



'''simple docstring'''






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


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

A_				:					Dict							=      {
    """facebook/blenderbot-3B""": """https://huggingface.co/facebook/blenderbot-3B/resolve/main/config.json""",
    # See all Blenderbot models at https://huggingface.co/models?filter=blenderbot
}



class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 UpperCAmelCase		      =    """blenderbot"""
 UpperCAmelCase		      =    ["""past_key_values"""]
 UpperCAmelCase		      =    {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







 def __init__(				self    ,a_=8_008    ,a_=128    ,a_=2    ,a_=10_240    ,a_=32    ,a_=24    ,a_=10_240    ,a_=32    ,a_=0.0    ,a_=0.0    ,a_=True    ,a_=True    ,a_="gelu"    ,a_=2_560    ,a_=0.1    ,a_=0.0    ,a_=0.0    ,a_=0.02    ,a_=1    ,a_=False    ,a_=0    ,a_=1    ,a_=2    ,a_=3    ,a_=2    ,**a_    ,)						->  Optional[Any]:
        _UpperCAmelCase    :		List[Any]     	=					vocab_size
        _UpperCAmelCase    :		Dict     	=					max_position_embeddings
        _UpperCAmelCase    :		int     	=					d_model
        _UpperCAmelCase    :		Any     	=					encoder_ffn_dim
        _UpperCAmelCase    :		Tuple     	=					encoder_layers
        _UpperCAmelCase    :		Optional[Any]     	=					encoder_attention_heads
        _UpperCAmelCase    :		int     	=					decoder_ffn_dim
        _UpperCAmelCase    :		List[Any]     	=					decoder_layers
        _UpperCAmelCase    :		str     	=					decoder_attention_heads
        _UpperCAmelCase    :		str     	=					dropout
        _UpperCAmelCase    :		int     	=					attention_dropout
        _UpperCAmelCase    :		Any     	=					activation_dropout
        _UpperCAmelCase    :		Any     	=					activation_function
        _UpperCAmelCase    :		Dict     	=					init_std
        _UpperCAmelCase    :		Dict     	=					encoder_layerdrop
        _UpperCAmelCase    :		str     	=					decoder_layerdrop
        _UpperCAmelCase    :		List[str]     	=					use_cache
        _UpperCAmelCase    :		Tuple     	=					encoder_layers
        _UpperCAmelCase    :		Any     	=					scale_embedding  # scale factor will be sqrt(d_model) if True

        super().__init__(
            pad_token_id=a_    ,bos_token_id=a_    ,eos_token_id=a_    ,is_encoder_decoder=a_    ,decoder_start_token_id=a_    ,encoder_no_repeat_ngram_size=a_    ,forced_eos_token_id=a_    ,**a_    ,)







class 				lowercase			(    _lowerCamelCase							):


 """simple docstring"""


 @property
 def _snake_case					(				self				)						->  Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		Any     	=					OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]				)
               if self.use_past:
                      _UpperCAmelCase    :		List[Any]     	=					{0: """batch"""}
                      _UpperCAmelCase    :		Dict     	=					{0: """batch""", 1: """past_decoder_sequence + sequence"""}
               else:
                      _UpperCAmelCase    :		Tuple     	=					{0: """batch""", 1: """decoder_sequence"""}
                      _UpperCAmelCase    :		Any     	=					{0: """batch""", 1: """decoder_sequence"""}
               if self.use_past:
                      self.fill_with_past_key_values_(a_    ,direction="""inputs"""				)
        elif self.task == "causal-lm":
               _UpperCAmelCase    :		str     	=					OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                   ]				)
               if self.use_past:
                      _UpperCAmelCase	,_UpperCAmelCase    :		Optional[Any]     	=					self.num_layers
                      for i in range(a_				):
                             _UpperCAmelCase    :		Tuple     	=					{0: """batch""", 2: """past_sequence + sequence"""}
                             _UpperCAmelCase    :		Union[str, Any]     	=					{0: """batch""", 2: """past_sequence + sequence"""}
        else:
               _UpperCAmelCase    :		str     	=					OrderedDict(
                   [
                       ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                       ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                       ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
                   ]				)

        return common_inputs







 @property
 # Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
 def _snake_case					(				self				)						->  Mapping[str, Mapping[int, str]]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		str     	=					super().outputs
        else:
               _UpperCAmelCase    :		List[str]     	=					super(a_    ,self				).outputs
               if self.use_past:
                      _UpperCAmelCase	,_UpperCAmelCase    :		Optional[int]     	=					self.num_layers
                      for i in range(a_				):
                             _UpperCAmelCase    :		Any     	=					{0: """batch""", 2: """past_sequence + sequence"""}
                             _UpperCAmelCase    :		List[Any]     	=					{0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        _UpperCAmelCase    :		List[Any]     	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_    ,a_    ,a_    ,a_    ,a_				)
        # Generate decoder inputs
        _UpperCAmelCase    :		Dict     	=					seq_length if not self.use_past else 1
        _UpperCAmelCase    :		Dict     	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_    ,a_    ,a_    ,a_    ,a_				)
        _UpperCAmelCase    :		Tuple     	=					{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
        _UpperCAmelCase    :		Tuple     	=					dict(**a_    ,**a_				)

        if self.use_past:
               if not is_torch_available():
                      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
               else:
                      import torch
               _UpperCAmelCase	,_UpperCAmelCase    :		Optional[int]     	=					common_inputs["""input_ids"""].shape
               _UpperCAmelCase    :		Optional[Any]     	=					common_inputs["""decoder_input_ids"""].shape[1]
               _UpperCAmelCase	,_UpperCAmelCase    :		Tuple     	=					self.num_attention_heads
               _UpperCAmelCase    :		Union[str, Any]     	=					(
                   batch,
                   num_encoder_attention_heads,
                   encoder_seq_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               _UpperCAmelCase    :		Union[str, Any]     	=					decoder_seq_length
               _UpperCAmelCase    :		List[Any]     	=					(
                   batch,
                   num_decoder_attention_heads,
                   decoder_past_length,
                   self._config.hidden_size // num_decoder_attention_heads,
               )
               _UpperCAmelCase    :		Optional[Any]     	=					torch.cat(
                   [common_inputs["""decoder_attention_mask"""], torch.ones(a_    ,a_				)]    ,dim=1				)
               _UpperCAmelCase    :		Dict     	=					[]
               _UpperCAmelCase	,_UpperCAmelCase    :		int     	=					self.num_layers

               for _ in range(a_				):
                      common_inputs["past_key_values"].append(
                          (
                              torch.zeros(a_				),
                              torch.zeros(a_				),
                              torch.zeros(a_				),
                              torch.zeros(a_				),
                          )				)
        return common_inputs







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        _UpperCAmelCase    :		Optional[Any]     	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            a_    ,a_    ,a_    ,a_    ,a_				)

        if self.use_past:
               if not is_torch_available():
                      raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""				)
               else:
                      import torch
               _UpperCAmelCase	,_UpperCAmelCase    :		List[Any]     	=					common_inputs["""input_ids"""].shape
               _UpperCAmelCase    :		Dict     	=					seqlen
               _UpperCAmelCase	,_UpperCAmelCase    :		List[Any]     	=					self.num_layers
               _UpperCAmelCase	,_UpperCAmelCase    :		Dict     	=					self.num_attention_heads
               _UpperCAmelCase    :		str     	=					(
                   batch,
                   num_encoder_attention_heads,
                   past_key_values_length,
                   self._config.hidden_size // num_encoder_attention_heads,
               )
               _UpperCAmelCase    :		Dict     	=					common_inputs["""attention_mask"""].dtype
               _UpperCAmelCase    :		List[str]     	=					torch.cat(
                   [common_inputs["""attention_mask"""], torch.ones(a_    ,a_    ,dtype=a_				)]    ,dim=1				)
               _UpperCAmelCase    :		Any     	=					[
                   (torch.zeros(a_				), torch.zeros(a_				)) for _ in range(a_				)
               ]
        return common_inputs







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        # Copied from OnnxConfig.generate_dummy_inputs
        # Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
        # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
        _UpperCAmelCase    :		List[str]     	=					compute_effective_axis_dimension(
            a_    ,fixed_dimension=OnnxConfig.default_fixed_batch    ,num_token_to_add=0				)

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        _UpperCAmelCase    :		List[str]     	=					tokenizer.num_special_tokens_to_add(a_				)
        _UpperCAmelCase    :		str     	=					compute_effective_axis_dimension(
            a_    ,fixed_dimension=OnnxConfig.default_fixed_sequence    ,num_token_to_add=a_				)

        # Generate dummy inputs according to compute batch and sequence
        _UpperCAmelCase    :		Dict     	=					[""" """.join([tokenizer.unk_token]				) * seq_length] * batch_size
        _UpperCAmelCase    :		Optional[Any]     	=					dict(tokenizer(a_    ,return_tensors=a_				)				)
        return common_inputs







 def _snake_case					(				self    ,a_    ,a_ = -1    ,a_ = -1    ,a_ = False    ,a_ = None    ,)						->  Mapping[str, Any]:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		int     	=					self._generate_dummy_inputs_for_default_and_seqaseq_lm(
                   a_    ,batch_size=a_    ,seq_length=a_    ,is_pair=a_    ,framework=a_				)

        elif self.task == "causal-lm":
               _UpperCAmelCase    :		Tuple     	=					self._generate_dummy_inputs_for_causal_lm(
                   a_    ,batch_size=a_    ,seq_length=a_    ,is_pair=a_    ,framework=a_				)
        else:
               _UpperCAmelCase    :		List[str]     	=					self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
                   a_    ,batch_size=a_    ,seq_length=a_    ,is_pair=a_    ,framework=a_				)

        return common_inputs







 def _snake_case					(				self    ,a_    ,a_    ,a_    ,a_				)						->  str:
        if self.task in ["default", "seq2seq-lm"]:
               _UpperCAmelCase    :		Optional[Any]     	=					super()._flatten_past_key_values_(a_    ,a_    ,a_    ,a_				)
        else:
               _UpperCAmelCase    :		Tuple     	=					super(a_    ,self				)._flatten_past_key_values_(
                   a_    ,a_    ,a_    ,a_				)







 def _snake_case					(				self    ,a_    ,a_				)						->  Any:
        if direction not in ["inputs", "outputs"]:
               raise ValueError(f'''direction must either be "inputs" or "outputs", but {direction} was given'''				)

        _UpperCAmelCase    :		Union[str, Any]     	=					"""past_key_values""" if direction == """inputs""" else """present"""
        _UpperCAmelCase	,_UpperCAmelCase    :		Optional[Any]     	=					self.num_layers

        _UpperCAmelCase    :		int     	=					"""past_encoder_sequence"""
        _UpperCAmelCase    :		str     	=					"""past_decoder_sequence""" if direction == """inputs""" else """past_decoder_sequence + sequence"""

        for i in range(a_				):
               _UpperCAmelCase    :		Union[str, Any]     	=					{0: """batch""", 2: decoder_sequence}
               _UpperCAmelCase    :		Dict     	=					{0: """batch""", 2: decoder_sequence}
               _UpperCAmelCase    :		Dict     	=					{0: """batch""", 2: encoder_sequence}
               _UpperCAmelCase    :		Union[str, Any]     	=					{0: """batch""", 2: encoder_sequence}








"""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)





