




"""simple docstring"""

import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VivitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VivitForVideoClassification, VivitModel
						from transformers.models.vivit.modeling_vivit import VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from transformers import VivitImageProcessor



class  _snake_case				:

				'''simple docstring'''




				def __init__(							self      :  Tuple					,							snake_case      :  Tuple					,							snake_case      :  Any=2					,							snake_case      :  Dict=True					,							snake_case      :  Any=True					,							snake_case      :  Union[str, Any]=10					,							snake_case      :  Optional[Any]=10					,							snake_case      :  Dict=8					,							snake_case      :  Optional[Any]=[2, 4, 4]					,							snake_case      :  List[str]=3					,							snake_case      :  int=768					,							snake_case      :  str=5					,							snake_case      :  Dict=4					,							snake_case      :  Any=37					,							snake_case      :  Optional[Any]="gelu_fast"					,							snake_case      :  Any=0.0					,							snake_case      :  str=0.0					,							snake_case      :  List[str]=0.02					,							snake_case      :  Union[str, Any]=1e-06					,							snake_case      :  List[str]=True					,							snake_case      :  Optional[int]=None					,							):
								UpperCAmelCase_					:List[Any]      			=      parent
								UpperCAmelCase_					:Any      			=      batch_size
								UpperCAmelCase_					:List[Any]      			=      is_training
								UpperCAmelCase_					:Optional[Any]      			=      use_labels
								UpperCAmelCase_					:Union[str, Any]      			=      num_labels
								UpperCAmelCase_					:Tuple      			=      image_size
								UpperCAmelCase_					:Tuple      			=      num_frames
								UpperCAmelCase_					:Optional[int]      			=      tubelet_size
								UpperCAmelCase_					:Any      			=      num_channels
								UpperCAmelCase_					:Optional[Any]      			=      hidden_size
								UpperCAmelCase_					:Union[str, Any]      			=      num_hidden_layers
								UpperCAmelCase_					:int      			=      num_attention_heads
								UpperCAmelCase_					:List[Any]      			=      intermediate_size
								UpperCAmelCase_					:Tuple      			=      hidden_act
								UpperCAmelCase_					:Tuple      			=      hidden_dropout_prob
								UpperCAmelCase_					:Dict      			=      attention_probs_dropout_prob
								UpperCAmelCase_					:str      			=      initializer_range
								UpperCAmelCase_					:Optional[Any]      			=      layer_norm_eps
								UpperCAmelCase_					:Tuple      			=      qkv_bias
								UpperCAmelCase_					:str      			=      scope

								UpperCAmelCase_					:Optional[Any]      			=      (
								    (self.image_size // self.tubelet_size[2])
								    * (self.image_size // self.tubelet_size[1])
								    * (self.num_frames // self.tubelet_size[0])
								) + 1  # CLS token





				def 				snake_case_ (							self      :  List[str]     ):
								UpperCAmelCase_					:List[Any]      			=      floats_tensor(
								    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]     )

								UpperCAmelCase_					:List[Any]      			=      None
								if self.use_labels:
												UpperCAmelCase_					:List[Any]      			=      ids_tensor([self.batch_size]					,							self.num_labels     )

								UpperCAmelCase_					:Optional[int]      			=      self.get_config()

								return config, pixel_values, labels





				def 				snake_case_ (							self      :  Optional[Any]     ):
								UpperCAmelCase_					:Optional[Any]      			=      VivitConfig(
								    num_frames=self.num_frames					,							image_size=self.image_size					,							tubelet_size=self.tubelet_size					,							num_channels=self.num_channels					,							hidden_size=self.hidden_size					,							num_hidden_layers=self.num_hidden_layers					,							num_attention_heads=self.num_attention_heads					,							intermediate_size=self.intermediate_size					,							hidden_act=self.hidden_act					,							hidden_dropout_prob=self.hidden_dropout_prob					,							attention_probs_dropout_prob=self.attention_probs_dropout_prob					,							initializer_range=self.initializer_range					,							layer_norm_eps=self.layer_norm_eps					,							qkv_bias=self.qkv_bias					,							)
								UpperCAmelCase_					:int      			=      self.num_labels
								return config





				def 				snake_case_ (							self      :  int					,							snake_case      :  Tuple					,							snake_case      :  int					,							snake_case      :  Tuple     ):
								UpperCAmelCase_					:List[str]      			=      VivitModel(config=snake_case     )
								model.to(snake_case     )
								model.eval()
								UpperCAmelCase_					:Optional[Any]      			=      model(snake_case     )
								self.parent.assertEqual(result.last_hidden_state.shape					,							(self.batch_size, self.seq_length, self.hidden_size)     )





				def 				snake_case_ (							self      :  Optional[Any]					,							snake_case      :  Tuple					,							snake_case      :  Union[str, Any]					,							snake_case      :  List[str]     ):
								UpperCAmelCase_					:Optional[Any]      			=      VivitForVideoClassification(snake_case     )
								model.to(snake_case     )
								model.eval()

								UpperCAmelCase_					:Optional[int]      			=      model(snake_case     )

								# verify the logits shape
								UpperCAmelCase_					:str      			=      torch.Size((self.batch_size, self.num_labels)     )
								self.parent.assertEqual(result.logits.shape					,							snake_case     )





				def 				snake_case_ (							self      :  str     ):
								UpperCAmelCase_					:Optional[Any]      			=      self.prepare_config_and_inputs()
								UpperCAmelCase_					:Optional[Any]      			=      config_and_inputs
								UpperCAmelCase_					:str      			=      {'''pixel_values''': pixel_values}
								return config, inputs_dict



@require_torch
class  _snake_case				(							A__							,						unittest.TestCase       ):

				'''simple docstring'''




				UpperCamelCase__		     =(VivitModel, VivitForVideoClassification) if is_torch_available() else ()

				UpperCamelCase__		     =False
				UpperCamelCase__		     =False
				UpperCamelCase__		     =False
				UpperCamelCase__		     =False





				def 				snake_case_ (							self      :  str     ):
								UpperCAmelCase_					:Optional[int]      			=      VivitModelTester(self     )
								UpperCAmelCase_					:List[Any]      			=      ConfigTester(self					,							config_class=snake_case					,							has_text_modality=snake_case					,							hidden_size=37     )





				def 				snake_case_ (							self      :  int					,							snake_case      :  Any					,							snake_case      :  Dict					,							snake_case      :  Tuple=False     ):
								UpperCAmelCase_					:Tuple      			=      copy.deepcopy(snake_case     )

								if return_labels:
												if model_class in get_values(snake_case     ):
																UpperCAmelCase_					:Any      			=      torch.zeros(
																    self.model_tester.batch_size					,							dtype=torch.long					,							device=snake_case     )

								return inputs_dict





				def 				snake_case_ (							self      :  Union[str, Any]     ):
								self.config_tester.run_common_tests()





				@unittest.skip(reason='''Vivit does not use inputs_embeds'''     )
				def 				snake_case_ (							self      :  List[Any]     ):
								pass





				def 				snake_case_ (							self      :  Dict     ):
								UpperCAmelCase_					:Dict      			=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												UpperCAmelCase_					:int      			=      model_class(snake_case     )
												self.assertIsInstance(model.get_input_embeddings()					,							(nn.Module)     )
												UpperCAmelCase_					:int      			=      model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(snake_case					,							nn.Linear     )     )





				def 				snake_case_ (							self      :  Union[str, Any]     ):
								UpperCAmelCase_					:Dict      			=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												UpperCAmelCase_					:Union[str, Any]      			=      model_class(snake_case     )
												UpperCAmelCase_					:Any      			=      inspect.signature(model.forward     )
												# signature.parameters is an OrderedDict => so arg_names order is deterministic
												UpperCAmelCase_					:Any      			=      [*signature.parameters.keys()]

												UpperCAmelCase_					:str      			=      ['''pixel_values''', '''head_mask''']
												self.assertListEqual(arg_names[:2]					,							snake_case     )





				def 				snake_case_ (							self      :  str     ):
								UpperCAmelCase_					:int      			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*snake_case     )





				def 				snake_case_ (							self      :  Dict     ):
								UpperCAmelCase_					:Optional[Any]      			=      self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_video_classification(*snake_case     )





				@slow
				def 				snake_case_ (							self      :  Optional[Any]     ):
								for model_name in VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												UpperCAmelCase_					:Union[str, Any]      			=      VivitModel.from_pretrained(snake_case     )
												self.assertIsNotNone(snake_case     )





				def 				snake_case_ (							self      :  int     ):
								UpperCAmelCase_					:Union[str, Any]      			=      self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_					:Optional[int]      			=      True

								for model_class in self.all_model_classes:
												UpperCAmelCase_					:Tuple      			=      self.model_tester.seq_length

												UpperCAmelCase_					:Union[str, Any]      			=      True
												UpperCAmelCase_					:List[str]      			=      False
												UpperCAmelCase_					:List[Any]      			=      True
												UpperCAmelCase_					:Dict      			=      model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
																UpperCAmelCase_					:Any      			=      model(**self._prepare_for_class(snake_case					,							snake_case     )     )
												UpperCAmelCase_					:int      			=      outputs.attentions
												self.assertEqual(len(snake_case     )					,							self.model_tester.num_hidden_layers     )

												# check that output_attentions also work using config
												del inputs_dict["output_attentions"]
												UpperCAmelCase_					:str      			=      True
												UpperCAmelCase_					:int      			=      model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
																UpperCAmelCase_					:List[str]      			=      model(**self._prepare_for_class(snake_case					,							snake_case     )     )
												UpperCAmelCase_					:Optional[int]      			=      outputs.attentions
												self.assertEqual(len(snake_case     )					,							self.model_tester.num_hidden_layers     )

												self.assertListEqual(
												    list(attentions[0].shape[-3:]     )					,							[self.model_tester.num_attention_heads, seq_len, seq_len]					,							)
												UpperCAmelCase_					:Tuple      			=      len(snake_case     )

												# Check attention is always last and order is fine
												UpperCAmelCase_					:Tuple      			=      True
												UpperCAmelCase_					:Any      			=      True
												UpperCAmelCase_					:Optional[Any]      			=      model_class(snake_case     )
												model.to(snake_case     )
												model.eval()
												with torch.no_grad():
																UpperCAmelCase_					:Tuple      			=      model(**self._prepare_for_class(snake_case					,							snake_case     )     )

												self.assertEqual(out_len + 1					,							len(snake_case     )     )

												UpperCAmelCase_					:int      			=      outputs.attentions

												self.assertEqual(len(snake_case     )					,							self.model_tester.num_hidden_layers     )

												self.assertListEqual(
												    list(self_attentions[0].shape[-3:]     )					,							[self.model_tester.num_attention_heads, seq_len, seq_len]					,							)





				def 				snake_case_ (							self      :  Any     ):
								def check_hidden_states_output(snake_case      :  Optional[int]					,							snake_case      :  List[str]					,							snake_case      :  Any     ):
												UpperCAmelCase_					:Any      			=      model_class(snake_case     )
												model.to(snake_case     )
												model.eval()

												with torch.no_grad():
																UpperCAmelCase_					:List[str]      			=      model(**self._prepare_for_class(snake_case					,							snake_case     )     )

												UpperCAmelCase_					:int      			=      outputs.hidden_states
												UpperCAmelCase_					:List[str]      			=      self.model_tester.num_hidden_layers + 1
												self.assertEqual(len(snake_case     )					,							snake_case     )

												UpperCAmelCase_					:Tuple      			=      self.model_tester.seq_length

												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]     )					,							[seq_length, self.model_tester.hidden_size]					,							)

								UpperCAmelCase_					:Union[str, Any]      			=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
												UpperCAmelCase_					:Dict      			=      True
												check_hidden_states_output(snake_case					,							snake_case					,							snake_case     )

												# check that output_hidden_states also work using config
												del inputs_dict["output_hidden_states"]
												UpperCAmelCase_					:Any      			=      True

												check_hidden_states_output(snake_case					,							snake_case					,							snake_case     )





				@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''     )
				def 				snake_case_ (							self      :  Optional[int]     ):
								pass



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Tuple      			=      hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video''',    filename='''eating_spaghetti_32_frames.npy''',    repo_type='''dataset'''  )
				UpperCAmelCase_					:Any      			=      np.load(__snake_case  )
				return list(__snake_case  )





@require_torch
@require_vision
class  _snake_case				(							unittest.TestCase       ):

				'''simple docstring'''




				@cached_property
				def 				snake_case_ (							self      :  List[Any]     ):
								return VivitImageProcessor() if is_vision_available() else None





				@slow
				def 				snake_case_ (							self      :  List[Any]     ):
								UpperCAmelCase_					:List[str]      			=      VivitForVideoClassification.from_pretrained('''google/vivit-b-16x2-kinetics400'''     ).to(snake_case     )

								UpperCAmelCase_					:str      			=      self.default_image_processor
								UpperCAmelCase_					:List[Any]      			=      prepare_video()
								UpperCAmelCase_					:int      			=      image_processor(snake_case					,							return_tensors='''pt'''     ).to(snake_case     )

								# forward pass
								with torch.no_grad():
												UpperCAmelCase_					:str      			=      model(**snake_case     )

								# verify the logits
								UpperCAmelCase_					:str      			=      torch.Size((1, 400)     )
								self.assertEqual(outputs.logits.shape					,							snake_case     )

								# taken from original model
								UpperCAmelCase_					:List[Any]      			=      torch.tensor([-1.0_543, 2.0_764, -0.2_104, 0.4_439, -0.9_658]     ).to(snake_case     )

								self.assertTrue(torch.allclose(outputs.logits[0, :5]					,							snake_case					,							atol=1e-4     )     )








"""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()
__lowerCamelCase								=						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__lowerCamelCase								=						[]
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 				a (						__snake_case     :				Optional[Any],    __snake_case     :				Any,    __snake_case     :				Tuple  ):



				'''simple docstring'''





				UpperCAmelCase_					:Optional[Any]      			=      state_dict.pop(__snake_case  )
				UpperCAmelCase_					:List[str]      			=      val



def 				a (						__snake_case     :				int  ):



				'''simple docstring'''





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

				return new_state_dict



def 				a (						__snake_case     :				Tuple,    __snake_case     :				Optional[Any]=False  ):



				'''simple docstring'''





				UpperCAmelCase_					:Union[str, Any]      			=      ''''''
				if is_panoptic:
								UpperCAmelCase_					:Tuple      			=      '''detr.'''

				# first: transformer encoder
				for i in range(6  ):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								UpperCAmelCase_					:Optional[Any]      			=      state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					:Tuple      			=      state_dict.pop(f'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'  )
								# next, add query, keys and values (in that order) to the state dict
								UpperCAmelCase_					:List[str]      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Union[str, Any]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					:Optional[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
								UpperCAmelCase_					:List[str]      			=      state_dict.pop(f'{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight'  )
								UpperCAmelCase_					: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
								UpperCAmelCase_					:Dict      			=      in_proj_weight[:256, :]
								UpperCAmelCase_					:Optional[int]      			=      in_proj_bias[:256]
								UpperCAmelCase_					:List[Any]      			=      in_proj_weight[256:512, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[256:512]
								UpperCAmelCase_					:int      			=      in_proj_weight[-256:, :]
								UpperCAmelCase_					:List[str]      			=      in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								UpperCAmelCase_					:int      			=      state_dict.pop(
								    f'{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight'  )
								UpperCAmelCase_					: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
								UpperCAmelCase_					:int      			=      in_proj_weight_cross_attn[:256, :]
								UpperCAmelCase_					:str      			=      in_proj_bias_cross_attn[:256]
								UpperCAmelCase_					:Tuple      			=      in_proj_weight_cross_attn[256:512, :]
								UpperCAmelCase_					:Any      			=      in_proj_bias_cross_attn[256:512]
								UpperCAmelCase_					:str      			=      in_proj_weight_cross_attn[-256:, :]
								UpperCAmelCase_					:List[Any]      			=      in_proj_bias_cross_attn[-256:]



def 				a (						):



				'''simple docstring'''





				UpperCAmelCase_					:Tuple      			=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				UpperCAmelCase_					:List[str]      			=      Image.open(requests.get(__snake_case,    stream=__snake_case  ).raw  )

				return im



@torch.no_grad()
def 				a (						__snake_case     :				Union[str, Any],    __snake_case     :				List[Any]  ):



				'''simple docstring'''





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

				# load image processor
				UpperCAmelCase_					:Dict      			=      '''coco_panoptic''' if is_panoptic else '''coco_detection'''
				UpperCAmelCase_					:Optional[Any]      			=      DetrImageProcessor(format=__snake_case  )

				# prepare image
				UpperCAmelCase_					:Dict      			=      prepare_img()
				UpperCAmelCase_					:Union[str, Any]      			=      image_processor(images=__snake_case,    return_tensors='''pt'''  )
				UpperCAmelCase_					:Optional[Any]      			=      encoding['''pixel_values''']

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

				# load original model from torch hub
				UpperCAmelCase_					:int      			=      torch.hub.load('''facebookresearch/detr''',    __snake_case,    pretrained=__snake_case  ).eval()
				UpperCAmelCase_					:List[Any]      			=      detr.state_dict()
				# rename keys
				for src, dest in rename_keys:
								if is_panoptic:
												UpperCAmelCase_					:Optional[int]      			=      '''detr.''' + src
								rename_key(__snake_case,    __snake_case,    __snake_case  )
				UpperCAmelCase_					:int      			=      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
				UpperCAmelCase_					:Tuple      			=      '''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'''  )
												):
																UpperCAmelCase_					:List[str]      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:List[Any]      			=      val
												elif "class_labels_classifier" in key or "bbox_predictor" in key:
																UpperCAmelCase_					:int      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Union[str, Any]      			=      val
												elif key.startswith('''bbox_attention'''  ) or key.startswith('''mask_head'''  ):
																continue
												else:
																UpperCAmelCase_					:Tuple      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Dict      			=      val
								else:
												if not key.startswith('''class_labels_classifier'''  ) and not key.startswith('''bbox_predictor'''  ):
																UpperCAmelCase_					:Any      			=      state_dict.pop(__snake_case  )
																UpperCAmelCase_					:Any      			=      val
    # finally, create HuggingFace model and load state dict
				UpperCAmelCase_					:Optional[int]      			=      DetrForSegmentation(__snake_case  ) if is_panoptic else DetrForObjectDetection(__snake_case  )
				model.load_state_dict(__snake_case  )
				model.eval()
				# verify our conversion
				UpperCAmelCase_					:List[Any]      			=      detr(__snake_case  )
				UpperCAmelCase_					:Optional[int]      			=      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__":
						__lowerCamelCase								=						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."
						)
						__lowerCamelCase								=						parser.parse_args()
						convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


