


"""simple docstring"""


import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

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


if is_tf_available():
		import tensorflow as tf

		from transformers import (
		    TFEfficientFormerForImageClassification,
		    TFEfficientFormerForImageClassificationWithTeacher,
		    TFEfficientFormerModel,
		)
		from transformers.models.efficientformer.modeling_tf_efficientformer import (
		    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		)


if is_vision_available():
		from PIL import Image

		from transformers import EfficientFormerImageProcessor
class snake_case      :







		def __init__(					self      :       Union[str, Any]    ,   a__      :       str    ,   a__      :       int = 13    ,   a__      :       int = 64    ,   a__      :       int = 2    ,   a__      :       int = 3    ,   a__      :       int = 3    ,   a__      :       bool = True    ,   a__      :       bool = True    ,   a__      :       int = 1_28    ,   a__      :       int=[16, 32, 64, 1_28]    ,   a__      :       int = 7    ,   a__      :       int = 4    ,   a__      :       int = 37    ,   a__      :       str = "gelu"    ,   a__      :       float = 0.1    ,   a__      :       float = 0.1    ,   a__      :       int = 10    ,   a__      :       float = 0.0_2    ,   a__      :       int = 2    ,   a__      :       int = 1    ,   a__      :       int = 1_28    ,   a__      :       List[int] = [2, 2, 2, 2]    ,   a__      :       int = 2    ,   a__      :       int = 2    ,   )       ->						Tuple:





						'''simple docstring'''







						_A					  =			parent
						_A					  =			batch_size
						_A					  =			image_size
						_A					  =			patch_size
						_A					  =			num_channels
						_A					  =			is_training
						_A					  =			use_labels
						_A					  =			hidden_size
						_A					  =			num_hidden_layers
						_A					  =			num_attention_heads
						_A					  =			intermediate_size
						_A					  =			hidden_act
						_A					  =			hidden_dropout_prob
						_A					  =			attention_probs_dropout_prob
						_A					  =			type_sequence_label_size
						_A					  =			initializer_range
						_A					  =			encoder_stride
						_A					  =			num_attention_outputs
						_A					  =			embed_dim
						_A					  =			embed_dim + 1
						_A					  =			resolution
						_A					  =			depths
						_A					  =			hidden_sizes
						_A					  =			dim
						_A					  =			mlp_expansion_ratio







		def  a_ (					self      :       Tuple		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

						_A					  =			None
						if self.use_labels:
										_A					  =			ids_tensor([self.batch_size]    ,   self.type_sequence_label_size		)

						_A					  =			self.get_config()

						return config, pixel_values, labels







		def  a_ (					self      :       Any		)       ->						Optional[int]:





						'''simple docstring'''







						return EfficientFormerConfig(
						    image_size=self.image_size    ,   patch_size=self.patch_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    ,   is_decoder=A_    ,   initializer_range=self.initializer_range    ,   encoder_stride=self.encoder_stride    ,   resolution=self.resolution    ,   depths=self.depths    ,   hidden_sizes=self.hidden_sizes    ,   dim=self.dim    ,   mlp_expansion_ratio=self.mlp_expansion_ratio    ,   )







		def  a_ (					self      :       Tuple    ,   a__      :       int    ,   a__      :       int    ,   a__      :       int		)       ->						Dict:





						'''simple docstring'''







						_A					  =			TFEfficientFormerModel(config=A_		)
						_A					  =			model(A_    ,   training=A_		)
						self.parent.assertEqual(result.last_hidden_state.shape    ,   (self.batch_size, self.seq_length, self.hidden_size)		)







		def  a_ (					self      :       List[str]    ,   a__      :       int    ,   a__      :       List[Any]    ,   a__      :       List[str]		)       ->						Any:





						'''simple docstring'''







						_A					  =			self.type_sequence_label_size
						_A					  =			TFEfficientFormerForImageClassification(A_		)
						_A					  =			model(A_    ,   labels=A_    ,   training=A_		)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)		)

						# test greyscale images
						_A					  =			1
						_A					  =			TFEfficientFormerForImageClassification(A_		)

						_A					  =			floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
						_A					  =			model(A_    ,   labels=A_		)
						self.parent.assertEqual(result.logits.shape    ,   (self.batch_size, self.type_sequence_label_size)		)







		def  a_ (					self      :       List[Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			self.prepare_config_and_inputs()
						_A  ,		_A  ,		_A					  =			config_and_inputs
						_A					  =			{"pixel_values": pixel_values}
						return config, inputs_dict





@require_tf
class snake_case      (  _UpperCAmelCase     ,				_UpperCAmelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			(
		    (
		        TFEfficientFormerModel,
		        TFEfficientFormerForImageClassificationWithTeacher,
		        TFEfficientFormerForImageClassification,
		    )
		    if is_tf_available()
		    else ()
		)
		__UpperCamelCase           =			(
		    {
		        '''feature-extraction''': TFEfficientFormerModel,
		        '''image-classification''': (
		            TFEfficientFormerForImageClassification,
		            TFEfficientFormerForImageClassificationWithTeacher,
		        ),
		    }
		    if is_tf_available()
		    else {}
		)

		__UpperCamelCase           =			False

		__UpperCamelCase           =			False
		__UpperCamelCase           =			False
		__UpperCamelCase           =			False
		__UpperCamelCase           =			False







		def  a_ (					self      :       List[str]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A					  =			TFEfficientFormerModelTester(self		)
						_A					  =			ConfigTester(
						    self    ,   config_class=A_    ,   has_text_modality=A_    ,   hidden_size=37		)







		def  a_ (					self      :       List[Any]		)       ->						Tuple:





						'''simple docstring'''







						self.config_tester.run_common_tests()







		@unittest.skip(reason="EfficientFormer does not use inputs_embeds"		)
		def  a_ (					self      :       int		)       ->						str:





						'''simple docstring'''







						pass







		@unittest.skip(reason="EfficientFormer does not support input and output embeddings"		)
		def  a_ (					self      :       List[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						pass







		def  a_ (					self      :       Tuple		)       ->						List[str]:





						'''simple docstring'''







						_A  ,		_A					  =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										_A					  =			model_class(A_		)
										_A					  =			inspect.signature(model.call		)
										# signature.parameters is an OrderedDict => so arg_names order is deterministic
										_A					  =			[*signature.parameters.keys()]

										_A					  =			["pixel_values"]
										self.assertListEqual(arg_names[:1]    ,   A_		)







		def  a_ (					self      :       Optional[Any]		)       ->						List[str]:





						'''simple docstring'''







						def check_hidden_states_output(a__      :       Dict    ,   a__      :       int    ,   a__      :       List[str]		):
										_A					  =			model_class(A_		)

										_A					  =			model(**self._prepare_for_class(A_    ,   A_		)    ,   training=A_		)
										_A					  =			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										_A					  =			getattr(
										    self.model_tester    ,   "expected_num_hidden_layers"    ,   self.model_tester.num_hidden_layers + 1		)
										self.assertEqual(len(A_		)    ,   A_		)

										if hasattr(self.model_tester    ,   "encoder_seq_length"		):
														_A					  =			self.model_tester.encoder_seq_length
														if hasattr(self.model_tester    ,   "chunk_length"		) and self.model_tester.chunk_length > 1:
																		_A					  =			seq_length * self.model_tester.chunk_length
										else:
														_A					  =			self.model_tester.seq_length

										self.assertListEqual(
										    list(hidden_states[-1].shape[-2:]		)    ,   [seq_length, self.model_tester.hidden_size]    ,   )

										if config.is_encoder_decoder:
														_A					  =			outputs.decoder_hidden_states

														self.asseretIsInstance(A_    ,   (list, tuple)		)
														self.assertEqual(len(A_		)    ,   A_		)
														_A					  =			getattr(self.model_tester    ,   "seq_length"    ,   A_		)
														_A					  =			getattr(self.model_tester    ,   "decoder_seq_length"    ,   A_		)

														self.assertListEqual(
														    list(hidden_states[-1].shape[-2:]		)    ,   [decoder_seq_length, self.model_tester.hidden_size]    ,   )

						_A  ,		_A					  =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										_A					  =			True
										check_hidden_states_output(A_    ,   A_    ,   A_		)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										_A					  =			True

										check_hidden_states_output(A_    ,   A_    ,   A_		)







		def  a_ (					self      :       Union[str, Any]    ,   a__      :       Tuple    ,   a__      :       Optional[int]    ,   a__      :       Dict=False		)       ->						Any:





						'''simple docstring'''







						_A					  =			super()._prepare_for_class(A_    ,   A_    ,   return_labels=A_		)

						if return_labels:
										if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
														del inputs_dict["labels"]

						return inputs_dict







		def  a_ (					self      :       Any		)       ->						int:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*A_		)







		@unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"		)
		def  a_ (					self      :       Any		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_image_modeling(*A_		)







		def  a_ (					self      :       Any		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*A_		)







		@slow
		def  a_ (					self      :       Dict		)       ->						Dict:





						'''simple docstring'''







						for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										_A					  =			TFEfficientFormerModel.from_pretrained(A_		)
										self.assertIsNotNone(A_		)







		def  a_ (					self      :       List[Any]		)       ->						Union[str, Any]:





						'''simple docstring'''







						_A  ,		_A					  =			self.model_tester.prepare_config_and_inputs_for_common()
						_A					  =			True

						_A					  =			getattr(self.model_tester    ,   "seq_length"    ,   A_		)
						_A					  =			getattr(self.model_tester    ,   "encoder_seq_length"    ,   A_		)
						_A					  =			getattr(self.model_tester    ,   "key_length"    ,   A_		)
						_A					  =			getattr(self.model_tester    ,   "chunk_length"    ,   A_		)

						if chunk_length is not None and hasattr(self.model_tester    ,   "num_hashes"		):
										_A					  =			encoder_seq_length * self.model_tester.num_hashes

						for model_class in self.all_model_classes:
										_A					  =			True
										_A					  =			False
										_A					  =			True
										_A					  =			model_class(A_		)

										_A					  =			model(**self._prepare_for_class(A_    ,   A_		)    ,   training=A_		)
										_A					  =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
										self.assertEqual(len(A_		)    ,   self.model_tester.num_attention_outputs		)

										# check that output_attentions also work using config
										del inputs_dict["output_attentions"]
										_A					  =			True
										_A					  =			model_class(A_		)
										_A					  =			model(**self._prepare_for_class(A_    ,   A_		)    ,   training=A_		)

										_A					  =			outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
										self.assertEqual(len(A_		)    ,   self.model_tester.num_attention_outputs		)

										if chunk_length is not None:
														self.assertListEqual(
														    list(attentions[0].shape[-4:]		)    ,   [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]    ,   )
										else:
														self.assertListEqual(
														    list(attentions[0].shape[-3:]		)    ,   [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]    ,   )







		def  a_ (					self      :       Dict		)       ->						int:





						'''simple docstring'''







						_A  ,		_A					  =			self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										# Prepare our model
										_A					  =			model_class(A_		)
										# These are maximally general inputs for the model, with multiple None dimensions
										# Hopefully this will catch any conditionals that fail for flexible shapes
										_A					  =			{
										    key: tf.keras.Input(shape=val.shape[1:]    ,   dtype=val.dtype    ,   name=A_		)
										    for key, val in model.input_signature.items()
										    if key in model.dummy_inputs
										}
										_A					  =			model(A_		)
										self.assertTrue(outputs_dict is not None		)






def      a__ (				)	->	Optional[int]:
				_A					  =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
				return image




@require_tf
@require_vision
class snake_case      (  unittest.TestCase):







		@cached_property
		def  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						return (
						    EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300"		)
						    if is_vision_available()
						    else None
						)







		@slow
		def  a_ (					self      :       Tuple		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"		)
						_A					  =			self.default_image_processor
						_A					  =			prepare_img()
						_A					  =			image_processor(images=A_    ,   return_tensors="tf"		)
						# forward pass
						_A					  =			model(**A_    ,   training=A_		)
						# verify the logits
						_A					  =			tf.TensorShape((1, 10_00)		)
						self.assertEqual(outputs.logits.shape    ,   A_		)
						_A					  =			tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]		)
						self.assertTrue(np.allclose(outputs.logits[0, :3]    ,   A_    ,   atol=1E-4		)		)







		@slow
		def  a_ (					self      :       Any		)       ->						Any:





						'''simple docstring'''







						_A					  =			TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
						    "snap-research/efficientformer-l1-300"		)
						_A					  =			self.default_image_processor
						_A					  =			prepare_img()
						_A					  =			image_processor(images=A_    ,   return_tensors="tf"		)
						# forward pass
						_A					  =			model(**A_    ,   training=A_		)
						# verify the logits
						_A					  =			tf.TensorShape((1, 10_00)		)
						self.assertEqual(outputs.logits.shape    ,   A_		)
						_A					  =			tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]		)
						self.assertTrue(np.allclose(outputs.logits[0, :3]    ,   A_    ,   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 (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
a_       							=    []
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}.cross_attn.out_proj.weight''',
		        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
		    )
		)
		rename_keys.append(
		    (
		        f'''transformer.decoder.layers.{i}.cross_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'''))

		# q, k, v projections in self/cross-attention in decoder for conditional DETR
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
		)

		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
		)
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
		)
		# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
		)
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
		rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
		rename_keys.append(
		    (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
		)

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase   )	->	List[str]:
				_A					  =			state_dict.pop(__lowercase   )
				_A					  =			val






def      a__ (				__lowercase   )	->	List[str]:
				_A					  =			OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												_A					  =			key.replace("backbone.0.body"     ,			"backbone.conv_encoder.model"   )
												_A					  =			value
								else:
												_A					  =			value

				return new_state_dict






def      a__ (				__lowercase     ,			__lowercase=False   )	->	Any:
				_A					  =			""
				if is_panoptic:
								_A					  =			"conditional_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)
								_A					  =			state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""   )
								_A					  =			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
								_A					  =			in_proj_weight[:256, :]
								_A					  =			in_proj_bias[:256]
								_A					  =			in_proj_weight[256:512, :]
								_A					  =			in_proj_bias[256:512]
								_A					  =			in_proj_weight[-256:, :]
								_A					  =			in_proj_bias[-256:]






def      a__ (				)	->	int:
				_A					  =			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_A					  =			Image.open(requests.get(__lowercase     ,			stream=__lowercase   ).raw   )

				return im






@torch.no_grad()
def      a__ (				__lowercase     ,			__lowercase   )	->	Any:
				_A					  =			ConditionalDetrConfig()
				# set backbone and dilation attributes
				if "resnet101" in model_name:
								_A					  =			"resnet101"
				if "dc5" in model_name:
								_A					  =			True
				_A					  =			"panoptic" in model_name
				if is_panoptic:
								_A					  =			250
				else:
								_A					  =			91
								_A					  =			"huggingface/label-files"
								_A					  =			"coco-detection-id2label.json"
								_A					  =			json.load(open(hf_hub_download(__lowercase     ,			__lowercase     ,			repo_type="dataset"   )     ,			"r"   )   )
								_A					  =			{int(__lowercase   ): v for k, v in idalabel.items()}
								_A					  =			idalabel
								_A					  =			{v: k for k, v in idalabel.items()}

				# load image processor
				_A					  =			"coco_panoptic" if is_panoptic else "coco_detection"
				_A					  =			ConditionalDetrImageProcessor(format=__lowercase   )

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

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

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


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

		parser.add_argument(
		    "--model_name",
		    default="conditional_detr_resnet50",
		    type=str,
		    help="Name of the CONDITIONAL_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."
		)
		a_       							=    parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)