"""simple docstring"""







import argparse
import gc
import os
import sys
from pathlib import Path
from typing import List  # noqa: F401

import pytorch_lightning as pl
import torch
from finetune import SummarizationModule, TranslationModule
from finetune import main as ft_main
from make_student import create_student_by_copying_alternating_layers, get_layers_to_supervise
from torch import nn

from transformers import AutoModelForSeqaSeqLM, MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import calculate_bleu, check_output_dir, freeze_params, label_smoothed_nll_loss, use_task_specific_params


# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import generic_train  # noqa






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Optional[Any]    =    ["loss", "ce_loss", "mlm_loss", "hid_loss_enc", "hid_loss_dec"]







   def __init__(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      assert Path(hparams.data_dir	).exists()
      __lowerCamelCase    		=					Path(hparams.output_dir	)
      self.output_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					self.output_dir.joinpath('student'	)

      __lowerCamelCase    		=					str(_SCREAMING_SNAKE_CASE	)  # Tell lightning we are training the student
      __lowerCamelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(hparams.teacher	).eval()
      use_task_specific_params(_SCREAMING_SNAKE_CASE ,				hparams.task	)  # We copy good generation parameters to student by default
      if hparams.student is not None:
         __lowerCamelCase    		=					AutoModelForSeqaSeqLM.from_pretrained(hparams.student	)
         use_task_specific_params(_SCREAMING_SNAKE_CASE ,				hparams.task	)
         __lowerCamelCase		,__lowerCamelCase    		=					None, None
      else:
         __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					create_student_by_copying_alternating_layers(
             _SCREAMING_SNAKE_CASE ,				e=hparams.student_encoder_layers ,				d=hparams.student_decoder_layers ,				save_path=_SCREAMING_SNAKE_CASE	)

      if hparams.length_penalty != -1:
         __lowerCamelCase    		=					hparams.length_penalty
      __lowerCamelCase    		=					hparams.teacher  # Use teacher's tokenizer
      super().__init__(_SCREAMING_SNAKE_CASE ,				model=_SCREAMING_SNAKE_CASE ,				config=student.config	)
      assert student.config.model_type == teacher.config.model_type, (
          F'''teacher, student model types should be the same, got {student.config.model_type} !='''
          F''' {teacher.config.model_type}'''
      )

      if student.config.model_type == "t5":
         __lowerCamelCase    		=					len(student.get_encoder().block	)
         __lowerCamelCase    		=					len(student.get_decoder().block	)
         __lowerCamelCase    		=					len(teacher.get_encoder().block	)
         __lowerCamelCase    		=					len(teacher.get_decoder().block	)
      else:
         __lowerCamelCase    		=					student.config.encoder_layers
         __lowerCamelCase    		=					student.config.decoder_layers
         __lowerCamelCase    		=					teacher.config.encoder_layers
         __lowerCamelCase    		=					teacher.config.decoder_layers

      __lowerCamelCase    		=					not (hparams.student is None or hparams.teacher == hparams.student)
      __lowerCamelCase    		=					(not self.different_base_models) and hparams.alpha_hid > 0
      __lowerCamelCase    		=					self.different_base_models or (student_encoder_layers != teacher_encoder_layers)
      # self.different_encoder determines whether we need to run the teacher encoder
      __lowerCamelCase    		=					teacher
      freeze_params(self.teacher	)

      if not self.different_encoder:  # To save RAM, delete teacher encoder and freeze student encoder.
         try:
            del self.teacher.model.encoder
         except AttributeError:  # T5
            del self.teacher.encoder

      if e_layer_ids is None:
         __lowerCamelCase    		=					list(range(_SCREAMING_SNAKE_CASE	)	)
      if d_layer_ids is None:
         __lowerCamelCase    		=					list(range(_SCREAMING_SNAKE_CASE	)	)

      __lowerCamelCase		,__lowerCamelCase    		=					e_layer_ids, d_layer_ids  # type: List[int], List[int]

      if self.do_calc_hidden_loss:  # Intermediate supervision: Decide which layers to supervise
         if hparams.supervise_forward:
            __lowerCamelCase    		=					get_layers_to_supervise(
                n_student=len(self.e_layer_ids	) ,				n_teacher=_SCREAMING_SNAKE_CASE	)
            __lowerCamelCase    		=					get_layers_to_supervise(
                n_student=len(self.d_layer_ids	) ,				n_teacher=_SCREAMING_SNAKE_CASE	)
         else:  # student layer should emulate hidden states of the teacher layer it was copied from
            __lowerCamelCase    		=					self.e_layer_ids
            __lowerCamelCase    		=					self.d_layer_ids
      else:
         __lowerCamelCase    		=					None
         __lowerCamelCase    		=					None

      __lowerCamelCase    		=					nn.KLDivLoss(reduction='batchmean'	)
      __lowerCamelCase    		=					2.0
      __lowerCamelCase    		=					hparams.alpha_mlm
      __lowerCamelCase    		=					hparams.alpha_ce
      __lowerCamelCase    		=					hparams.alpha_hid
      gc.collect()
      torch.cuda.empty_cache()







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					mask[:, :, None].expand_as(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					s_logits.size(-1	)
      __lowerCamelCase    		=					torch.masked_select(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					torch.masked_select(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length * voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					s_logits_slct.view(-1 ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      __lowerCamelCase    		=					t_logits_slct.view(-1 ,				_SCREAMING_SNAKE_CASE	)  # (bs * seq_length, voc_size) modulo the 1s in mask
      assert t_logits_slct.size() == s_logits_slct.size()
      __lowerCamelCase    		=					(
          self.ce_loss_fct(
              nn.functional.log_softmax(s_logits_slct / self.temperature ,				dim=-1	) ,				nn.functional.softmax(t_logits_slct / self.temperature ,				dim=-1	) ,				)
          * (self.temperature) ** 2
      )
      return loss_ce







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      SummarizationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      add_distill_args(_SCREAMING_SNAKE_CASE	)
      return parser







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					self.tokenizer.pad_token_id
      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase    		=					batch['input_ids'], batch['attention_mask'], batch['labels']
      if isinstance(self.model ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.model._shift_right(_SCREAMING_SNAKE_CASE	)
      else:
         __lowerCamelCase    		=					shift_tokens_right(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)

      # noinspection PyCallingNonCallable
      __lowerCamelCase    		=					self(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				output_attentions=_SCREAMING_SNAKE_CASE ,				use_cache=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					student_outputs['logits']

      # Same cross entropy vs. label smoothing logic as finetune.py
      assert lm_logits.shape[-1] == self.model.config.vocab_size
      if self.hparams.label_smoothing == 0:
         # Same behavior as modeling_bart.py, besides ignoring pad_token_id
         __lowerCamelCase    		=					nn.CrossEntropyLoss(ignore_index=_SCREAMING_SNAKE_CASE	)
         __lowerCamelCase    		=					loss_fct(lm_logits.view(-1 ,				lm_logits.shape[-1]	) ,				labels.view(-1	)	)
      else:
         __lowerCamelCase    		=					nn.functional.log_softmax(_SCREAMING_SNAKE_CASE ,				dim=-1	)
         __lowerCamelCase		,__lowerCamelCase    		=					label_smoothed_nll_loss(
             _SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				self.hparams.label_smoothing ,				ignore_index=_SCREAMING_SNAKE_CASE	)

      def zero_tensor():
         return torch.tensor(0.0	).type_as(_SCREAMING_SNAKE_CASE	)

      __lowerCamelCase    		=					student_outputs[
          'encoder_last_hidden_state'
      ]  # use this unless self.different_base_models
      __lowerCamelCase		,__lowerCamelCase    		=					zero_tensor(), zero_tensor()
      if self.different_encoder:  # compute encoder hidden state loss
         __lowerCamelCase    		=					self.teacher.get_encoder()(
             _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				)
         if self.different_base_models:
            __lowerCamelCase    		=					all_teacher_encoder_outputs['last_hidden_state']
         elif self.do_calc_hidden_loss:
            __lowerCamelCase    		=					self.calc_hidden_loss(
                _SCREAMING_SNAKE_CASE ,				student_outputs['encoder_hidden_states'] ,				all_teacher_encoder_outputs['hidden_states'] ,				self.e_matches ,				normalize_hidden=self.hparams.normalize_hidden ,				)

      __lowerCamelCase    		=					self.teacher(
          _SCREAMING_SNAKE_CASE ,				attention_mask=_SCREAMING_SNAKE_CASE ,				encoder_outputs=(teacher_enc_outputs,) ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ,				output_hidden_states=self.do_calc_hidden_loss ,				use_cache=_SCREAMING_SNAKE_CASE ,				)
      __lowerCamelCase    		=					decoder_input_ids.ne(_SCREAMING_SNAKE_CASE	)
      __lowerCamelCase    		=					self.calc_ce_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				teacher_outputs['logits']	)
      if self.do_calc_hidden_loss:  # Intermediate supervision of decoder hidden states
         __lowerCamelCase    		=					self.calc_hidden_loss(
             _SCREAMING_SNAKE_CASE ,				student_outputs['decoder_hidden_states'] ,				teacher_outputs['decoder_hidden_states'] ,				self.d_matches ,				normalize_hidden=self.hparams.normalize_hidden ,				)

      __lowerCamelCase    		=					(
          self.alpha_ce * loss_ce
          + self.alpha_mlm * student_lm_loss
          + self.hparams.alpha_hid * (hid_loss_enc + hid_loss_dec)
      )
      return blended_loss, loss_ce, student_lm_loss, hid_loss_enc, hid_loss_dec







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      __lowerCamelCase    		=					'expected list or tuple for hidden_states, got tensor of shape: '
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	), F'''{msg}{hidden_states.shape}'''
      assert not isinstance(_SCREAMING_SNAKE_CASE ,				torch.Tensor	), F'''{msg}{hidden_states_T.shape}'''
      __lowerCamelCase    		=					attention_mask.to(hidden_states[0]	)
      __lowerCamelCase    		=					mask.sum() * hidden_states[0].size(-1	)
      __lowerCamelCase    		=					torch.stack([hidden_states[i] for i in range(len(_SCREAMING_SNAKE_CASE	)	)]	)
      __lowerCamelCase    		=					torch.stack([hidden_states_T[j] for j in matches]	)
      assert student_states.shape == teacher_states.shape, F'''{student_states.shape} != {teacher_states.shape}'''
      if normalize_hidden:
         __lowerCamelCase    		=					nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,				student_states.shape[1:]	)
         __lowerCamelCase    		=					nn.functional.layer_norm(_SCREAMING_SNAKE_CASE ,				teacher_states.shape[1:]	)
      __lowerCamelCase    		=					nn.functional.mse_loss(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				reduction='none'	)
      __lowerCamelCase    		=					(mse * mask.unsqueeze(0	).unsqueeze(-1	)).sum() / valid_count
      return masked_mse



def       A__    (	lowercase_     :	List[Any]						)  ->							List[Any]:
   # NOTE: if --student argument was specified and the teacher and student base models
   # are different, the models still have to have the same tokenizer, specified by
   # --tokenizer_name. So, for example, you can distill from t5_large to t5_small but not
   # from bart to t5. This s because if the tokenizers are different, the output space
   # for the two models is also different and their logits are not comparable.
   parser.add_argument('--teacher'      , type=lowercase_						)
   parser.add_argument('--alpha_ce'      , default=0.8      , type=lowercase_						)
   parser.add_argument('--alpha_mlm'      , default=0.2      , type=lowercase_						)
   parser.add_argument('--alpha_hid'      , default=0.0      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student'      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_decoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--student_encoder_layers'      , default=12      , type=lowercase_      , required=lowercase_						)
   parser.add_argument('--no_teacher'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--length_penalty'      , type=lowercase_      , default=-1						)
   parser.add_argument('--supervise_forward'      , action='store_true'      , default=lowercase_						)
   parser.add_argument('--normalize_hidden'      , action='store_true'      , default=lowercase_						)






class      _SCREAMING_SNAKE_CASE							(			UpperCAmelCase				):



   '''simple docstring'''



   __UpperCAmelCase	:							Any    =    "translation"
   __UpperCAmelCase	:							List[Any]    =    ["bleu"]
   __UpperCAmelCase	:							Dict    =    "bleu"







   def __init__(	self ,				_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      super().__init__(_SCREAMING_SNAKE_CASE ,				**_SCREAMING_SNAKE_CASE	)
      assert hparams.src_lang is not None
      assert hparams.tgt_lang is not None
      __lowerCamelCase    		=					hparams.src_lang
      __lowerCamelCase    		=					hparams.tgt_lang
      if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,				_SCREAMING_SNAKE_CASE	):
         __lowerCamelCase    		=					self.tokenizer.lang_code_to_id[hparams.tgt_lang]







   def    _A				(	self ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      return calculate_bleu(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)







   @staticmethod
   def    _A				(	_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	):


      '''simple docstring'''

      TranslationModule.add_model_specific_args(_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE	)
      add_distill_args(_SCREAMING_SNAKE_CASE	)
      return parser



def       A__    (	lowercase_     :	str						)  ->							Any:
   if args.no_teacher:
      __lowerCamelCase    		=					TranslationModule if 'translation' in args.task else SummarizationModule
   else:  # DISTILL WITH TEACHER
      __lowerCamelCase    		=					TranslationDistiller if 'translation' in args.task else SummarizationDistiller
   __lowerCamelCase    		=					module_cls.__name__
   print(F'''using module {args.setup_cls}'''						)
   __lowerCamelCase    		=					module_cls(lowercase_						)
   return model



def       A__    (	lowercase_     :	Dict						)  ->							List[Any]:
   Path(args.output_dir						).mkdir(exist_ok=lowercase_						)
   check_output_dir(lowercase_      , expected_items=3						)

   __lowerCamelCase    		=					create_module(lowercase_						)
   return ft_main(lowercase_      , model=lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Any   =   argparse.ArgumentParser()
     _SCREAMING_SNAKE_CASE	:		List[Any]   =   pl.Trainer.add_argparse_args(parser)
     _SCREAMING_SNAKE_CASE	:		Union[str, Any]   =   SummarizationDistiller.add_model_specific_args(parser, os.getcwd())
     _SCREAMING_SNAKE_CASE	:		Any   =   parser.parse_args()

     distill_main(args)











"""simple docstring"""







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


logging.set_verbosity_info()
lowercase__						:							Dict        =      logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
lowercase__						:							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}.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"""),
    ]
)

def 							UpperCamelCase_       (       lowerCAmelCase__ :							str       ,  lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							Optional[Any]		)		->  List[Any]:

  """simple docstring"""




  lowerCAmelCase_  :							Dict								=					state_dict.pop(lowerCAmelCase__		)
  lowerCAmelCase_  :							Union[str, Any]								=					val

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Union[str, Any]		)		->  Any:

  """simple docstring"""




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

  return new_state_dict

def 							UpperCamelCase_       (       lowerCAmelCase__ :							Dict       ,  lowerCAmelCase__ :							List[Any]=False		)		->  Optional[int]:

  """simple docstring"""




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

def 							UpperCamelCase_       (       )		->  Any:

  """simple docstring"""




  lowerCAmelCase_  :							Optional[Any]								=					'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowerCAmelCase_  :							List[str]								=					Image.open(requests.get(lowerCAmelCase__       ,  stream=lowerCAmelCase__		).raw		)

  return im

@torch.no_grad()
def 							UpperCamelCase_       (       lowerCAmelCase__ :							Any       ,  lowerCAmelCase__ :							Tuple		)		->  str:

  """simple docstring"""




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

  # load image processor
  lowerCAmelCase_  :							Optional[int]								=					'coco_panoptic' if is_panoptic else 'coco_detection'
  lowerCAmelCase_  :							List[Any]								=					DetrImageProcessor(format=lowerCAmelCase__		)

  # prepare image
  lowerCAmelCase_  :							str								=					prepare_img()
  lowerCAmelCase_  :							Tuple								=					image_processor(images=lowerCAmelCase__       ,  return_tensors='pt'		)
  lowerCAmelCase_  :							int								=					encoding['pixel_values']

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

  # load original model from torch hub
  lowerCAmelCase_  :							int								=					torch.hub.load('facebookresearch/detr'       ,  lowerCAmelCase__       ,  pretrained=lowerCAmelCase__		).eval()
  lowerCAmelCase_  :							Optional[Any]								=					detr.state_dict()
  # rename keys
  for src, dest in rename_keys:
    if is_panoptic:
      lowerCAmelCase_  :							List[Any]								=					'detr.' + src
    rename_key(lowerCAmelCase__       ,  lowerCAmelCase__       ,  lowerCAmelCase__		)
  lowerCAmelCase_  :							Optional[int]								=					rename_backbone_keys(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
  lowerCAmelCase_  :							List[str]								=					'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'		)
      ):
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Any								=					val
      elif "class_labels_classifier" in key or "bbox_predictor" in key:
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
      elif key.startswith('bbox_attention'		) or key.startswith('mask_head'		):
        continue
      else:
        lowerCAmelCase_  :							Optional[int]								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							Dict								=					val
    else:
      if not key.startswith('class_labels_classifier'		) and not key.startswith('bbox_predictor'		):
        lowerCAmelCase_  :							str								=					state_dict.pop(lowerCAmelCase__		)
        lowerCAmelCase_  :							str								=					val
    # finally, create HuggingFace model and load state dict
  lowerCAmelCase_  :							List[str]								=					DetrForSegmentation(lowerCAmelCase__		) if is_panoptic else DetrForObjectDetection(lowerCAmelCase__		)
  model.load_state_dict(lowerCAmelCase__		)
  model.eval()
  # verify our conversion
  lowerCAmelCase_  :							Tuple								=					detr(lowerCAmelCase__		)
  lowerCAmelCase_  :							List[Any]								=					model(lowerCAmelCase__		)
  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(lowerCAmelCase__		).mkdir(exist_ok=lowerCAmelCase__		)
  model.save_pretrained(lowerCAmelCase__		)
  image_processor.save_pretrained(lowerCAmelCase__		)


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

     parser.add_argument(
         """--model_name""", default="""detr_resnet50""", type=str, 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."""
     )
     lowercase__						:							Tuple        =      parser.parse_args()
     convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)





