




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

_lowerCamelCase      			=						{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Any	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						Any				)  ->							int:
    for attribute in key.split('''.'''				):
        UpperCAmelCase_										=					getattr(__UpperCamelCase	,					__UpperCamelCase				)

    if weight_type is not None:
        UpperCAmelCase_										=					getattr(__UpperCamelCase	,					__UpperCamelCase				).shape
    else:
        UpperCAmelCase_										=					hf_pointer.shape

    assert hf_shape == value.shape, (
        f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
        f' {value.shape} for {full_name}'
    )

    if weight_type == "weight":
        UpperCAmelCase_										=					value
    elif weight_type == "weight_g":
        UpperCAmelCase_										=					value
    elif weight_type == "weight_v":
        UpperCAmelCase_										=					value
    elif weight_type == "bias":
        UpperCAmelCase_										=					value
    else:
        UpperCAmelCase_										=					value

    logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Optional[Any]				)  ->							Dict:
    UpperCAmelCase_										=					[]
    UpperCAmelCase_										=					fairseq_model.state_dict()

    UpperCAmelCase_										=					hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCAmelCase_										=					False
        if "conv_layers" in name:
            load_conv_layer(
                __UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					hf_model.config.feat_extract_norm == '''group'''	,					)
            UpperCAmelCase_										=					True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCAmelCase_										=					"sew." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                if key in name or key.split('''w2v_model.'''				)[-1] == name.split('''.'''				)[0]:
                    UpperCAmelCase_										=					True
                    if "*" in mapped_key:
                        UpperCAmelCase_										=					name.split(__UpperCamelCase				)[0].split('''.'''				)[-2]
                        UpperCAmelCase_										=					mapped_key.replace('''*'''	,					__UpperCamelCase				)
                    if "weight_g" in name:
                        UpperCAmelCase_										=					"weight_g"
                    elif "weight_v" in name:
                        UpperCAmelCase_										=					"weight_v"
                    elif "weight" in name:
                        UpperCAmelCase_										=					"weight"
                    elif "bias" in name:
                        UpperCAmelCase_										=					"bias"
                    else:
                        UpperCAmelCase_										=					None
                    set_recursively(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)
                continue
        if not is_used:
            unused_weights.append(__UpperCamelCase				)

    logger.warning(f'Unused weights: {unused_weights}'				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						str	,					__UpperCamelCase      :						str				)  ->							int:
    UpperCAmelCase_										=					full_name.split('''conv_layers.'''				)[-1]
    UpperCAmelCase_										=					name.split('''.'''				)
    UpperCAmelCase_										=					int(items[0]				)
    UpperCAmelCase_										=					int(items[1]				)

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
            )
            UpperCAmelCase_										=					value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
            )
            UpperCAmelCase_										=					value
            logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'				)
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                f'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
                " found."
            )
            UpperCAmelCase_										=					value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                f'{full_name} has size {value.shape}, but'
                f' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
            )
            UpperCAmelCase_										=					value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'				)
    else:
        unused_weights.append(__UpperCamelCase				)




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Any				)  ->							Optional[int]:
    UpperCAmelCase_										=					SEWConfig()
    if is_finetuned:
        UpperCAmelCase_										=					model.wav_encoder.wav_model.cfg
    else:
        UpperCAmelCase_										=					model.cfg

    UpperCAmelCase_										=					fs_config.conv_bias
    UpperCAmelCase_										=					eval(fs_config.conv_feature_layers				)
    UpperCAmelCase_										=					[x[0] for x in conv_layers]
    UpperCAmelCase_										=					[x[1] for x in conv_layers]
    UpperCAmelCase_										=					[x[2] for x in conv_layers]
    UpperCAmelCase_										=					"gelu"
    UpperCAmelCase_										=					"layer" if fs_config.extractor_mode == "layer_norm" else "group"
    UpperCAmelCase_										=					0.0
    UpperCAmelCase_										=					fs_config.activation_fn.name
    UpperCAmelCase_										=					fs_config.encoder_embed_dim
    UpperCAmelCase_										=					0.02
    UpperCAmelCase_										=					fs_config.encoder_ffn_embed_dim
    UpperCAmelCase_										=					1e-5
    UpperCAmelCase_										=					fs_config.encoder_layerdrop
    UpperCAmelCase_										=					fs_config.encoder_attention_heads
    UpperCAmelCase_										=					fs_config.conv_pos_groups
    UpperCAmelCase_										=					fs_config.conv_pos
    UpperCAmelCase_										=					len(__UpperCamelCase				)
    UpperCAmelCase_										=					fs_config.encoder_layers
    UpperCAmelCase_										=					fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        UpperCAmelCase_										=					model.cfg
        UpperCAmelCase_										=					fs_config.final_dropout
        UpperCAmelCase_										=					fs_config.layerdrop
    UpperCAmelCase_										=					fs_config.activation_dropout
    UpperCAmelCase_										=					fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    UpperCAmelCase_										=					fs_config.attention_dropout
    UpperCAmelCase_										=					fs_config.dropout_input
    UpperCAmelCase_										=					fs_config.dropout
    UpperCAmelCase_										=					fs_config.mask_channel_length
    UpperCAmelCase_										=					fs_config.mask_channel_prob
    UpperCAmelCase_										=					fs_config.mask_length
    UpperCAmelCase_										=					fs_config.mask_prob

    UpperCAmelCase_										=					"Wav2Vec2FeatureExtractor"
    UpperCAmelCase_										=					"Wav2Vec2CTCTokenizer"

    return config




@torch.no_grad()
def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						Dict	,					__UpperCamelCase      :						Dict=None	,					__UpperCamelCase      :						Union[str, Any]=None	,					__UpperCamelCase      :						List[str]=True				)  ->							Union[str, Any]:

    if is_finetuned:
        UpperCAmelCase_										=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]	,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''				)[:-1]				)}				)
    else:
        UpperCAmelCase_										=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]				)

    if config_path is not None:
        UpperCAmelCase_										=					SEWConfig.from_pretrained(__UpperCamelCase				)
    else:
        UpperCAmelCase_										=					convert_config(model[0]	,					__UpperCamelCase				)
    UpperCAmelCase_										=					model[0].eval()

    UpperCAmelCase_										=					True if config.feat_extract_norm == "layer" else False
    UpperCAmelCase_										=					WavaVecaFeatureExtractor(
        feature_size=1	,					sampling_rate=1_6000	,					padding_value=0	,					do_normalize=__UpperCamelCase	,					return_attention_mask=__UpperCamelCase	,					)

    if is_finetuned:
        if dict_path:
            UpperCAmelCase_										=					Dictionary.load(__UpperCamelCase				)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCAmelCase_										=					target_dict.pad_index
            UpperCAmelCase_										=					target_dict.bos_index
            UpperCAmelCase_										=					target_dict.pad_index
            UpperCAmelCase_										=					target_dict.bos_index
            UpperCAmelCase_										=					target_dict.eos_index
            UpperCAmelCase_										=					len(target_dict.symbols				)
            UpperCAmelCase_										=					os.path.join(__UpperCamelCase	,					'''vocab.json'''				)
            if not os.path.isdir(__UpperCamelCase				):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__UpperCamelCase				)				)
                return
            os.makedirs(__UpperCamelCase	,					exist_ok=__UpperCamelCase				)
            with open(__UpperCamelCase	,					'''w'''	,					encoding='''utf-8'''				) as vocab_handle:
                json.dump(target_dict.indices	,					__UpperCamelCase				)
            UpperCAmelCase_										=					WavaVecaCTCTokenizer(
                __UpperCamelCase	,					unk_token=target_dict.unk_word	,					pad_token=target_dict.pad_word	,					bos_token=target_dict.bos_word	,					eos_token=target_dict.eos_word	,					word_delimiter_token='''|'''	,					do_lower_case=__UpperCamelCase	,					)
            UpperCAmelCase_										=					WavaVecaProcessor(feature_extractor=__UpperCamelCase	,					tokenizer=__UpperCamelCase				)
            processor.save_pretrained(__UpperCamelCase				)

        UpperCAmelCase_										=					SEWForCTC(__UpperCamelCase				)
    else:
        UpperCAmelCase_										=					SEWModel(__UpperCamelCase				)
        feature_extractor.save_pretrained(__UpperCamelCase				)

    recursively_load_weights(__UpperCamelCase	,					__UpperCamelCase	,					__UpperCamelCase				)

    hf_model.save_pretrained(__UpperCamelCase				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
 parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
 parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
 parser.add_argument(
     '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
 )
 _lowerCamelCase      			=						parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )











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)





