"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers.models.openai.modeling_tf_openai import (
      TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFOpenAIGPTDoubleHeadsModel,
      TFOpenAIGPTForSequenceClassification,
      TFOpenAIGPTLMHeadModel,
      TFOpenAIGPTModel,
  )
class   UpperCAmelCase_	:
    def __init__(	self			:       Tuple   ,						_lowercase			:       List[str]   ,						)						->    Tuple:
       _lowercase							     =       parent
       _lowercase							     =       1_3
       _lowercase							     =       7
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       True
       _lowercase							     =       9_9
       _lowercase							     =       3_2
       _lowercase							     =       2
       _lowercase							     =       4
       _lowercase							     =       3_7
       _lowercase							     =       "gelu"
       _lowercase							     =       0.1
       _lowercase							     =       0.1
       _lowercase							     =       5_1_2
       _lowercase							     =       1_6
       _lowercase							     =       2
       _lowercase							     =       0.02
       _lowercase							     =       3
       _lowercase							     =       4
       _lowercase							     =       None
       _lowercase							     =       self.vocab_size - 1
    def 		_lowerCamelCase      (	self			:       int		)						->    List[Any]:
       _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.vocab_size		)

       _lowercase							     =       None
       if self.use_input_mask:
          _lowercase							     =       random_attention_mask([self.batch_size, self.seq_length]		)

       _lowercase							     =       None
       if self.use_token_type_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.type_vocab_size		)

       _lowercase							     =       None
       if self.use_mc_token_ids:
          _lowercase							     =       ids_tensor([self.batch_size, self.num_choices]   ,						self.seq_length		)

       _lowercase							     =       None
       _lowercase							     =       None
       _lowercase							     =       None
       if self.use_labels:
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
          _lowercase							     =       ids_tensor([self.batch_size, self.seq_length]   ,						self.num_labels		)
          _lowercase							     =       ids_tensor([self.batch_size]   ,						self.num_choices		)

       _lowercase							     =       OpenAIGPTConfig(
           vocab_size=self.vocab_size   ,						n_embd=self.hidden_size   ,						n_layer=self.num_hidden_layers   ,						n_head=self.num_attention_heads   ,						n_positions=self.max_position_embeddings   ,						pad_token_id=self.pad_token_id   ,						)

       _lowercase							     =       ids_tensor([self.num_hidden_layers, self.num_attention_heads]   ,						2		)

       return (
           config,
           input_ids,
           input_mask,
           head_mask,
           token_type_ids,
           mc_token_ids,
           sequence_labels,
           token_labels,
           choice_labels,
       )
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[Any]   ,						_lowercase			:       int   ,						*_lowercase			:       List[str]		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       [input_ids, input_mask]
       _lowercase							     =       model(_lowercase		)

       _lowercase							     =       model(_lowercase		)

       self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.seq_length, self.hidden_size)		)
    def 		_lowerCamelCase      (	self			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       str   ,						_lowercase			:       Dict   ,						_lowercase			:       List[str]   ,						*_lowercase			:       Optional[Any]		)						->    Dict:
       _lowercase							     =       TFOpenAIGPTLMHeadModel(config=_lowercase		)
       _lowercase							     =       {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.seq_length, self.vocab_size)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       int   ,						_lowercase			:       str   ,						_lowercase			:       int   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       Tuple		)						->    Optional[int]:
       _lowercase							     =       TFOpenAIGPTDoubleHeadsModel(config=_lowercase		)

       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)
       _lowercase							     =       tf.tile(tf.expand_dims(_lowercase   ,						1		)   ,						(1, self.num_choices, 1)		)

       _lowercase							     =       {
           "input_ids": multiple_choice_inputs_ids,
           "mc_token_ids": mc_token_ids,
           "attention_mask": multiple_choice_input_mask,
           "token_type_ids": multiple_choice_token_type_ids,
       }
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(
           result.logits.shape   ,						(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)		)
       self.parent.assertEqual(result.mc_logits.shape   ,						(self.batch_size, self.num_choices)		)
    def 		_lowerCamelCase      (	self			:       Union[str, Any]   ,						_lowercase			:       List[Any]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Any   ,						_lowercase			:       Optional[Any]   ,						_lowercase			:       List[Any]   ,						*_lowercase			:       str		)						->    str:
       _lowercase							     =       self.num_labels
       _lowercase							     =       ids_tensor([self.batch_size]   ,						self.type_sequence_label_size		)
       _lowercase							     =       {
           "input_ids": input_ids,
           "attention_mask": input_mask,
           "token_type_ids": token_type_ids,
           "labels": sequence_labels,
       }
       _lowercase							     =       TFOpenAIGPTForSequenceClassification(_lowercase		)
       _lowercase							     =       model(_lowercase		)
       self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels)		)




    def 		_lowerCamelCase      (	self			:       str		)						->    Tuple:
       _lowercase							     =       self.prepare_config_and_inputs()

       (
           (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  (
           _lowercase
       )     ,  
       )							     =       config_and_inputs

       _lowercase							     =       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
       return config, inputs_dict
@require_tf
class   UpperCAmelCase_	(				lowercase__,    lowercase__,    unittest.TestCase   ):
    snake_case_											=      (
        (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
        if is_tf_available()
        else ()
    )
    snake_case_											=      (
        (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
    )  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
    snake_case_											=      (
        {
            """feature-extraction""": TFOpenAIGPTModel,
            """text-classification""": TFOpenAIGPTForSequenceClassification,
            """text-generation""": TFOpenAIGPTLMHeadModel,
            """zero-shot""": TFOpenAIGPTForSequenceClassification,
        }
        if is_tf_available()
        else {}
    )
    snake_case_											=      False
    snake_case_											=      False
    def 		_lowerCamelCase      (	self			:       Optional[int]   ,						_lowercase			:       Optional[int]   ,						_lowercase			:       List[str]   ,						_lowercase			:       Tuple   ,						_lowercase			:       Union[str, Any]   ,						_lowercase			:       Any		)						->    List[str]:
       if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
          # Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
          # `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
          # tiny config could not be created.
          return True

       return False
    def 		_lowerCamelCase      (	self			:       Union[str, Any]		)						->    str:
       _lowercase							     =       TFOpenAIGPTModelTester(self		)
       _lowercase							     =       ConfigTester(self   ,						config_class=_lowercase   ,						n_embd=3_7		)
    def 		_lowerCamelCase      (	self			:       List[Any]		)						->    Optional[int]:
       self.config_tester.run_common_tests()
    def 		_lowerCamelCase      (	self			:       Any		)						->    List[str]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_model(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[int]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_lm_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    List[Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_double_head(*_lowercase		)
    def 		_lowerCamelCase      (	self			:       int		)						->    Union[str, Any]:
       _lowercase							     =       self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_lowercase		)




    @slow
    def 		_lowerCamelCase      (	self			:       Tuple		)						->    Dict:
       for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          _lowercase							     =       TFOpenAIGPTModel.from_pretrained(_lowercase		)
          self.assertIsNotNone(_lowercase		)
@require_tf
class   UpperCAmelCase_	(				unittest.TestCase   ):
    @slow
    def 		_lowerCamelCase      (	self			:       Optional[Any]		)						->    Union[str, Any]:
       _lowercase							     =       TFOpenAIGPTLMHeadModel.from_pretrained("openai-gpt"		)
       _lowercase							     =       tf.convert_to_tensor([[4_8_1, 4_7_3_5, 5_4_4]]   ,						dtype=tf.intaa		)  # the president is
       _lowercase							     =       [
           4_8_1,
           4_7_3_5,
           5_4_4,
           2_4_6,
           9_6_3,
           8_7_0,
           7_6_2,
           2_3_9,
           2_4_4,
           4_0_4_7_7,
           2_4_4,
           2_4_9,
           7_1_9,
           8_8_1,
           4_8_7,
           5_4_4,
           2_4_0,
           2_4_4,
           6_0_3,
           4_8_1,
       ]  # the president is a very good man. " \n " i\'m sure he is, " said the

       _lowercase							     =       model.generate(_lowercase   ,						do_sample=_lowercase		)
       self.assertListEqual(output_ids[0].numpy().tolist()   ,						_lowercase		)
"""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()
__UpperCamelCase       :    Union[str, Any]         =		logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase       :    Optional[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     (	_snake_case		:       List[str],				_snake_case		:       Tuple,				_snake_case		:       Any       ):
   _lowercase							     =       state_dict.pop(_snake_case       )
   _lowercase							     =       val


def   __UpperCAmelCase     (	_snake_case		:       Optional[int]       ):
   _lowercase							     =       OrderedDict()
   for key, value in state_dict.items():
      if "backbone.0.body" in key:
         _lowercase							     =       key.replace("backbone.0.body",				"backbone.conv_encoder.model"       )
         _lowercase							     =       value
      else:
         _lowercase							     =       value

   return new_state_dict


def   __UpperCAmelCase     (	_snake_case		:       Optional[Any],				_snake_case		:       int=False       ):
   _lowercase							     =       ""
   if is_panoptic:
      _lowercase							     =       "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)
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
   # 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
      _lowercase							     =       state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight[:2_5_6, :]
      _lowercase							     =       in_proj_bias[:2_5_6]
      _lowercase							     =       in_proj_weight[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight[-2_5_6:, :]
      _lowercase							     =       in_proj_bias[-2_5_6:]
      # read in weights + bias of input projection layer of cross-attention
      _lowercase							     =       state_dict.pop(
          f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""       )
      _lowercase							     =       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
      _lowercase							     =       in_proj_weight_cross_attn[:2_5_6, :]
      _lowercase							     =       in_proj_bias_cross_attn[:2_5_6]
      _lowercase							     =       in_proj_weight_cross_attn[2_5_6:5_1_2, :]
      _lowercase							     =       in_proj_bias_cross_attn[2_5_6:5_1_2]
      _lowercase							     =       in_proj_weight_cross_attn[-2_5_6:, :]
      _lowercase							     =       in_proj_bias_cross_attn[-2_5_6:]


def   __UpperCAmelCase     (	):
   _lowercase							     =       "http://images.cocodataset.org/val2017/000000039769.jpg"
   _lowercase							     =       Image.open(requests.get(_snake_case,				stream=_snake_case       ).raw       )

   return im


@torch.no_grad()
def   __UpperCAmelCase     (	_snake_case		:       Tuple,				_snake_case		:       str       ):
   _lowercase							     =       DetrConfig()
   # set backbone and dilation attributes
   if "resnet101" in model_name:
      _lowercase							     =       "resnet101"
   if "dc5" in model_name:
      _lowercase							     =       True
   _lowercase							     =       "panoptic" in model_name
   if is_panoptic:
      _lowercase							     =       2_5_0
   else:
      _lowercase							     =       9_1
      _lowercase							     =       "huggingface/label-files"
      _lowercase							     =       "coco-detection-id2label.json"
      _lowercase							     =       json.load(open(hf_hub_download(_snake_case,				_snake_case,				repo_type="dataset"       ),				"r"       )       )
      _lowercase							     =       {int(_snake_case       ): v for k, v in idalabel.items()}
      _lowercase							     =       idalabel
      _lowercase							     =       {v: k for k, v in idalabel.items()}

   # load image processor
   _lowercase							     =       "coco_panoptic" if is_panoptic else "coco_detection"
   _lowercase							     =       DetrImageProcessor(format=_snake_case       )

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

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

   # load original model from torch hub
   _lowercase							     =       torch.hub.load("facebookresearch/detr",				_snake_case,				pretrained=_snake_case       ).eval()
   _lowercase							     =       detr.state_dict()
   # rename keys
   for src, dest in rename_keys:
      if is_panoptic:
         _lowercase							     =       "detr." + src
      rename_key(_snake_case,				_snake_case,				_snake_case       )
   _lowercase							     =       rename_backbone_keys(_snake_case       )
   # query, key and value matrices need special treatment
   read_in_q_k_v(_snake_case,				is_panoptic=_snake_case       )
   # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
   _lowercase							     =       "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"       )
         ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif "class_labels_classifier" in key or "bbox_predictor" in key:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
         elif key.startswith("bbox_attention"       ) or key.startswith("mask_head"       ):
            continue
         else:
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
      else:
         if not key.startswith("class_labels_classifier"       ) and not key.startswith("bbox_predictor"       ):
            _lowercase							     =       state_dict.pop(_snake_case       )
            _lowercase							     =       val
    # finally, create HuggingFace model and load state dict
   _lowercase							     =       DetrForSegmentation(_snake_case       ) if is_panoptic else DetrForObjectDetection(_snake_case       )
   model.load_state_dict(_snake_case       )
   model.eval()
   # verify our conversion
   _lowercase							     =       detr(_snake_case       )
   _lowercase							     =       model(_snake_case       )
   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(_snake_case       ).mkdir(exist_ok=_snake_case       )
   model.save_pretrained(_snake_case       )
   image_processor.save_pretrained(_snake_case       )


if __name__ == "__main__":
  __UpperCamelCase       :    int         =		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."
  )
  __UpperCamelCase       :    str         =		parser.parse_args()
  convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)