




import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
_lowerCamelCase      			=						'src/diffusers'
_lowerCamelCase      			=						'.'


# This is to make sure the diffusers module imported is the one in the repo.
_lowerCamelCase      			=						importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
_lowerCamelCase      			=						spec.loader.load_module()




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[Any]				)  ->							Tuple:
    return line.startswith(__UpperCamelCase				) or len(__UpperCamelCase				) <= 1 or re.search(R'''^\s*\)(\s*->.*:|:)\s*$'''	,					__UpperCamelCase				) is not None




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							List[str]:
    UpperCAmelCase_										=					object_name.split('''.'''				)
    UpperCAmelCase_										=					0

    # First let's find the module where our object lives.
    UpperCAmelCase_										=					parts[i]
    while i < len(__UpperCamelCase				) and not os.path.isfile(os.path.join(__UpperCamelCase	,					f'{module}.py'				)				):
        i += 1
        if i < len(__UpperCamelCase				):
            UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					parts[i]				)
    if i >= len(__UpperCamelCase				):
        raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.'				)

    with open(os.path.join(__UpperCamelCase	,					f'{module}.py'				)	,					'''r'''	,					encoding='''utf-8'''	,					newline='''\n'''				) as f:
        UpperCAmelCase_										=					f.readlines()

    # Now let's find the class / func in the code!
    UpperCAmelCase_										=					''''''
    UpperCAmelCase_										=					0
    for name in parts[i + 1 :]:
        while (
            line_index < len(__UpperCamelCase				) and re.search(Rf'^{indent}(class|def)\s+{name}(\(|\:)'	,					lines[line_index]				) is None
        ):
            line_index += 1
        indent += "    "
        line_index += 1

    if line_index >= len(__UpperCamelCase				):
        raise ValueError(f' {object_name} does not match any function or class in {module}.'				)

    # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
    UpperCAmelCase_										=					line_index
    while line_index < len(__UpperCamelCase				) and _should_continue(lines[line_index]	,					__UpperCamelCase				):
        line_index += 1
    # Clean up empty lines at the end (if any).
    while len(lines[line_index - 1]				) <= 1:
        line_index -= 1

    UpperCAmelCase_										=					lines[start_index:line_index]
    return "".join(__UpperCamelCase				)


_lowerCamelCase      			=						re.compile(r'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
_lowerCamelCase      			=						re.compile(r'^\s*(\S+)->(\S+)(\s+.*|$)')
_lowerCamelCase      			=						re.compile(r'<FILL\s+[^>]*>')




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Optional[int]:
    UpperCAmelCase_										=					code.split('''\n'''				)
    UpperCAmelCase_										=					0
    while idx < len(__UpperCamelCase				) and len(lines[idx]				) == 0:
        idx += 1
    if idx < len(__UpperCamelCase				):
        return re.search(R'''^(\s*)\S'''	,					lines[idx]				).groups()[0]
    return ""




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					len(get_indent(__UpperCamelCase				)				) > 0
    if has_indent:
        UpperCAmelCase_										=					f'class Bla:\n{code}'
    UpperCAmelCase_										=					black.Mode(target_versions={black.TargetVersion.PYaa}	,					line_length=119	,					preview=__UpperCamelCase				)
    UpperCAmelCase_										=					black.format_str(__UpperCamelCase	,					mode=__UpperCamelCase				)
    UpperCAmelCase_			,							UpperCAmelCase_										=					style_docstrings_in_code(__UpperCamelCase				)
    return result[len('''class Bla:\n'''				) :] if has_indent else result




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						List[Any]=False				)  ->							List[str]:
    with open(__UpperCamelCase	,					'''r'''	,					encoding='''utf-8'''	,					newline='''\n'''				) as f:
        UpperCAmelCase_										=					f.readlines()
    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					0
    # Not a for loop cause `lines` is going to change (if `overwrite=True`).
    while line_index < len(__UpperCamelCase				):
        UpperCAmelCase_										=					_re_copy_warning.search(lines[line_index]				)
        if search is None:
            line_index += 1
            continue

        # There is some copied code here, let's retrieve the original.
        UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					search.groups()
        UpperCAmelCase_										=					find_code_in_diffusers(__UpperCamelCase				)
        UpperCAmelCase_										=					get_indent(__UpperCamelCase				)

        UpperCAmelCase_										=					line_index + 1 if indent == theoretical_indent else line_index + 2
        UpperCAmelCase_										=					theoretical_indent
        UpperCAmelCase_										=					start_index

        # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
        UpperCAmelCase_										=					True
        while line_index < len(__UpperCamelCase				) and should_continue:
            line_index += 1
            if line_index >= len(__UpperCamelCase				):
                break
            UpperCAmelCase_										=					lines[line_index]
            UpperCAmelCase_										=					_should_continue(__UpperCamelCase	,					__UpperCamelCase				) and re.search(f'^{indent}# End copy'	,					__UpperCamelCase				) is None
        # Clean up empty lines at the end (if any).
        while len(lines[line_index - 1]				) <= 1:
            line_index -= 1

        UpperCAmelCase_										=					lines[start_index:line_index]
        UpperCAmelCase_										=					''''''.join(__UpperCamelCase				)

        # Remove any nested `Copied from` comments to avoid circular copies
        UpperCAmelCase_										=					[line for line in theoretical_code.split('''\n'''				) if _re_copy_warning.search(__UpperCamelCase				) is None]
        UpperCAmelCase_										=					'''\n'''.join(__UpperCamelCase				)

        # Before comparing, use the `replace_pattern` on the original code.
        if len(__UpperCamelCase				) > 0:
            UpperCAmelCase_										=					replace_pattern.replace('''with'''	,					''''''				).split(''','''				)
            UpperCAmelCase_										=					[_re_replace_pattern.search(__UpperCamelCase				) for p in patterns]
            for pattern in patterns:
                if pattern is None:
                    continue
                UpperCAmelCase_			,							UpperCAmelCase_			,							UpperCAmelCase_										=					pattern.groups()
                UpperCAmelCase_										=					re.sub(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
                if option.strip() == "all-casing":
                    UpperCAmelCase_										=					re.sub(obja.lower()	,					obja.lower()	,					__UpperCamelCase				)
                    UpperCAmelCase_										=					re.sub(obja.upper()	,					obja.upper()	,					__UpperCamelCase				)

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
            UpperCAmelCase_										=					blackify(lines[start_index - 1] + theoretical_code				)
            UpperCAmelCase_										=					theoretical_code[len(lines[start_index - 1]				) :]

        # Test for a diff and act accordingly.
        if observed_code != theoretical_code:
            diffs.append([object_name, start_index]				)
            if overwrite:
                UpperCAmelCase_										=					lines[:start_index] + [theoretical_code] + lines[line_index:]
                UpperCAmelCase_										=					start_index + 1

    if overwrite and len(__UpperCamelCase				) > 0:
        # Warn the user a file has been modified.
        print(f'Detected changes, rewriting {filename}.'				)
        with open(__UpperCamelCase	,					'''w'''	,					encoding='''utf-8'''	,					newline='''\n'''				) as f:
            f.writelines(__UpperCamelCase				)
    return diffs




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						bool = False				)  ->							Optional[Any]:
    UpperCAmelCase_										=					glob.glob(os.path.join(__UpperCamelCase	,					'''**/*.py'''				)	,					recursive=__UpperCamelCase				)
    UpperCAmelCase_										=					[]
    for filename in all_files:
        UpperCAmelCase_										=					is_copy_consistent(__UpperCamelCase	,					__UpperCamelCase				)
        diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
    if not overwrite and len(__UpperCamelCase				) > 0:
        UpperCAmelCase_										=					'''\n'''.join(__UpperCamelCase				)
        raise Exception(
            '''Found the following copy inconsistencies:\n'''
            + diff
            + '''\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.'''				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
 _lowerCamelCase      			=						parser.parse_args()

 check_copies(args.fix_and_overwrite)











import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase      			=						logging.get_logger(__name__)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple				)  ->							Dict:
    # initialize config
    if "resnet-50" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-50'''				)
    elif "resnet-101" in model_name:
        UpperCAmelCase_										=					ResNetConfig.from_pretrained('''microsoft/resnet-101'''				)
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101'''				)

    UpperCAmelCase_										=					DetrConfig(use_timm_backbone=__UpperCamelCase	,					backbone_config=__UpperCamelCase				)

    # set label attributes
    UpperCAmelCase_										=					'''panoptic''' in model_name
    if is_panoptic:
        UpperCAmelCase_										=					250
    else:
        UpperCAmelCase_										=					91
        UpperCAmelCase_										=					'''huggingface/label-files'''
        UpperCAmelCase_										=					'''coco-detection-id2label.json'''
        UpperCAmelCase_										=					json.load(open(hf_hub_download(__UpperCamelCase	,					__UpperCamelCase	,					repo_type='''dataset'''				)	,					'''r'''				)				)
        UpperCAmelCase_										=					{int(__UpperCamelCase				): v for k, v in idalabel.items()}
        UpperCAmelCase_										=					idalabel
        UpperCAmelCase_										=					{v: k for k, v in idalabel.items()}

    return config, is_panoptic




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]				)  ->							Union[str, Any]:
    # here we list all keys to be renamed (original name on the left, our name on the right)
    UpperCAmelCase_										=					[]

    # stem
    # fmt: off
    rename_keys.append(('''backbone.0.body.conv1.weight''', '''backbone.conv_encoder.model.embedder.embedder.convolution.weight''')				)
    rename_keys.append(('''backbone.0.body.bn1.weight''', '''backbone.conv_encoder.model.embedder.embedder.normalization.weight''')				)
    rename_keys.append(('''backbone.0.body.bn1.bias''', '''backbone.conv_encoder.model.embedder.embedder.normalization.bias''')				)
    rename_keys.append(('''backbone.0.body.bn1.running_mean''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_mean''')				)
    rename_keys.append(('''backbone.0.body.bn1.running_var''', '''backbone.conv_encoder.model.embedder.embedder.normalization.running_var''')				)
    # stages
    for stage_idx in range(len(config.backbone_config.depths				)				):
        for layer_idx in range(config.backbone_config.depths[stage_idx]				):
            # shortcut
            if layer_idx == 0:
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var',
                    )				)
            # 3 convs
            for i in range(3				):
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean',
                    )				)
                rename_keys.append(
                    (
                        f'backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var',
                        f'backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var',
                    )				)
    # fmt: on

    for i in range(config.encoder_layers				):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append(
            (
                f'transformer.encoder.layers.{i}.self_attn.out_proj.weight',
                f'encoder.layers.{i}.self_attn.out_proj.weight',
            )				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.self_attn.out_proj.bias', f'encoder.layers.{i}.self_attn.out_proj.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.weight', f'encoder.layers.{i}.fc1.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear1.bias', f'encoder.layers.{i}.fc1.bias')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.weight', f'encoder.layers.{i}.fc2.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.linear2.bias', f'encoder.layers.{i}.fc2.bias')				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.norm1.weight', f'encoder.layers.{i}.self_attn_layer_norm.weight')				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.norm1.bias', f'encoder.layers.{i}.self_attn_layer_norm.bias')				)
        rename_keys.append(
            (f'transformer.encoder.layers.{i}.norm2.weight', f'encoder.layers.{i}.final_layer_norm.weight')				)
        rename_keys.append((f'transformer.encoder.layers.{i}.norm2.bias', f'encoder.layers.{i}.final_layer_norm.bias')				)
        # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
        rename_keys.append(
            (
                f'transformer.decoder.layers.{i}.self_attn.out_proj.weight',
                f'decoder.layers.{i}.self_attn.out_proj.weight',
            )				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.self_attn.out_proj.bias', f'decoder.layers.{i}.self_attn.out_proj.bias')				)
        rename_keys.append(
            (
                f'transformer.decoder.layers.{i}.multihead_attn.out_proj.weight',
                f'decoder.layers.{i}.encoder_attn.out_proj.weight',
            )				)
        rename_keys.append(
            (
                f'transformer.decoder.layers.{i}.multihead_attn.out_proj.bias',
                f'decoder.layers.{i}.encoder_attn.out_proj.bias',
            )				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.weight', f'decoder.layers.{i}.fc1.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear1.bias', f'decoder.layers.{i}.fc1.bias')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.weight', f'decoder.layers.{i}.fc2.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.linear2.bias', f'decoder.layers.{i}.fc2.bias')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm1.weight', f'decoder.layers.{i}.self_attn_layer_norm.weight')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm1.bias', f'decoder.layers.{i}.self_attn_layer_norm.bias')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm2.weight', f'decoder.layers.{i}.encoder_attn_layer_norm.weight')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm2.bias', f'decoder.layers.{i}.encoder_attn_layer_norm.bias')				)
        rename_keys.append(
            (f'transformer.decoder.layers.{i}.norm3.weight', f'decoder.layers.{i}.final_layer_norm.weight')				)
        rename_keys.append((f'transformer.decoder.layers.{i}.norm3.bias', f'decoder.layers.{i}.final_layer_norm.bias')				)

    # convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
    rename_keys.extend(
        [
            ('''input_proj.weight''', '''input_projection.weight'''),
            ('''input_proj.bias''', '''input_projection.bias'''),
            ('''query_embed.weight''', '''query_position_embeddings.weight'''),
            ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
            ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
            ('''class_embed.weight''', '''class_labels_classifier.weight'''),
            ('''class_embed.bias''', '''class_labels_classifier.bias'''),
            ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
            ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
            ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
            ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
            ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
            ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
        ]				)

    return rename_keys




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
    UpperCAmelCase_										=					val




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						List[Any]=False				)  ->							Dict:
    UpperCAmelCase_										=					''''''
    if is_panoptic:
        UpperCAmelCase_										=					'''detr.'''

    # first: transformer encoder
    for i in range(6				):
        # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'				)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'				)
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase_										=					in_proj_weight[:256, :]
        UpperCAmelCase_										=					in_proj_bias[:256]
        UpperCAmelCase_										=					in_proj_weight[256:512, :]
        UpperCAmelCase_										=					in_proj_bias[256:512]
        UpperCAmelCase_										=					in_proj_weight[-256:, :]
        UpperCAmelCase_										=					in_proj_bias[-256:]
    # next: transformer decoder (which is a bit more complex because it also includes cross-attention)
    for i in range(6				):
        # read in weights + bias of input projection layer of self-attention
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'				)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias'				)
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase_										=					in_proj_weight[:256, :]
        UpperCAmelCase_										=					in_proj_bias[:256]
        UpperCAmelCase_										=					in_proj_weight[256:512, :]
        UpperCAmelCase_										=					in_proj_bias[256:512]
        UpperCAmelCase_										=					in_proj_weight[-256:, :]
        UpperCAmelCase_										=					in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        UpperCAmelCase_										=					state_dict.pop(
            f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'				)
        UpperCAmelCase_										=					state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias'				)
        # next, add query, keys and values (in that order) of cross-attention to the state dict
        UpperCAmelCase_										=					in_proj_weight_cross_attn[:256, :]
        UpperCAmelCase_										=					in_proj_bias_cross_attn[:256]
        UpperCAmelCase_										=					in_proj_weight_cross_attn[256:512, :]
        UpperCAmelCase_										=					in_proj_bias_cross_attn[256:512]
        UpperCAmelCase_										=					in_proj_weight_cross_attn[-256:, :]
        UpperCAmelCase_										=					in_proj_bias_cross_attn[-256:]




def  SCREAMING_SNAKE_CASE     (	)  ->							int:
    UpperCAmelCase_										=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase_										=					Image.open(requests.get(__UpperCamelCase	,					stream=__UpperCamelCase				).raw				)

    return im




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						Any=None	,					__UpperCamelCase      :						Optional[Any]=False				)  ->							Optional[Any]:
    UpperCAmelCase_			,							UpperCAmelCase_										=					get_detr_config(__UpperCamelCase				)

    # load original model from torch hub
    UpperCAmelCase_										=					{
        '''detr-resnet-50''': '''detr_resnet50''',
        '''detr-resnet-101''': '''detr_resnet101''',
    }
    logger.info(f'Converting model {model_name}...'				)
    UpperCAmelCase_										=					torch.hub.load('''facebookresearch/detr'''	,					model_name_to_original_name[model_name]	,					pretrained=__UpperCamelCase				).eval()
    UpperCAmelCase_										=					detr.state_dict()
    # rename keys
    for src, dest in create_rename_keys(__UpperCamelCase				):
        if is_panoptic:
            UpperCAmelCase_										=					'''detr.''' + src
        rename_key(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
    # query, key and value matrices need special treatment
    read_in_q_k_v(__UpperCamelCase	,					is_panoptic=__UpperCamelCase				)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    UpperCAmelCase_										=					'''detr.model.''' if is_panoptic else '''model.'''
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('''detr'''				)
                and not key.startswith('''class_labels_classifier'''				)
                and not key.startswith('''bbox_predictor'''				)
            ):
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
            elif key.startswith('''bbox_attention'''				) or key.startswith('''mask_head'''				):
                continue
            else:
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val
        else:
            if not key.startswith('''class_labels_classifier'''				) and not key.startswith('''bbox_predictor'''				):
                UpperCAmelCase_										=					state_dict.pop(__UpperCamelCase				)
                UpperCAmelCase_										=					val

    # finally, create HuggingFace model and load state dict
    UpperCAmelCase_										=					DetrForSegmentation(__UpperCamelCase				) if is_panoptic else DetrForObjectDetection(__UpperCamelCase				)
    model.load_state_dict(__UpperCamelCase				)
    model.eval()

    # verify our conversion on an image
    UpperCAmelCase_										=					'''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCAmelCase_										=					DetrImageProcessor(format=__UpperCamelCase				)

    UpperCAmelCase_										=					processor(images=prepare_img()	,					return_tensors='''pt'''				)
    UpperCAmelCase_										=					encoding['''pixel_values''']

    UpperCAmelCase_										=					detr(__UpperCamelCase				)
    UpperCAmelCase_										=					model(__UpperCamelCase				)

    assert torch.allclose(outputs.logits	,					original_outputs['''pred_logits''']	,					atol=1e-3				)
    assert torch.allclose(outputs.pred_boxes	,					original_outputs['''pred_boxes''']	,					atol=1e-3				)
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks	,					original_outputs['''pred_masks''']	,					atol=1e-4				)
    print('''Looks ok!'''				)

    if pytorch_dump_folder_path is not None:
        # Save model and image processor
        logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'				)
        Path(__UpperCamelCase				).mkdir(exist_ok=__UpperCamelCase				)
        model.save_pretrained(__UpperCamelCase				)
        processor.save_pretrained(__UpperCamelCase				)

    if push_to_hub:
        # Upload model and image processor to the hub
        logger.info('''Uploading PyTorch model and image processor to the hub...'''				)
        model.push_to_hub(f'nielsr/{model_name}'				)
        processor.push_to_hub(f'nielsr/{model_name}'				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()

 parser.add_argument(
     '--model_name',
     default='detr-resnet-50',
     type=str,
     choices=['detr-resnet-50', 'detr-resnet-101'],
     help='Name of the DETR model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
 )
 parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the model to the hub or not.')
 _lowerCamelCase      			=						parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





