



import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case		:List[str]   	=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__snake_case		:Union[str, Any]   	=  []
for i in range(6):
 # 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}.cross_attn.out_proj.weight',
         f'decoder.layers.{i}.encoder_attn.out_proj.weight',
     )
 )
 rename_keys.append(
     (
         f'transformer.decoder.layers.{i}.cross_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'))

 # q, k, v projections in self/cross-attention in decoder for conditional DETR
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.sa_qcontent_proj.weight', f'decoder.layers.{i}.sa_qcontent_proj.weight')
 )
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.sa_kcontent_proj.weight', f'decoder.layers.{i}.sa_kcontent_proj.weight')
 )
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.sa_qpos_proj.weight', f'decoder.layers.{i}.sa_qpos_proj.weight')
 )
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.sa_kpos_proj.weight', f'decoder.layers.{i}.sa_kpos_proj.weight')
 )
 rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.weight', f'decoder.layers.{i}.sa_v_proj.weight'))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.ca_qcontent_proj.weight', f'decoder.layers.{i}.ca_qcontent_proj.weight')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.ca_kcontent_proj.weight', f'decoder.layers.{i}.ca_kcontent_proj.weight')
 )
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.ca_kpos_proj.weight', f'decoder.layers.{i}.ca_kpos_proj.weight')
 )
 rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.weight', f'decoder.layers.{i}.ca_v_proj.weight'))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight', f'decoder.layers.{i}.ca_qpos_sine_proj.weight')
 )

 rename_keys.append(
     (f'transformer.decoder.layers.{i}.sa_qcontent_proj.bias', f'decoder.layers.{i}.sa_qcontent_proj.bias')
 )
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.sa_kcontent_proj.bias', f'decoder.layers.{i}.sa_kcontent_proj.bias')
 )
 rename_keys.append((f'transformer.decoder.layers.{i}.sa_qpos_proj.bias', f'decoder.layers.{i}.sa_qpos_proj.bias'))
 rename_keys.append((f'transformer.decoder.layers.{i}.sa_kpos_proj.bias', f'decoder.layers.{i}.sa_kpos_proj.bias'))
 rename_keys.append((f'transformer.decoder.layers.{i}.sa_v_proj.bias', f'decoder.layers.{i}.sa_v_proj.bias'))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.ca_qcontent_proj.bias', f'decoder.layers.{i}.ca_qcontent_proj.bias')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.ca_kcontent_proj.bias', f'decoder.layers.{i}.ca_kcontent_proj.bias')
 )
 rename_keys.append((f'transformer.decoder.layers.{i}.ca_kpos_proj.bias', f'decoder.layers.{i}.ca_kpos_proj.bias'))
 rename_keys.append((f'transformer.decoder.layers.{i}.ca_v_proj.bias', f'decoder.layers.{i}.ca_v_proj.bias'))
 rename_keys.append(
     (f'transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias', f'decoder.layers.{i}.ca_qpos_sine_proj.bias')
 )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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'''),
        ('''transformer.decoder.ref_point_head.layers.0.weight''', '''decoder.ref_point_head.layers.0.weight'''),
        ('''transformer.decoder.ref_point_head.layers.0.bias''', '''decoder.ref_point_head.layers.0.bias'''),
        ('''transformer.decoder.ref_point_head.layers.1.weight''', '''decoder.ref_point_head.layers.1.weight'''),
        ('''transformer.decoder.ref_point_head.layers.1.bias''', '''decoder.ref_point_head.layers.1.bias'''),
        ('''transformer.decoder.query_scale.layers.0.weight''', '''decoder.query_scale.layers.0.weight'''),
        ('''transformer.decoder.query_scale.layers.0.bias''', '''decoder.query_scale.layers.0.bias'''),
        ('''transformer.decoder.query_scale.layers.1.weight''', '''decoder.query_scale.layers.1.weight'''),
        ('''transformer.decoder.query_scale.layers.1.bias''', '''decoder.query_scale.layers.1.bias'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.weight''', '''decoder.layers.0.ca_qpos_proj.weight'''),
        ('''transformer.decoder.layers.0.ca_qpos_proj.bias''', '''decoder.layers.0.ca_qpos_proj.bias'''),
    ]
)





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       state_dict.pop(_UpperCAmelCase       )
    __a		 =       val





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            __a		 =       key.replace('''backbone.0.body'''     ,				'''backbone.conv_encoder.model'''       )
            __a		 =       value
        else:
            __a		 =       value

    return new_state_dict





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase=False       ):
    __a		 =       ''''''
    if is_panoptic:
        __a		 =       '''conditional_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)
        __a		 =       state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'       )
        __a		 =       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
        __a		 =       in_proj_weight[:256, :]
        __a		 =       in_proj_bias[:256]
        __a		 =       in_proj_weight[256:512, :]
        __a		 =       in_proj_bias[256:512]
        __a		 =       in_proj_weight[-256:, :]
        __a		 =       in_proj_bias[-256:]





def   __snake_case			(    ):
    __a		 =       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a		 =       Image.open(requests.get(_UpperCAmelCase     ,				stream=_UpperCAmelCase       ).raw       )

    return im





@torch.no_grad()
def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        __a		 =       '''resnet101'''
    if "dc5" in model_name:
        __a		 =       True
    __a		 =       '''panoptic''' in model_name
    if is_panoptic:
        __a		 =       250
    else:
        __a		 =       91
        __a		 =       '''huggingface/label-files'''
        __a		 =       '''coco-detection-id2label.json'''
        __a		 =       json.load(open(hf_hub_download(_UpperCAmelCase     ,				_UpperCAmelCase     ,				repo_type='''dataset'''       )     ,				'''r'''       )       )
        __a		 =       {int(_UpperCAmelCase       ): v for k, v in idalabel.items()}
        __a		 =       idalabel
        __a		 =       {v: k for k, v in idalabel.items()}

    # load image processor
    __a		 =       '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    __a		 =       ConditionalDetrImageProcessor(format=_UpperCAmelCase       )

    # prepare image
    __a		 =       prepare_img()
    __a		 =       image_processor(images=_UpperCAmelCase     ,				return_tensors='''pt'''       )
    __a		 =       encoding['''pixel_values''']

    logger.info(f'Converting model {model_name}...'       )

    # load original model from torch hub
    __a		 =       torch.hub.load('''DeppMeng/ConditionalDETR'''     ,				_UpperCAmelCase     ,				pretrained=_UpperCAmelCase       ).eval()
    __a		 =       conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            __a		 =       '''conditional_detr.''' + src
        rename_key(_UpperCAmelCase     ,				_UpperCAmelCase     ,				_UpperCAmelCase       )
    __a		 =       rename_backbone_keys(_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
    __a		 =       '''conditional_detr.model.''' if is_panoptic else '''model.'''
    for key in state_dict.copy().keys():
        if is_panoptic:
            if (
                key.startswith('''conditional_detr'''       )
                and not key.startswith('''class_labels_classifier'''       )
                and not key.startswith('''bbox_predictor'''       )
            ):
                __a		 =       state_dict.pop(_UpperCAmelCase       )
                __a		 =       val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                __a		 =       state_dict.pop(_UpperCAmelCase       )
                __a		 =       val
            elif key.startswith('''bbox_attention'''       ) or key.startswith('''mask_head'''       ):
                continue
            else:
                __a		 =       state_dict.pop(_UpperCAmelCase       )
                __a		 =       val
        else:
            if not key.startswith('''class_labels_classifier'''       ) and not key.startswith('''bbox_predictor'''       ):
                __a		 =       state_dict.pop(_UpperCAmelCase       )
                __a		 =       val
    # finally, create HuggingFace model and load state dict
    __a		 =       ConditionalDetrForSegmentation(_UpperCAmelCase       ) if is_panoptic else ConditionalDetrForObjectDetection(_UpperCAmelCase       )
    model.load_state_dict(_UpperCAmelCase       )
    model.eval()
    model.push_to_hub(repo_id=_UpperCAmelCase     ,				organization='''DepuMeng'''     ,				commit_message='''Add model'''       )
    # verify our conversion
    __a		 =       conditional_detr(_UpperCAmelCase       )
    __a		 =       model(_UpperCAmelCase       )
    assert torch.allclose(outputs.logits     ,				original_outputs['''pred_logits''']     ,				atol=1E-4       )
    assert torch.allclose(outputs.pred_boxes     ,				original_outputs['''pred_boxes''']     ,				atol=1E-4       )
    if is_panoptic:
        assert torch.allclose(outputs.pred_masks     ,				original_outputs['''pred_masks''']     ,				atol=1E-4       )

    # 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       )
    image_processor.save_pretrained(_UpperCAmelCase       )


if __name__ == "__main__":
 __snake_case		:Tuple   	=  argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''conditional_detr_resnet50''',
     type=str,
     help='''Name of the CONDITIONAL_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.'''
 )
 __snake_case		:List[Any]   	=  parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)










from typing import List

import jiwer
import jiwer.transforms as tr
from packaging import version

import datasets
from datasets.config import PY_VERSION


if PY_VERSION < version.parse('''3.8'''):
 import importlib_metadata
else:
 import importlib.metadata as importlib_metadata


__snake_case		:int   	=  ''''''


if version.parse(importlib_metadata.version('''jiwer''')) < version.parse('''2.3.0'''):

 class 					_A (			tr.AbstractTransform					):



        def __init__(	self							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  str = " "):


            '''simple docstring'''

            __a		 =       sentence_delimiter



        def 	_lowerCamelCase    (	self							:  Dict  ,	__SCREAMING_SNAKE_CASE							:  str):


            '''simple docstring'''

            return list(__SCREAMING_SNAKE_CASE)





        def 	_lowerCamelCase    (	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  List[str]):


            '''simple docstring'''

            __a		 =       []
            for sent_idx, sentence in enumerate(__SCREAMING_SNAKE_CASE):
                chars.extend(self.process_string(__SCREAMING_SNAKE_CASE))
                if self.sentence_delimiter is not None and self.sentence_delimiter != "" and sent_idx < len(__SCREAMING_SNAKE_CASE) - 1:
                    chars.append(self.sentence_delimiter)
            return chars


 __snake_case		:Any   	=  tr.Compose(
     [tr.RemoveMultipleSpaces(), tr.Strip(), SentencesToListOfCharacters(SENTENCE_DELIMITER)]
 )
else:
 __snake_case		:Optional[int]   	=  tr.Compose(
     [
         tr.RemoveMultipleSpaces(),
         tr.Strip(),
         tr.ReduceToSingleSentence(SENTENCE_DELIMITER),
         tr.ReduceToListOfListOfChars(),
     ]
 )


__snake_case		:Optional[int]   	=  '''\
@inproceedings{inproceedings,
    author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
    year = {2004},
    month = {01},
    pages = {},
    title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}
}
'''

__snake_case		:Tuple   	=  '''\
Character error rate (CER) is a common metric of the performance of an automatic speech recognition system.

CER is similar to Word Error Rate (WER), but operates on character instead of word. Please refer to docs of WER for further information.

Character error rate can be computed as:

CER = (S + D + I) / N = (S + D + I) / (S + D + C)

where

S is the number of substitutions,
D is the number of deletions,
I is the number of insertions,
C is the number of correct characters,
N is the number of characters in the reference (N=S+D+C).

CER\'s output is not always a number between 0 and 1, in particular when there is a high number of insertions. This value is often associated to the percentage of characters that were incorrectly predicted. The lower the value, the better the
performance of the ASR system with a CER of 0 being a perfect score.
'''

__snake_case		:Tuple   	=  '''
Computes CER score of transcribed segments against references.
Args:
    references: list of references for each speech input.
    predictions: list of transcribtions to score.
    concatenate_texts: Whether or not to concatenate sentences before evaluation, set to True for more accurate result.
Returns:
    (float): the character error rate

Examples:

    >>> predictions = ["this is the prediction", "there is an other sample"]
    >>> references = ["this is the reference", "there is another one"]
    >>> cer = datasets.load_metric("cer")
    >>> cer_score = cer.compute(predictions=predictions, references=references)
    >>> print(cer_score)
    0.34146341463414637
'''

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,_KWARGS_DESCRIPTION					)
class 					_A (			datasets.Metric					):



       def 	_lowerCamelCase    (	self							:  Optional[Any]):


           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
                   {
                       '''predictions''': datasets.Value('''string'''  ,	id='''sequence'''),
                       '''references''': datasets.Value('''string'''  ,	id='''sequence'''),
                   })  ,	codebase_urls=['''https://github.com/jitsi/jiwer/''']  ,	reference_urls=[
                   '''https://en.wikipedia.org/wiki/Word_error_rate''',
                   '''https://sites.google.com/site/textdigitisation/qualitymeasures/computingerrorrates''',
               ]  ,	)





       def 	_lowerCamelCase    (	self							:  Optional[int]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  Dict=False):


           '''simple docstring'''

           if concatenate_texts:
               return jiwer.compute_measures(
                   __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	truth_transform=__SCREAMING_SNAKE_CASE  ,	hypothesis_transform=__SCREAMING_SNAKE_CASE  ,	)["wer"]

           __a		 =       0
           __a		 =       0
           for prediction, reference in zip(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               __a		 =       jiwer.compute_measures(
                   __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	truth_transform=__SCREAMING_SNAKE_CASE  ,	hypothesis_transform=__SCREAMING_SNAKE_CASE  ,	)
               incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
               total += measures["substitutions"] + measures["deletions"] + measures["hits"]

           return incorrect / total





