






import unittest

from transformers import ElectraConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_PRETRAINING_MAPPING,
		    ElectraForCausalLM,
		    ElectraForMaskedLM,
		    ElectraForMultipleChoice,
		    ElectraForPreTraining,
		    ElectraForQuestionAnswering,
		    ElectraForSequenceClassification,
		    ElectraForTokenClassification,
		    ElectraModel,
		)
		from transformers.models.electra.modeling_electra import ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST





class  snake_case__      :
	def __init__(       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__=13    ,     lowerCAmelCase__=7    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=True    ,     lowerCAmelCase__=99    ,     lowerCAmelCase__=32    ,     lowerCAmelCase__=5    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__=37    ,     lowerCAmelCase__="gelu"    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=0.1    ,     lowerCAmelCase__=5_12    ,     lowerCAmelCase__=16    ,     lowerCAmelCase__=2    ,     lowerCAmelCase__=0.0_2    ,     lowerCAmelCase__=3    ,     lowerCAmelCase__=4    ,     lowerCAmelCase__=None    ,     )							->   str:
							__magic_name__    :     Optional[int]     		=      parent
							__magic_name__    :     Tuple     		=      batch_size
							__magic_name__    :     Union[str, Any]     		=      seq_length
							__magic_name__    :     List[str]     		=      is_training
							__magic_name__    :     Dict     		=      use_input_mask
							__magic_name__    :     str     		=      use_token_type_ids
							__magic_name__    :     Optional[int]     		=      use_labels
							__magic_name__    :     List[Any]     		=      vocab_size
							__magic_name__    :     Optional[Any]     		=      hidden_size
							__magic_name__    :     Dict     		=      num_hidden_layers
							__magic_name__    :     Union[str, Any]     		=      num_attention_heads
							__magic_name__    :     Union[str, Any]     		=      intermediate_size
							__magic_name__    :     Optional[Any]     		=      hidden_act
							__magic_name__    :     Optional[Any]     		=      hidden_dropout_prob
							__magic_name__    :     str     		=      attention_probs_dropout_prob
							__magic_name__    :     Any     		=      max_position_embeddings
							__magic_name__    :     Tuple     		=      type_vocab_size
							__magic_name__    :     Union[str, Any]     		=      type_sequence_label_size
							__magic_name__    :     List[str]     		=      initializer_range
							__magic_name__    :     Dict     		=      num_labels
							__magic_name__    :     Tuple     		=      num_choices
							__magic_name__    :     int     		=      scope
	def     __magic_name__       (       self				)							->   Any:
							__magic_name__    :     Optional[Any]     		=      ids_tensor([self.batch_size, self.seq_length]    ,     self.vocab_size				)

							__magic_name__    :     List[Any]     		=      None
							if self.use_input_mask:
													__magic_name__    :     Tuple     		=      random_attention_mask([self.batch_size, self.seq_length]				)

							__magic_name__    :     int     		=      None
							if self.use_token_type_ids:
													__magic_name__    :     Union[str, Any]     		=      ids_tensor([self.batch_size, self.seq_length]    ,     self.type_vocab_size				)

							__magic_name__    :     Union[str, Any]     		=      None
							__magic_name__    :     Union[str, Any]     		=      None
							__magic_name__    :     Union[str, Any]     		=      None
							if self.use_labels:
													__magic_name__    :     Optional[int]     		=      ids_tensor([self.batch_size]    ,     self.type_sequence_label_size				)
													__magic_name__    :     Tuple     		=      ids_tensor([self.batch_size, self.seq_length]    ,     self.num_labels				)
													__magic_name__    :     str     		=      ids_tensor([self.batch_size]    ,     self.num_choices				)
													__magic_name__    :     List[Any]     		=      ids_tensor([self.batch_size, self.seq_length]    ,     1				)

							__magic_name__    :     Optional[Any]     		=      self.get_config()

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    fake_token_labels,
							)
	def     __magic_name__       (       self				)							->   Dict:
							return ElectraConfig(
							    vocab_size=self.vocab_size    ,     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    ,     max_position_embeddings=self.max_position_embeddings    ,     type_vocab_size=self.type_vocab_size    ,     is_decoder=lowerCAmelCase__    ,     initializer_range=self.initializer_range    ,     )
	def     __magic_name__       (       self				)							->   List[Any]:
							(
							    __magic_name__
							)    :     int     		=      self.prepare_config_and_inputs()

							__magic_name__    :     Union[str, Any]     		=      True
							__magic_name__    :     Optional[Any]     		=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]				)
							__magic_name__    :     int     		=      ids_tensor([self.batch_size, self.seq_length]    ,     vocab_size=2				)

							return (
							    config,
							    input_ids,
							    token_type_ids,
							    input_mask,
							    sequence_labels,
							    token_labels,
							    choice_labels,
							    encoder_hidden_states,
							    encoder_attention_mask,
							)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Optional[Any]:
							__magic_name__    :     List[str]     		=      ElectraModel(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     List[Any]     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__				)
							__magic_name__    :     List[str]     		=      model(lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__				)
							__magic_name__    :     Union[str, Any]     		=      model(lowerCAmelCase__				)
							self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   int:
							__magic_name__    :     Union[str, Any]     		=      True
							__magic_name__    :     str     		=      ElectraModel(lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Optional[Any]     		=      model(
							    lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     encoder_hidden_states=lowerCAmelCase__    ,     encoder_attention_mask=lowerCAmelCase__    ,     )
							__magic_name__    :     Optional[Any]     		=      model(
							    lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     encoder_hidden_states=lowerCAmelCase__    ,     )
							__magic_name__    :     str     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__				)
							self.parent.assertEqual(result.last_hidden_state.shape    ,     (self.batch_size, self.seq_length, self.hidden_size)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Tuple:
							__magic_name__    :     str     		=      ElectraForMaskedLM(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     int     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__				)
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   List[str]:
							__magic_name__    :     List[str]     		=      ElectraForCausalLM(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Tuple     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__				)
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.vocab_size)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Tuple:
							__magic_name__    :     Dict     		=      self.num_labels
							__magic_name__    :     Tuple     		=      ElectraForTokenClassification(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Optional[int]     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__				)
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length, self.num_labels)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Optional[int]:
							__magic_name__    :     Any     		=      self.num_labels
							__magic_name__    :     int     		=      ElectraForPreTraining(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Optional[int]     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__				)
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.seq_length)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   str:
							__magic_name__    :     List[str]     		=      self.num_labels
							__magic_name__    :     int     		=      ElectraForSequenceClassification(lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     int     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__				)
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_labels)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Union[str, Any]:
							__magic_name__    :     Optional[Any]     		=      ElectraForQuestionAnswering(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     Tuple     		=      model(
							    lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     start_positions=lowerCAmelCase__    ,     end_positions=lowerCAmelCase__    ,     )
							self.parent.assertEqual(result.start_logits.shape    ,     (self.batch_size, self.seq_length)				)
							self.parent.assertEqual(result.end_logits.shape    ,     (self.batch_size, self.seq_length)				)
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     )							->   Dict:
							__magic_name__    :     List[str]     		=      self.num_choices
							__magic_name__    :     int     		=      ElectraForMultipleChoice(config=lowerCAmelCase__				)
							model.to(lowerCAmelCase__				)
							model.eval()
							__magic_name__    :     str     		=      input_ids.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
							__magic_name__    :     Union[str, Any]     		=      token_type_ids.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
							__magic_name__    :     List[Any]     		=      input_mask.unsqueeze(1				).expand(-1    ,     self.num_choices    ,     -1				).contiguous()
							__magic_name__    :     List[Any]     		=      model(
							    lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__    ,     token_type_ids=lowerCAmelCase__    ,     labels=lowerCAmelCase__    ,     )
							self.parent.assertEqual(result.logits.shape    ,     (self.batch_size, self.num_choices)				)







	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     Any     		=      self.prepare_config_and_inputs()
							(
							    __magic_name__
							)    :     Optional[int]     		=      config_and_inputs
							__magic_name__    :     Optional[Any]     		=      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
							return config, inputs_dict





@require_torch
class  snake_case__      (							_lowerCAmelCase		,						_lowerCAmelCase		,						unittest.TestCase   ):
	lowercase__		:  Any							   =					(
	    (
	        ElectraModel,
	        ElectraForPreTraining,
	        ElectraForMaskedLM,
	        ElectraForCausalLM,
	        ElectraForMultipleChoice,
	        ElectraForTokenClassification,
	        ElectraForSequenceClassification,
	        ElectraForQuestionAnswering,
	    )
	    if is_torch_available()
	    else ()
	)
	lowercase__		:  Optional[int]							   =					(
	    {
	        '''feature-extraction''': ElectraModel,
	        '''fill-mask''': ElectraForMaskedLM,
	        '''question-answering''': ElectraForQuestionAnswering,
	        '''text-classification''': ElectraForSequenceClassification,
	        '''text-generation''': ElectraForCausalLM,
	        '''token-classification''': ElectraForTokenClassification,
	        '''zero-shot''': ElectraForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)
	lowercase__		:  int							   =					True
	def     __magic_name__       (       self    ,     lowerCAmelCase__    ,     lowerCAmelCase__    ,     lowerCAmelCase__=False				)							->   Tuple:
							__magic_name__    :     int     		=      super()._prepare_for_class(lowerCAmelCase__    ,     lowerCAmelCase__    ,     return_labels=lowerCAmelCase__				)

							if return_labels:
													if model_class in get_values(lowerCAmelCase__				):
																			__magic_name__    :     Dict     		=      torch.zeros(
																			    (self.model_tester.batch_size, self.model_tester.seq_length)    ,     dtype=torch.long    ,     device=lowerCAmelCase__				)
							return inputs_dict
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     str     		=      ElectraModelTester(self				)
							__magic_name__    :     List[str]     		=      ConfigTester(self    ,     config_class=lowerCAmelCase__    ,     hidden_size=37				)
	def     __magic_name__       (       self				)							->   List[Any]:
							self.config_tester.run_common_tests()
	def     __magic_name__       (       self				)							->   Any:
							__magic_name__    :     Tuple     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_model(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   List[str]:
							__magic_name__    :     List[str]     		=      self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_electra_model_as_decoder(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     Optional[Any]     		=      self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
													__magic_name__    :     Dict     		=      type
													self.model_tester.create_and_check_electra_model(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Tuple:
							__magic_name__    :     List[Any]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_masked_lm(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     List[str]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_token_classification(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_pretraining(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     Dict     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_sequence_classification(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Union[str, Any]:
							__magic_name__    :     Union[str, Any]     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_question_answering(*lowerCAmelCase__				)
	def     __magic_name__       (       self				)							->   Optional[int]:
							__magic_name__    :     str     		=      self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_electra_for_multiple_choice(*lowerCAmelCase__				)
	@slow
	def     __magic_name__       (       self				)							->   List[str]:
							for model_name in ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													__magic_name__    :     List[str]     		=      ElectraModel.from_pretrained(lowerCAmelCase__				)
													self.assertIsNotNone(lowerCAmelCase__				)







	def     __magic_name__       (       self				)							->   int:
							__magic_name__    :     str     		=      self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_electra_for_causal_lm(*lowerCAmelCase__				)





@require_torch
class  snake_case__      (							unittest.TestCase   ):
	@slow
	def     __magic_name__       (       self				)							->   Dict:
							__magic_name__    :     int     		=      ElectraModel.from_pretrained("""google/electra-small-discriminator"""				)
							__magic_name__    :     Tuple     		=      torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]]				)
							__magic_name__    :     List[Any]     		=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]				)
							__magic_name__    :     Any     		=      model(lowerCAmelCase__    ,     attention_mask=lowerCAmelCase__				)[0]
							__magic_name__    :     Tuple     		=      torch.Size((1, 11, 2_56)				)
							self.assertEqual(output.shape    ,     lowerCAmelCase__				)
							__magic_name__    :     Optional[int]     		=      torch.tensor(
							    [[[0.4_4_7_1, 0.6_8_2_1, -0.3_2_6_5], [0.4_6_2_7, 0.5_2_5_5, -0.3_6_6_8], [0.4_5_3_2, 0.3_3_1_3, -0.4_3_4_4]]]				)

							self.assertTrue(torch.allclose(output[:, 1:4, 1:4]    ,     lowerCAmelCase__    ,     atol=1e-4				)				)








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()
__magic_name__:  List[Any]  	=			logging.get_logger(__name__)

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						if "backbone.0.body" in orig_key:
												__magic_name__    :     Optional[int]     		=      orig_key.replace("""backbone.0.body""",					"""backbone.conv_encoder.model"""				)
						if "transformer" in orig_key:
												__magic_name__    :     Tuple     		=      orig_key.replace("""transformer.""",					""""""				)
						if "norm1" in orig_key:
												if "encoder" in orig_key:
																		__magic_name__    :     int     		=      orig_key.replace("""norm1""",					"""self_attn_layer_norm"""				)
												else:
																		__magic_name__    :     List[str]     		=      orig_key.replace("""norm1""",					"""encoder_attn_layer_norm"""				)
						if "norm2" in orig_key:
												if "encoder" in orig_key:
																		__magic_name__    :     str     		=      orig_key.replace("""norm2""",					"""final_layer_norm"""				)
												else:
																		__magic_name__    :     Optional[int]     		=      orig_key.replace("""norm2""",					"""self_attn_layer_norm"""				)
						if "norm3" in orig_key:
												__magic_name__    :     Optional[int]     		=      orig_key.replace("""norm3""",					"""final_layer_norm"""				)
						if "linear1" in orig_key:
												__magic_name__    :     Dict     		=      orig_key.replace("""linear1""",					"""fc1"""				)
						if "linear2" in orig_key:
												__magic_name__    :     Optional[int]     		=      orig_key.replace("""linear2""",					"""fc2"""				)
						if "query_embed" in orig_key:
												__magic_name__    :     Optional[Any]     		=      orig_key.replace("""query_embed""",					"""query_position_embeddings"""				)
						if "cross_attn" in orig_key:
												__magic_name__    :     str     		=      orig_key.replace("""cross_attn""",					"""encoder_attn"""				)

						return orig_key

def 					UpperCamelCase		( _A				):






						"""simple docstring"""







						for i in range(6				):
												# read in weights + bias of input projection layer of self-attention
												__magic_name__    :     Tuple     		=      state_dict.pop(f'decoder.layers.{i}.self_attn.in_proj_weight'				)
												__magic_name__    :     Dict     		=      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
												__magic_name__    :     Tuple     		=      in_proj_weight[:256, :]
												__magic_name__    :     Union[str, Any]     		=      in_proj_bias[:256]
												__magic_name__    :     Union[str, Any]     		=      in_proj_weight[256:512, :]
												__magic_name__    :     List[Any]     		=      in_proj_bias[256:512]
												__magic_name__    :     Optional[int]     		=      in_proj_weight[-256:, :]
												__magic_name__    :     Dict     		=      in_proj_bias[-256:]

def 					UpperCamelCase		( ):






						"""simple docstring"""







						__magic_name__    :     Union[str, Any]     		=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
						__magic_name__    :     List[Any]     		=      Image.open(requests.get(_A,					stream=_A				).raw				)

						return im

@torch.no_grad()
def 					UpperCamelCase		( _A,					_A,					_A,					_A,					_A,					_A,					_A,					):






						"""simple docstring"""







						__magic_name__    :     Tuple     		=      DeformableDetrConfig()
						# set config attributes
						if single_scale:
												__magic_name__    :     Union[str, Any]     		=      1
						__magic_name__    :     Optional[int]     		=      dilation
						__magic_name__    :     Any     		=      with_box_refine
						__magic_name__    :     Dict     		=      two_stage
						# set labels
						__magic_name__    :     Union[str, Any]     		=      91
						__magic_name__    :     Optional[Any]     		=      """huggingface/label-files"""
						__magic_name__    :     List[str]     		=      """coco-detection-id2label.json"""
						__magic_name__    :     str     		=      json.load(open(cached_download(hf_hub_url(_A,					_A,					repo_type="""dataset"""				)				),					"""r"""				)				)
						__magic_name__    :     Optional[int]     		=      {int(_A				): v for k, v in idalabel.items()}
						__magic_name__    :     Optional[Any]     		=      idalabel
						__magic_name__    :     Optional[int]     		=      {v: k for k, v in idalabel.items()}

						# load image processor
						__magic_name__    :     Any     		=      DeformableDetrImageProcessor(format="""coco_detection"""				)

						# prepare image
						__magic_name__    :     str     		=      prepare_img()
						__magic_name__    :     List[str]     		=      image_processor(images=_A,					return_tensors="""pt"""				)
						__magic_name__    :     List[Any]     		=      encoding["""pixel_values"""]

						logger.info("""Converting model..."""				)

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

						__magic_name__    :     Optional[int]     		=      """cuda""" if torch.cuda.is_available() else """cpu"""
						model.to(_A				)
						# verify our conversion
						__magic_name__    :     List[Any]     		=      model(pixel_values.to(_A				)				)

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

						if single_scale:
												__magic_name__    :     Any     		=      torch.tensor(
												    [[-9.9051, -4.2541, -6.4852], [-9.6947, -4.0854, -6.8033], [-10.0665, -5.8470, -7.7003]]				)
												__magic_name__    :     Optional[Any]     		=      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:
												__magic_name__    :     Dict     		=      torch.tensor(
												    [[-8.9652, -4.1074, -5.6635], [-9.0596, -4.9447, -6.6075], [-10.1178, -4.5275, -6.2671]]				)
												__magic_name__    :     List[Any]     		=      torch.tensor([[0.7665, 0.4130, 0.4769], [0.8364, 0.1841, 0.3391], [0.6261, 0.3895, 0.7978]]				)

						if with_box_refine:
												__magic_name__    :     Optional[Any]     		=      torch.tensor(
												    [[-8.8895, -5.4187, -6.8153], [-8.4706, -6.1668, -7.6184], [-9.0042, -5.5359, -6.9141]]				)
												__magic_name__    :     List[str]     		=      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:
												__magic_name__    :     Union[str, Any]     		=      torch.tensor(
												    [[-6.7108, -4.3213, -6.3777], [-8.9014, -6.1799, -6.7240], [-6.9315, -4.4735, -6.2298]]				)
												__magic_name__    :     List[str]     		=      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(_A				),					atol=1e-4				)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3],					expected_boxes.to(_A				),					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(_A				).mkdir(exist_ok=_A				)
						model.save_pretrained(_A				)
						image_processor.save_pretrained(_A				)

						# Push to hub
						if push_to_hub:
												__magic_name__    :     Tuple     		=      """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=_A,					organization="""nielsr""",					commit_message="""Add model"""				)


if __name__ == "__main__":
		__magic_name__:  str  	=			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."
		)
		__magic_name__:  Any  	=			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,
		)
