





import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union

import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForPreTraining,
    is_apex_available,
    trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices


if is_apex_available():
  from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
  UpperCAmelCase_  							=				True
  from torch.cuda.amp import autocast


UpperCAmelCase_  							=				logging.getLogger(__name__)






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							str     	=	field(
       metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}    ,       )
   a		:							Optional[bool]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to freeze the feature extractor layers of the model."}       )
   a		:							Optional[bool]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Whether to log verbose messages or not."}    ,       )
   a		:							Optional[float]     	=	field(
       default=2.0    ,       metadata={"help": "Maximum temperature for gumbel softmax."}       )
   a		:							Optional[float]     	=	field(
       default=0.5    ,       metadata={"help": "Minimum temperature for gumbel softmax."}       )
   a		:							Optional[float]     	=	field(
       default=0.9_9_9_9_9_5    ,       metadata={"help": "Decay of gumbel temperature during training."}       )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    ModelArguments	,     __UpperCAmelCase:    TrainingArguments   )   ->			Union[str, Any]:
    logging.basicConfig(
        format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''	,     datefmt='''%m/%d/%Y %H:%M:%S'''	,     handlers=[logging.StreamHandler(sys.stdout   )]	,     )
    UpperCamelCase__							:       List[Any]      =   logging.WARNING
    if model_args.verbose_logging:
        UpperCamelCase__							:       Optional[int]      =   logging.DEBUG
    elif trainer_utils.is_main_process(training_args.local_rank   ):
        UpperCamelCase__							:       Union[str, Any]      =   logging.INFO
    logger.setLevel(__UpperCAmelCase   )






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							str     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "The name of the dataset to use (via the datasets library)."}       )
   a		:							Optional[str]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}       )
   a		:							Optional[str]     	=	field(
       default="train"    ,       metadata={
           "help": "The name of the training data set split to use (via the datasets library). Defaults to 'train'"
       }    ,       )
   a		:							Optional[str]     	=	field(
       default="validation"    ,       metadata={
           "help": (
               "The name of the validation data set split to use (via the datasets library). Defaults to 'validation'"
           )
       }    ,       )
   a		:							Optional[str]     	=	field(
       default="file"    ,       metadata={"help": "Column in the dataset that contains speech file path. Defaults to 'file'"}    ,       )
   a		:							bool     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "Overwrite the cached preprocessed datasets or not."}       )
   a		:							Optional[int]     	=	field(
       default=1    ,       metadata={
           "help": "The percentage of the train set used as validation set in case there's no validation split"
       }    ,       )
   a		:							Optional[int]     	=	field(
       default=__lowerCamelCase    ,       metadata={"help": "The number of processes to use for the preprocessing."}    ,       )
   a		:							Optional[float]     	=	field(
       default=2_0.0    ,       metadata={"help": "Filter audio files that are longer than `max_duration_in_seconds` seconds"}       )






@dataclass
class 					lowercase__ :

   '''simple docstring'''


   a		:							WavaVecaForPreTraining
   a		:							WavaVecaFeatureExtractor
   a		:							Union[bool, str]     	=	"longest"
   a		:							Optional[int]     	=	None
   a		:							Optional[int]     	=	None




   def __call__(					self,	__magic_name__							)				->   Dict[str, torch.Tensor]:






       """simple docstring"""
       # reformat list to dict and set to pytorch format
       UpperCamelCase__							:       List[str]      =   self.feature_extractor.pad(
           __magic_name__,	max_length=self.max_length,	padding=self.padding,	pad_to_multiple_of=self.pad_to_multiple_of,	return_tensors='''pt''',	)
       UpperCamelCase__							:       Optional[int]      =   self.model._get_feat_extract_output_lengths(batch['''input_values'''].shape[-1]							)

       UpperCamelCase__							:       Optional[int]      =   batch['''input_values'''].shape[0]

       # make sure that no loss is computed on padded inputs
       if batch["attention_mask"] is not None:
           # compute real output lengths according to convolution formula
           UpperCamelCase__							:       List[Any]      =   self.model._get_feat_extract_output_lengths(batch['''attention_mask'''].sum(-1							)							).to(
               torch.long							)

           UpperCamelCase__							:       Tuple      =   torch.zeros(
               (batch_size, mask_indices_seq_length),	dtype=torch.long,	device=batch['''input_values'''].device							)

           # these two operations makes sure that all values
           # before the output lengths indices are attended to
           UpperCamelCase__							:       Dict      =   1
           UpperCamelCase__							:       Tuple      =   attention_mask.flip([-1]							).cumsum(-1							).flip([-1]							).bool()

       # sample randomly masked indices
       UpperCamelCase__							:       Optional[int]      =   _compute_mask_indices(
           (batch_size, mask_indices_seq_length),	self.model.config.mask_time_prob,	self.model.config.mask_time_length,	attention_mask=__magic_name__,	min_masks=2,	)

       return batch






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   def __init__(					self,	*__magic_name__,	__magic_name__=1,	__magic_name__=0,	__magic_name__=1.0,	**__magic_name__							)				->   List[Any]:






       """simple docstring"""
       super().__init__(*__magic_name__,	**__magic_name__							)
       UpperCamelCase__							:       Union[str, Any]      =   0
       UpperCamelCase__							:       List[Any]      =   max_gumbel_temp
       UpperCamelCase__							:       List[Any]      =   min_gumbel_temp
       UpperCamelCase__							:       Any      =   gumbel_temp_decay




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   torch.Tensor:






       """simple docstring"""
       model.train()
       UpperCamelCase__							:       Tuple      =   self._prepare_inputs(__magic_name__							)

       if self.use_amp:
           with autocast():
               UpperCamelCase__							:       List[str]      =   self.compute_loss(__magic_name__,	__magic_name__							)
       else:
           UpperCamelCase__							:       str      =   self.compute_loss(__magic_name__,	__magic_name__							)

       if self.args.n_gpu > 1 or self.deepspeed:
           if model.module.config.ctc_loss_reduction == "mean":
               UpperCamelCase__							:       int      =   loss.mean()
           elif model.module.config.ctc_loss_reduction == "sum":
               UpperCamelCase__							:       Optional[Any]      =   loss.sum() / (inputs['''mask_time_indices''']).sum()
           else:
               raise ValueError(f"{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"							)

       if self.args.gradient_accumulation_steps > 1:
           UpperCamelCase__							:       Tuple      =   loss / self.args.gradient_accumulation_steps

       if self.use_amp:
           self.scaler.scale(__magic_name__							).backward()
       elif self.use_apex:
           with amp.scale_loss(__magic_name__,	self.optimizer							) as scaled_loss:
               scaled_loss.backward()
       elif self.deepspeed:
           self.deepspeed.backward(__magic_name__							)
       else:
           loss.backward()

       self.num_update_step += 1
       # make sure gumbel softmax temperature is decayed
       if self.args.n_gpu > 1 or self.deepspeed:
           model.module.set_gumbel_temperature(
               max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,	self.min_gumbel_temp							)							)
       else:
           model.set_gumbel_temperature(
               max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step,	self.min_gumbel_temp							)							)

       return loss.detach()




def 	lowerCAmelCase_					(						)   ->			Union[str, Any]:
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    UpperCamelCase__							:       Optional[int]      =   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )

    UpperCamelCase__     ,UpperCamelCase__     ,UpperCamelCase__							:       int      =   parser.parse_args_into_dataclasses()
    configure_logger(__UpperCAmelCase	,     __UpperCAmelCase   )

    # Downloading and loading a dataset from the hub.
    UpperCamelCase__							:       Any      =   load_dataset(data_args.dataset_name	,     data_args.dataset_config_name	,     cache_dir=model_args.cache_dir   )

    if "validation" not in datasets.keys():
        # make sure only "validation" and "train" keys remain"
        UpperCamelCase__							:       Any      =   DatasetDict()
        UpperCamelCase__							:       str      =   load_dataset(
            data_args.dataset_name	,     data_args.dataset_config_name	,     split=f"{data_args.train_split_name}[:{data_args.validation_split_percentage}%]"	,     cache_dir=model_args.cache_dir	,     )
        UpperCamelCase__							:       Dict      =   load_dataset(
            data_args.dataset_name	,     data_args.dataset_config_name	,     split=f"{data_args.train_split_name}[{data_args.validation_split_percentage}%:]"	,     cache_dir=model_args.cache_dir	,     )
    else:
        # make sure only "validation" and "train" keys remain"
        UpperCamelCase__							:       Optional[Any]      =   DatasetDict()
        UpperCamelCase__							:       List[Any]      =   load_dataset(
            data_args.dataset_name	,     data_args.dataset_config_name	,     split='''validation'''	,     cache_dir=model_args.cache_dir	,     )
        UpperCamelCase__							:       Optional[Any]      =   load_dataset(
            data_args.dataset_name	,     data_args.dataset_config_name	,     split=f"{data_args.train_split_name}"	,     cache_dir=model_args.cache_dir	,     )

    # only normalized-inputs-training is supported
    UpperCamelCase__							:       Dict      =   WavaVecaFeatureExtractor.from_pretrained(
        model_args.model_name_or_path	,     cache_dir=model_args.cache_dir	,     do_normalize=__UpperCAmelCase   )

    def prepare_dataset(__UpperCAmelCase:    int   ):
        # check that all files have the correct sampling rate
        UpperCamelCase__     ,UpperCamelCase__							:       Any      =   librosa.load(batch[data_args.speech_file_column]	,     sr=feature_extractor.sampling_rate   )
        return batch

    # load audio files into numpy arrays
    UpperCamelCase__							:       Dict      =   datasets.map(
        __UpperCAmelCase	,     num_proc=data_args.preprocessing_num_workers	,     remove_columns=datasets['''train'''].column_names   )

    # filter audio files that are too long
    UpperCamelCase__							:       List[Any]      =   vectorized_datasets.filter(
        lambda __UpperCAmelCase   : len(data['''speech''']   ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate   )   )

    def normalize(__UpperCAmelCase:    Optional[int]   ):
        return feature_extractor(batch['''speech''']	,     sampling_rate=feature_extractor.sampling_rate   )

    # normalize and transform to `BatchFeatures`
    UpperCamelCase__							:       int      =   vectorized_datasets.map(
        __UpperCAmelCase	,     batched=__UpperCAmelCase	,     num_proc=data_args.preprocessing_num_workers	,     load_from_cache_file=not data_args.overwrite_cache	,     remove_columns=vectorized_datasets['''train'''].column_names	,     )

    # pretraining is only supported for "newer" stable layer norm architecture
    # apply_spec_augment has to be True, mask_feature_prob has to be 0.0
    UpperCamelCase__							:       Union[str, Any]      =   WavaVecaConfig.from_pretrained(
        model_args.model_name_or_path	,     cache_dir=model_args.cache_dir	,     gradient_checkpointing=training_args.gradient_checkpointing	,     )

    if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
        raise ValueError(
            '''PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'''
            ''' ``config.feat_extract_norm=\'layer\''''   )

    UpperCamelCase__							:       Union[str, Any]      =   WavaVecaForPreTraining(__UpperCAmelCase   )

    UpperCamelCase__							:       str      =   DataCollatorForWavaVecaPretraining(model=__UpperCAmelCase	,     feature_extractor=__UpperCAmelCase   )

    UpperCamelCase__							:       Dict      =   WavaVecaPreTrainer(
        model=__UpperCAmelCase	,     data_collator=__UpperCAmelCase	,     args=__UpperCAmelCase	,     train_dataset=vectorized_datasets['''train''']	,     eval_dataset=vectorized_datasets['''validation''']	,     tokenizer=__UpperCAmelCase	,     max_gumbel_temp=model_args.max_gumbel_temperature	,     min_gumbel_temp=model_args.min_gumbel_temperature	,     gumbel_temp_decay=model_args.gumbel_temperature_decay	,     )
    trainer.train()


if __name__ == "__main__":
  main()








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()
UpperCAmelCase_  							=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase_  							=				[]
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 	lowerCAmelCase_					(						__UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    Dict   )   ->			Dict:
    UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   val




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict   )   ->			List[str]:
    UpperCamelCase__							:       List[str]      =   OrderedDict()
    for key, value in state_dict.items():
        if "backbone.0.body" in key:
            UpperCamelCase__							:       Optional[Any]      =   key.replace('''backbone.0.body'''	,     '''backbone.conv_encoder.model'''   )
            UpperCamelCase__							:       List[str]      =   value
        else:
            UpperCamelCase__							:       Optional[int]      =   value

    return new_state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Optional[int]=False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   ''''''
    if is_panoptic:
        UpperCamelCase__							:       List[Any]      =   '''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)
        UpperCamelCase__							:       List[Any]      =   state_dict.pop(f"{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"   )
        UpperCamelCase__							:       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
        UpperCamelCase__							:       Any      =   in_proj_weight[:256, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[:256]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[256:512, :]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[256:512]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[-256:, :]
        UpperCamelCase__							:       List[Any]      =   in_proj_bias[-256:]




def 	lowerCAmelCase_					(						)   ->			int:
    UpperCamelCase__							:       Any      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Optional[int]      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )

    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Any   )   ->			Optional[int]:
    UpperCamelCase__							:       Tuple      =   ConditionalDetrConfig()
    # set backbone and dilation attributes
    if "resnet101" in model_name:
        UpperCamelCase__							:       Optional[Any]      =   '''resnet101'''
    if "dc5" in model_name:
        UpperCamelCase__							:       Dict      =   True
    UpperCamelCase__							:       Union[str, Any]      =   '''panoptic''' in model_name
    if is_panoptic:
        UpperCamelCase__							:       Any      =   250
    else:
        UpperCamelCase__							:       int      =   91
        UpperCamelCase__							:       List[str]      =   '''huggingface/label-files'''
        UpperCamelCase__							:       List[str]      =   '''coco-detection-id2label.json'''
        UpperCamelCase__							:       Tuple      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
        UpperCamelCase__							:       List[str]      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
        UpperCamelCase__							:       Dict      =   idalabel
        UpperCamelCase__							:       Dict      =   {v: k for k, v in idalabel.items()}

    # load image processor
    UpperCamelCase__							:       int      =   '''coco_panoptic''' if is_panoptic else '''coco_detection'''
    UpperCamelCase__							:       str      =   ConditionalDetrImageProcessor(format=__UpperCAmelCase   )

    # prepare image
    UpperCamelCase__							:       Tuple      =   prepare_img()
    UpperCamelCase__							:       List[str]      =   image_processor(images=__UpperCAmelCase	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Union[str, Any]      =   encoding['''pixel_values''']

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

    # load original model from torch hub
    UpperCamelCase__							:       Union[str, Any]      =   torch.hub.load('''DeppMeng/ConditionalDETR'''	,     __UpperCAmelCase	,     pretrained=__UpperCAmelCase   ).eval()
    UpperCamelCase__							:       Optional[int]      =   conditional_detr.state_dict()
    # rename keys
    for src, dest in rename_keys:
        if is_panoptic:
            UpperCamelCase__							:       List[str]      =   '''conditional_detr.''' + src
        rename_key(__UpperCAmelCase	,     __UpperCAmelCase	,     __UpperCAmelCase   )
    UpperCamelCase__							:       str      =   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
    UpperCamelCase__							:       List[str]      =   '''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'''   )
            ):
                UpperCamelCase__							:       Tuple      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       List[Any]      =   val
            elif "class_labels_classifier" in key or "bbox_predictor" in key:
                UpperCamelCase__							:       Any      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       Dict      =   val
            elif key.startswith('''bbox_attention'''   ) or key.startswith('''mask_head'''   ):
                continue
            else:
                UpperCamelCase__							:       List[Any]      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       Optional[int]      =   val
        else:
            if not key.startswith('''class_labels_classifier'''   ) and not key.startswith('''bbox_predictor'''   ):
                UpperCamelCase__							:       List[str]      =   state_dict.pop(__UpperCAmelCase   )
                UpperCamelCase__							:       Optional[int]      =   val
    # finally, create HuggingFace model and load state dict
    UpperCamelCase__							:       str      =   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
    UpperCamelCase__							:       str      =   conditional_detr(__UpperCAmelCase   )
    UpperCamelCase__							:       Optional[Any]      =   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__":
  UpperCAmelCase_  							=				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.'
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)

