
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    :	int						=						logging.get_logger(__name__)


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Tuple:
    # initialize config
    if "resnet-50" in model_name:
        __lowercase						:	Optional[int]					=				ResNetConfig.from_pretrained('''microsoft/resnet-50'''		)
    elif "resnet-101" in model_name:
        __lowercase						:	Optional[Any]					=				ResNetConfig.from_pretrained('''microsoft/resnet-101'''		)
    else:
        raise ValueError('''Model name should include either resnet50 or resnet101'''		)

    __lowercase						:	Tuple					=				DetrConfig(use_timm_backbone=__lowerCAmelCase		,    backbone_config=__lowerCAmelCase		)

    # set label attributes
    __lowercase						:	Tuple					=				"panoptic" in model_name
    if is_panoptic:
        __lowercase						:	Any					=				250
    else:
        __lowercase						:	Dict					=				91
        __lowercase						:	Optional[int]					=				"huggingface/label-files"
        __lowercase						:	Any					=				"coco-detection-id2label.json"
        __lowercase						:	int					=				json.load(open(hf_hub_download(__lowerCAmelCase		,    __lowerCAmelCase		,    repo_type='''dataset'''		)		,    '''r'''		)		)
        __lowercase						:	int					=				{int(__lowerCAmelCase		): v for k, v in idalabel.items()}
        __lowercase						:	Any					=				idalabel
        __lowercase						:	Optional[Any]					=				{v: k for k, v in idalabel.items()}

    return config, is_panoptic


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

    # 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 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		,    __lowerCAmelCase		)       ->  int:
    __lowercase						:	List[str]					=				state_dict.pop(__lowerCAmelCase		)
    __lowercase						:	List[str]					=				val


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase=False		)       ->  str:
    __lowercase						:	str					=				""
    if is_panoptic:
        __lowercase						:	Dict					=				"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)
        __lowercase						:	Dict					=				state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'		)
        __lowercase						:	int					=				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
        __lowercase						:	Union[str, Any]					=				in_proj_weight[:256, :]
        __lowercase						:	Any					=				in_proj_bias[:256]
        __lowercase						:	Tuple					=				in_proj_weight[256:512, :]
        __lowercase						:	Union[str, Any]					=				in_proj_bias[256:512]
        __lowercase						:	Optional[int]					=				in_proj_weight[-256:, :]
        __lowercase						:	Union[str, Any]					=				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
        __lowercase						:	str					=				state_dict.pop(F'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'		)
        __lowercase						:	int					=				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
        __lowercase						:	List[str]					=				in_proj_weight[:256, :]
        __lowercase						:	Dict					=				in_proj_bias[:256]
        __lowercase						:	Optional[Any]					=				in_proj_weight[256:512, :]
        __lowercase						:	List[str]					=				in_proj_bias[256:512]
        __lowercase						:	Dict					=				in_proj_weight[-256:, :]
        __lowercase						:	Any					=				in_proj_bias[-256:]
        # read in weights + bias of input projection layer of cross-attention
        __lowercase						:	int					=				state_dict.pop(
            F'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'		)
        __lowercase						:	int					=				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
        __lowercase						:	List[str]					=				in_proj_weight_cross_attn[:256, :]
        __lowercase						:	str					=				in_proj_bias_cross_attn[:256]
        __lowercase						:	Tuple					=				in_proj_weight_cross_attn[256:512, :]
        __lowercase						:	int					=				in_proj_bias_cross_attn[256:512]
        __lowercase						:	Optional[Any]					=				in_proj_weight_cross_attn[-256:, :]
        __lowercase						:	Tuple					=				in_proj_bias_cross_attn[-256:]


def 			UpperCAmelCase_     (  )       ->  str:
    __lowercase						:	Tuple					=				"http://images.cocodataset.org/val2017/000000039769.jpg"
    __lowercase						:	List[Any]					=				Image.open(requests.get(__lowerCAmelCase		,    stream=__lowerCAmelCase		).raw		)

    return im


@torch.no_grad()
def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase=None		,    __lowerCAmelCase=False		)       ->  List[str]:
    __lowercase						:	Tuple					=				get_detr_config(__lowerCAmelCase		)

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

    # finally, create HuggingFace model and load state dict
    __lowercase						:	Optional[int]					=				DetrForSegmentation(__lowerCAmelCase		) if is_panoptic else DetrForObjectDetection(__lowerCAmelCase		)
    model.load_state_dict(__lowerCAmelCase		)
    model.eval()

    # verify our conversion on an image
    __lowercase						:	Optional[int]					=				"coco_panoptic" if is_panoptic else "coco_detection"
    __lowercase						:	Dict					=				DetrImageProcessor(format=__lowerCAmelCase		)

    __lowercase						:	Optional[Any]					=				processor(images=prepare_img()		,    return_tensors='''pt'''		)
    __lowercase						:	Union[str, Any]					=				encoding["pixel_values"]

    __lowercase						:	str					=				detr(__lowerCAmelCase		)
    __lowercase						:	Tuple					=				model(__lowerCAmelCase		)

    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(__lowerCAmelCase		).mkdir(exist_ok=__lowerCAmelCase		)
        model.save_pretrained(__lowerCAmelCase		)
        processor.save_pretrained(__lowerCAmelCase		)

    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    :	str						=						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    :	int						=						parser.parse_args()
 convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

lowerCamelCase	:							Dict				={
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = '''speech_to_text_2'''
 _lowerCAmelCase       :       Dict        = ['''past_key_values''']
 _lowerCAmelCase       :       Any        = {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=1_00_00							,   SCREAMING_SNAKE_CASE					:   List[Any]=6							,   SCREAMING_SNAKE_CASE					:   List[Any]=20_48							,   SCREAMING_SNAKE_CASE					:   Dict=4							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   List[Any]="relu"							,   SCREAMING_SNAKE_CASE					:   Tuple=2_56							,   SCREAMING_SNAKE_CASE					:   List[str]=0.1							,   SCREAMING_SNAKE_CASE					:   Tuple=0.0							,   SCREAMING_SNAKE_CASE					:   Any=0.0							,   SCREAMING_SNAKE_CASE					:   int=0.0_2							,   SCREAMING_SNAKE_CASE					:   Dict=2							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   Any=1							,   SCREAMING_SNAKE_CASE					:   List[Any]=0							,   SCREAMING_SNAKE_CASE					:   Any=2							,   SCREAMING_SNAKE_CASE					:   str=10_24							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  vocab_size
      UpperCamelCase__			:   Optional[Any]       				=  d_model
      UpperCamelCase__			:   Optional[Any]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Any       				=  decoder_attention_heads
      UpperCamelCase__			:   List[str]       				=  dropout
      UpperCamelCase__			:   int       				=  attention_dropout
      UpperCamelCase__			:   Optional[int]       				=  activation_dropout
      UpperCamelCase__			:   Union[str, Any]       				=  activation_function
      UpperCamelCase__			:   Tuple       				=  init_std
      UpperCamelCase__			:   Optional[int]       				=  decoder_layerdrop
      UpperCamelCase__			:   Dict       				=  use_cache
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Union[str, Any]       				=  scale_embedding  # scale factor will be sqrt(d_model) if True
      UpperCamelCase__			:   Optional[Any]       				=  max_target_positions

      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE							,   bos_token_id=SCREAMING_SNAKE_CASE							,   eos_token_id=SCREAMING_SNAKE_CASE							,   decoder_start_token_id=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   )