

from typing import TYPE_CHECKING

from ..utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available


__A          =			{
    '''configuration_utils''': ['''GenerationConfig'''],
    '''streamers''': ['''TextIteratorStreamer''', '''TextStreamer'''],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A          =			[
     '''Constraint''',
     '''ConstraintListState''',
     '''DisjunctiveConstraint''',
     '''PhrasalConstraint''',
 ]
 __A          =			[
     '''BeamHypotheses''',
     '''BeamScorer''',
     '''BeamSearchScorer''',
     '''ConstrainedBeamSearchScorer''',
 ]
 __A          =			[
     '''EpsilonLogitsWarper''',
     '''EtaLogitsWarper''',
     '''ForcedBOSTokenLogitsProcessor''',
     '''ForcedEOSTokenLogitsProcessor''',
     '''HammingDiversityLogitsProcessor''',
     '''InfNanRemoveLogitsProcessor''',
     '''LogitsProcessor''',
     '''LogitsProcessorList''',
     '''LogitsWarper''',
     '''MinLengthLogitsProcessor''',
     '''MinNewTokensLengthLogitsProcessor''',
     '''NoBadWordsLogitsProcessor''',
     '''NoRepeatNGramLogitsProcessor''',
     '''PrefixConstrainedLogitsProcessor''',
     '''RepetitionPenaltyLogitsProcessor''',
     '''SequenceBiasLogitsProcessor''',
     '''EncoderRepetitionPenaltyLogitsProcessor''',
     '''TemperatureLogitsWarper''',
     '''TopKLogitsWarper''',
     '''TopPLogitsWarper''',
     '''TypicalLogitsWarper''',
     '''EncoderNoRepeatNGramLogitsProcessor''',
     '''ExponentialDecayLengthPenalty''',
     '''LogitNormalization''',
 ]
 __A          =			[
     '''MaxNewTokensCriteria''',
     '''MaxLengthCriteria''',
     '''MaxTimeCriteria''',
     '''StoppingCriteria''',
     '''StoppingCriteriaList''',
     '''validate_stopping_criteria''',
 ]
 __A          =			[
     '''GenerationMixin''',
     '''top_k_top_p_filtering''',
     '''GreedySearchEncoderDecoderOutput''',
     '''GreedySearchDecoderOnlyOutput''',
     '''SampleEncoderDecoderOutput''',
     '''SampleDecoderOnlyOutput''',
     '''BeamSearchEncoderDecoderOutput''',
     '''BeamSearchDecoderOnlyOutput''',
     '''BeamSampleEncoderDecoderOutput''',
     '''BeamSampleDecoderOnlyOutput''',
     '''ContrastiveSearchEncoderDecoderOutput''',
     '''ContrastiveSearchDecoderOnlyOutput''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A          =			[
     '''TFForcedBOSTokenLogitsProcessor''',
     '''TFForcedEOSTokenLogitsProcessor''',
     '''TFLogitsProcessor''',
     '''TFLogitsProcessorList''',
     '''TFLogitsWarper''',
     '''TFMinLengthLogitsProcessor''',
     '''TFNoBadWordsLogitsProcessor''',
     '''TFNoRepeatNGramLogitsProcessor''',
     '''TFRepetitionPenaltyLogitsProcessor''',
     '''TFTemperatureLogitsWarper''',
     '''TFTopKLogitsWarper''',
     '''TFTopPLogitsWarper''',
     '''TFForceTokensLogitsProcessor''',
     '''TFSuppressTokensAtBeginLogitsProcessor''',
     '''TFSuppressTokensLogitsProcessor''',
 ]
 __A          =			[
     '''TFGenerationMixin''',
     '''tf_top_k_top_p_filtering''',
     '''TFGreedySearchDecoderOnlyOutput''',
     '''TFGreedySearchEncoderDecoderOutput''',
     '''TFSampleEncoderDecoderOutput''',
     '''TFSampleDecoderOnlyOutput''',
     '''TFBeamSearchEncoderDecoderOutput''',
     '''TFBeamSearchDecoderOnlyOutput''',
     '''TFBeamSampleEncoderDecoderOutput''',
     '''TFBeamSampleDecoderOnlyOutput''',
     '''TFContrastiveSearchEncoderDecoderOutput''',
     '''TFContrastiveSearchDecoderOnlyOutput''',
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A          =			[
     '''FlaxForcedBOSTokenLogitsProcessor''',
     '''FlaxForcedEOSTokenLogitsProcessor''',
     '''FlaxLogitsProcessor''',
     '''FlaxLogitsProcessorList''',
     '''FlaxLogitsWarper''',
     '''FlaxMinLengthLogitsProcessor''',
     '''FlaxTemperatureLogitsWarper''',
     '''FlaxTopKLogitsWarper''',
     '''FlaxTopPLogitsWarper''',
 ]
 __A          =			[
     '''FlaxGenerationMixin''',
     '''FlaxGreedySearchOutput''',
     '''FlaxSampleOutput''',
     '''FlaxBeamSearchOutput''',
 ]

if TYPE_CHECKING:
 from .configuration_utils import GenerationConfig
 from .streamers import TextIteratorStreamer, TextStreamer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
  from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
  from .logits_process import (
      EncoderNoRepeatNGramLogitsProcessor,
      EncoderRepetitionPenaltyLogitsProcessor,
      EpsilonLogitsWarper,
      EtaLogitsWarper,
      ExponentialDecayLengthPenalty,
      ForcedBOSTokenLogitsProcessor,
      ForcedEOSTokenLogitsProcessor,
      HammingDiversityLogitsProcessor,
      InfNanRemoveLogitsProcessor,
      LogitNormalization,
      LogitsProcessor,
      LogitsProcessorList,
      LogitsWarper,
      MinLengthLogitsProcessor,
      MinNewTokensLengthLogitsProcessor,
      NoBadWordsLogitsProcessor,
      NoRepeatNGramLogitsProcessor,
      PrefixConstrainedLogitsProcessor,
      RepetitionPenaltyLogitsProcessor,
      SequenceBiasLogitsProcessor,
      TemperatureLogitsWarper,
      TopKLogitsWarper,
      TopPLogitsWarper,
      TypicalLogitsWarper,
  )
  from .stopping_criteria import (
      MaxLengthCriteria,
      MaxNewTokensCriteria,
      MaxTimeCriteria,
      StoppingCriteria,
      StoppingCriteriaList,
      validate_stopping_criteria,
  )
  from .utils import (
      BeamSampleDecoderOnlyOutput,
      BeamSampleEncoderDecoderOutput,
      BeamSearchDecoderOnlyOutput,
      BeamSearchEncoderDecoderOutput,
      ContrastiveSearchDecoderOnlyOutput,
      ContrastiveSearchEncoderDecoderOutput,
      GenerationMixin,
      GreedySearchDecoderOnlyOutput,
      GreedySearchEncoderDecoderOutput,
      SampleDecoderOnlyOutput,
      SampleEncoderDecoderOutput,
      top_k_top_p_filtering,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tf_logits_process import (
      TFForcedBOSTokenLogitsProcessor,
      TFForcedEOSTokenLogitsProcessor,
      TFForceTokensLogitsProcessor,
      TFLogitsProcessor,
      TFLogitsProcessorList,
      TFLogitsWarper,
      TFMinLengthLogitsProcessor,
      TFNoBadWordsLogitsProcessor,
      TFNoRepeatNGramLogitsProcessor,
      TFRepetitionPenaltyLogitsProcessor,
      TFSuppressTokensAtBeginLogitsProcessor,
      TFSuppressTokensLogitsProcessor,
      TFTemperatureLogitsWarper,
      TFTopKLogitsWarper,
      TFTopPLogitsWarper,
  )
  from .tf_utils import (
      TFBeamSampleDecoderOnlyOutput,
      TFBeamSampleEncoderDecoderOutput,
      TFBeamSearchDecoderOnlyOutput,
      TFBeamSearchEncoderDecoderOutput,
      TFContrastiveSearchDecoderOnlyOutput,
      TFContrastiveSearchEncoderDecoderOutput,
      TFGenerationMixin,
      TFGreedySearchDecoderOnlyOutput,
      TFGreedySearchEncoderDecoderOutput,
      TFSampleDecoderOnlyOutput,
      TFSampleEncoderDecoderOutput,
      tf_top_k_top_p_filtering,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .flax_logits_process import (
      FlaxForcedBOSTokenLogitsProcessor,
      FlaxForcedEOSTokenLogitsProcessor,
      FlaxLogitsProcessor,
      FlaxLogitsProcessorList,
      FlaxLogitsWarper,
      FlaxMinLengthLogitsProcessor,
      FlaxTemperatureLogitsWarper,
      FlaxTopKLogitsWarper,
      FlaxTopPLogitsWarper,
  )
  from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
 import sys

 __A          =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


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



def    snake_case_(_UpperCamelCase  )							->			Tuple:



  """simple docstring"""






  if "backbone.0.body" in orig_key:
    _snake_case		=							orig_key.replace('''backbone.0.body'''				, '''backbone.conv_encoder.model'''  )
  if "transformer" in orig_key:
    _snake_case		=							orig_key.replace('''transformer.'''				, ''''''  )
  if "norm1" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm1'''				, '''self_attn_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm1'''				, '''encoder_attn_layer_norm'''  )
  if "norm2" in orig_key:
    if "encoder" in orig_key:
      _snake_case		=							orig_key.replace('''norm2'''				, '''final_layer_norm'''  )
    else:
      _snake_case		=							orig_key.replace('''norm2'''				, '''self_attn_layer_norm'''  )
  if "norm3" in orig_key:
    _snake_case		=							orig_key.replace('''norm3'''				, '''final_layer_norm'''  )
  if "linear1" in orig_key:
    _snake_case		=							orig_key.replace('''linear1'''				, '''fc1'''  )
  if "linear2" in orig_key:
    _snake_case		=							orig_key.replace('''linear2'''				, '''fc2'''  )
  if "query_embed" in orig_key:
    _snake_case		=							orig_key.replace('''query_embed'''				, '''query_position_embeddings'''  )
  if "cross_attn" in orig_key:
    _snake_case		=							orig_key.replace('''cross_attn'''				, '''encoder_attn'''  )

  return orig_key



def    snake_case_(_UpperCamelCase  )							->			Any:



  """simple docstring"""






  for i in range(6  ):
    # read in weights + bias of input projection layer of self-attention
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""  )
    _snake_case		=							state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""  )
    # next, add query, keys and values (in that order) to the state dict
    _snake_case		=							in_proj_weight[:256, :]
    _snake_case		=							in_proj_bias[:256]
    _snake_case		=							in_proj_weight[256:512, :]
    _snake_case		=							in_proj_bias[256:512]
    _snake_case		=							in_proj_weight[-256:, :]
    _snake_case		=							in_proj_bias[-256:]



def    snake_case_()							->			int:



  """simple docstring"""






  _snake_case		=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case		=							Image.open(requests.get(_UpperCamelCase				, stream=_UpperCamelCase  ).raw  )

  return im



@torch.no_grad()
def    snake_case_(_UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, _UpperCamelCase				, )							->			Dict:



  """simple docstring"""






  _snake_case		=							DeformableDetrConfig()
  # set config attributes
  if single_scale:
    _snake_case		=							1
  _snake_case		=							dilation
  _snake_case		=							with_box_refine
  _snake_case		=							two_stage
  # set labels
  _snake_case		=							91
  _snake_case		=							'''huggingface/label-files'''
  _snake_case		=							'''coco-detection-id2label.json'''
  _snake_case		=							json.load(open(cached_download(hf_hub_url(_UpperCamelCase				, _UpperCamelCase				, repo_type='''dataset'''  )  )				, '''r'''  )  )
  _snake_case		=							{int(_UpperCamelCase  ): v for k, v in idalabel.items()}
  _snake_case		=							idalabel
  _snake_case		=							{v: k for k, v in idalabel.items()}

  # load image processor
  _snake_case		=							DeformableDetrImageProcessor(format='''coco_detection'''  )

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

  logger.info('''Converting model...'''  )

  # load original state dict
  _snake_case		=							torch.load(_UpperCamelCase				, map_location='''cpu'''  )['''model''']
  # rename keys
  for key in state_dict.copy().keys():
    _snake_case		=							state_dict.pop(_UpperCamelCase  )
    _snake_case		=							val
  # query, key and value matrices need special treatment
  read_in_q_k_v(_UpperCamelCase  )
  # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
  _snake_case		=							'''model.'''
  for key in state_dict.copy().keys():
    if not key.startswith('''class_embed'''  ) and not key.startswith('''bbox_embed'''  ):
      _snake_case		=							state_dict.pop(_UpperCamelCase  )
      _snake_case		=							val
    # finally, create HuggingFace model and load state dict
  _snake_case		=							DeformableDetrForObjectDetection(_UpperCamelCase  )
  model.load_state_dict(_UpperCamelCase  )
  model.eval()

  _snake_case		=							'''cuda''' if torch.cuda.is_available() else '''cpu'''
  model.to(_UpperCamelCase  )
  # verify our conversion
  _snake_case		=							model(pixel_values.to(_UpperCamelCase  )  )

  _snake_case		=							torch.tensor(
      [[-9.6645, -4.3449, -5.8705], [-9.7035, -3.8504, -5.0724], [-10.5634, -5.3379, -7.5116]]  )
  _snake_case		=							torch.tensor([[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]  )

  if single_scale:
    _snake_case		=							torch.tensor(
        [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]  )
    _snake_case		=							torch.tensor([[0.7292, 0.4991, 0.5532], [0.7959, 0.2426, 0.4236], [0.7582, 0.3518, 0.4451]]  )

  if single_scale and dilation:
    _snake_case		=							torch.tensor(
        [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]  )
    _snake_case		=							torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]  )

  if with_box_refine:
    _snake_case		=							torch.tensor(
        [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]  )
    _snake_case		=							torch.tensor([[0.7828, 0.2208, 0.4323], [0.0892, 0.5996, 0.1319], [0.5524, 0.6389, 0.8914]]  )

  if with_box_refine and two_stage:
    _snake_case		=							torch.tensor(
        [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]  )
    _snake_case		=							torch.tensor([[0.2583, 0.5499, 0.4683], [0.7652, 0.9068, 0.4882], [0.5490, 0.2763, 0.0564]]  )

  print('''Logits:'''				, outputs.logits[0, :3, :3]  )

  assert torch.allclose(outputs.logits[0, :3, :3]				, expected_logits.to(_UpperCamelCase  )				, atol=1E-4  )
  assert torch.allclose(outputs.pred_boxes[0, :3, :3]				, expected_boxes.to(_UpperCamelCase  )				, atol=1E-4  )

  print('''Everything ok!'''  )

  # 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  )

  # Push to hub
  if push_to_hub:
    _snake_case		=							'''deformable-detr'''
    model_name += "-single-scale" if single_scale else ""
    model_name += "-dc5" if dilation else ""
    model_name += "-with-box-refine" if with_box_refine else ""
    model_name += "-two-stage" if two_stage else ""
    print('''Pushing model to hub...'''  )
    model.push_to_hub(repo_path_or_name=_UpperCamelCase				, organization='''nielsr'''				, commit_message='''Add model'''  )


if __name__ == "__main__":
 __A    	=			argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''',
     type=str,
     default='''/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth''',
     help='''Path to Pytorch checkpoint (.pth file) you\'d like to convert.''',
 )
 parser.add_argument('''--single_scale''', action='''store_true''', help='''Whether to set config.num_features_levels = 1.''')
 parser.add_argument('''--dilation''', action='''store_true''', help='''Whether to set config.dilation=True.''')
 parser.add_argument('''--with_box_refine''', action='''store_true''', help='''Whether to set config.with_box_refine=True.''')
 parser.add_argument('''--two_stage''', action='''store_true''', help='''Whether to set config.two_stage=True.''')
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default=None,
     type=str,
     required=True,
     help='''Path to the folder to output PyTorch model.''',
 )
 parser.add_argument(
     '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
 )
 __A    	=			parser.parse_args()
 convert_deformable_detr_checkpoint(
     args.checkpoint_path,
     args.single_scale,
     args.dilation,
     args.with_box_refine,
     args.two_stage,
     args.pytorch_dump_folder_path,
     args.push_to_hub,
 )

