers"""

# Matches is_xxx_available()
A__					: Tuple=	re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
A__					: Any=	re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


A__					: Optional[Any]=	"""
{0} = None
"""

A__					: List[Any]=	"""
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


A__					: Dict=	"""
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    _re_backend.findall(SCREAMING_SNAKE_CASE					)
    if len(SCREAMING_SNAKE_CASE					) == 0:
        return None

    return "_and_".join(SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( )					->	str:
    """simple docstring"""

    with open(os.path.join(SCREAMING_SNAKE_CASE ,	'__init__.py'					) ,	'r' ,	encoding='utf-8' ,	newline='\n'					) as f:
        UpperCamelCase__         =    f.readlines()

    # Get to the point we do the actual imports for type checking
    UpperCamelCase__         =    0
    UpperCamelCase__         =    {}
    # Go through the end of the file
    while line_index < len(SCREAMING_SNAKE_CASE					):
        # If the line contains is_backend_available, we grab all objects associated with the `else` block
        UpperCamelCase__         =    find_backend(lines[line_index]					)
        if backend is not None:
            while not lines[line_index].startswith('else:'					):
                line_index += 1
            line_index += 1
            UpperCamelCase__         =    []
            # Until we unindent, add backend objects to the list
            while line_index < len(SCREAMING_SNAKE_CASE					) and len(lines[line_index]					) > 1:
                UpperCamelCase__         =    lines[line_index]
                UpperCamelCase__         =    _re_single_line_import.search(SCREAMING_SNAKE_CASE					)
                if single_line_import_search is not None:
                    objects.extend(single_line_import_search.groups()[0].split(', '					)					)
                elif line.startswith(' ' * 8					):
                    objects.append(line[8:-2]					)
                line_index += 1

            if len(SCREAMING_SNAKE_CASE					) > 0:
                UpperCamelCase__         =    objects
        else:
            line_index += 1

    return backend_specific_objects







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[Any]:
    """simple docstring"""

    if name.isupper():
        return DUMMY_CONSTANT.format(SCREAMING_SNAKE_CASE					)
    elif name.islower():
        return DUMMY_FUNCTION.format(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
    else:
        return DUMMY_CLASS.format(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=None					)					->	int:
    """simple docstring"""

    if backend_specific_objects is None:
        UpperCamelCase__         =    read_init()
    # For special correspondence backend to module name as used in the function requires_modulename
    UpperCamelCase__         =    {}

    for backend, objects in backend_specific_objects.items():
        UpperCamelCase__         =    '[' + ', '.join(F'"{b}"' for b in backend.split('_and_'					)					) + ']'
        UpperCamelCase__         =    '# This file is autogenerated by the command `make fix-copies`, do not edit.\n'
        dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
        dummy_file += "\n".join([create_dummy_object(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					) for o in objects]					)
        UpperCamelCase__         =    dummy_file

    return dummy_files







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE=False					)					->	int:
    """simple docstring"""

    UpperCamelCase__         =    create_dummy_files()
    # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
    UpperCamelCase__         =    {'torch': 'pt'}

    # Locate actual dummy modules and read their content.
    UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	'utils'					)
    UpperCamelCase__         =    {
        backend: os.path.join(SCREAMING_SNAKE_CASE ,	F'dummy_{short_names.get(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)}_objects.py'					)
        for backend in dummy_files.keys()
    }

    UpperCamelCase__         =    {}
    for backend, file_path in dummy_file_paths.items():
        if os.path.isfile(SCREAMING_SNAKE_CASE					):
            with open(SCREAMING_SNAKE_CASE ,	'r' ,	encoding='utf-8' ,	newline='\n'					) as f:
                UpperCamelCase__         =    f.read()
        else:
            UpperCamelCase__         =    ''

    for backend in dummy_files.keys():
        if dummy_files[backend] != actual_dummies[backend]:
            if overwrite:
                print(
                    F'Updating diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)}_objects.py as the main '
                    '__init__ has new objects.'					)
                with open(dummy_file_paths[backend] ,	'w' ,	encoding='utf-8' ,	newline='\n'					) as f:
                    f.write(dummy_files[backend]					)
            else:
                raise ValueError(
                    'The main __init__ has objects that are not present in '
                    F'diffusers.utils.dummy_{short_names.get(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)}_objects.py. Run `make fix-copies` '
                    'to fix this.'					)


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

  check_dummies(args.fix_and_overwrite)




