


from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
		import numpy as np
		import tensorflow as tf

		from transformers import (
		    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaubertConfig,
		    TFFlaubertForMultipleChoice,
		    TFFlaubertForQuestionAnsweringSimple,
		    TFFlaubertForSequenceClassification,
		    TFFlaubertForTokenClassification,
		    TFFlaubertModel,
		    TFFlaubertWithLMHeadModel,
		)


class 					lowerCAmelCase_ :




						def __init__(	self						:   Dict   ,     __A						:   List[Any]   ,     )      ->Tuple:






									"""simple docstring"""






									a__ :Tuple						  =    parent
									a__ :int						  =    13
									a__ :Union[str, Any]						  =    7
									a__ :str						  =    True
									a__ :List[str]						  =    True
									a__ :Union[str, Any]						  =    True
									a__ :List[str]						  =    True
									a__ :List[str]						  =    True
									a__ :Union[str, Any]						  =    False
									a__ :int						  =    False
									a__ :Optional[int]						  =    False
									a__ :List[Any]						  =    2
									a__ :int						  =    99
									a__ :List[str]						  =    0
									a__ :Any						  =    32
									a__ :Union[str, Any]						  =    2
									a__ :Union[str, Any]						  =    4
									a__ :str						  =    0.1
									a__ :str						  =    0.1
									a__ :List[Any]						  =    512
									a__ :int						  =    16
									a__ :Union[str, Any]						  =    2
									a__ :List[Any]						  =    0.02
									a__ :List[str]						  =    3
									a__ :Optional[Any]						  =    4
									a__ :List[Any]						  =    "last"
									a__ :int						  =    True
									a__ :Optional[int]						  =    None
									a__ :List[str]						  =    0




						def  _snake_case					(	self						:   Dict						)      ->List[Any]:






									"""simple docstring"""






									a__ :Optional[Any]						  =    ids_tensor([self.batch_size, self.seq_length]   ,     self.vocab_size						)
									a__ :List[str]						  =    random_attention_mask([self.batch_size, self.seq_length]   ,     dtype=tf.floataa						)

									a__ :Optional[int]						  =    None
									if self.use_input_lengths:
												a__ :str						  =    (
												    ids_tensor([self.batch_size]   ,     vocab_size=2						) + self.seq_length - 2
												)  # small variation of seq_length

									a__ :Dict						  =    None
									if self.use_token_type_ids:
												a__ :int						  =    ids_tensor([self.batch_size, self.seq_length]   ,     self.n_langs						)

									a__ :str						  =    None
									a__ :List[Any]						  =    None
									a__ :Tuple						  =    None
									if self.use_labels:
												a__ :List[str]						  =    ids_tensor([self.batch_size]   ,     self.type_sequence_label_size						)
												a__ :int						  =    ids_tensor([self.batch_size, self.seq_length]   ,     self.num_labels						)
												a__ :Any						  =    ids_tensor([self.batch_size]   ,     2   ,     dtype=tf.floataa						)
												a__ :Tuple						  =    ids_tensor([self.batch_size]   ,     self.num_choices						)

									a__ :List[str]						  =    FlaubertConfig(
									    vocab_size=self.vocab_size   ,     n_special=self.n_special   ,     emb_dim=self.hidden_size   ,     n_layers=self.num_hidden_layers   ,     n_heads=self.num_attention_heads   ,     dropout=self.hidden_dropout_prob   ,     attention_dropout=self.attention_probs_dropout_prob   ,     gelu_activation=self.gelu_activation   ,     sinusoidal_embeddings=self.sinusoidal_embeddings   ,     asm=self.asm   ,     causal=self.causal   ,     n_langs=self.n_langs   ,     max_position_embeddings=self.max_position_embeddings   ,     initializer_range=self.initializer_range   ,     summary_type=self.summary_type   ,     use_proj=self.use_proj   ,     bos_token_id=self.bos_token_id   ,     )

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)




						def  _snake_case					(	self						:   List[str]   ,     __A						:   List[str]   ,     __A						:   int   ,     __A						:   List[Any]   ,     __A						:   Optional[Any]   ,     __A						:   List[Any]   ,     __A						:   str   ,     __A						:   int   ,     __A						:   Any   ,     __A						:   int   ,     )      ->Tuple:






									"""simple docstring"""






									a__ :Optional[int]						  =    TFFlaubertModel(config=__A						)
									a__ :Union[str, Any]						  =    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
									a__ :Optional[Any]						  =    model(__A						)

									a__ :Optional[int]						  =    [input_ids, input_mask]
									a__ :List[Any]						  =    model(__A						)
									self.parent.assertEqual(result.last_hidden_state.shape   ,     (self.batch_size, self.seq_length, self.hidden_size)						)




						def  _snake_case					(	self						:   Tuple   ,     __A						:   List[str]   ,     __A						:   str   ,     __A						:   Dict   ,     __A						:   Dict   ,     __A						:   List[str]   ,     __A						:   Optional[Any]   ,     __A						:   List[Any]   ,     __A						:   Tuple   ,     __A						:   str   ,     )      ->Union[str, Any]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    TFFlaubertWithLMHeadModel(__A						)

									a__ :Dict						  =    {"input_ids": input_ids, "lengths": input_lengths, "langs": token_type_ids}
									a__ :str						  =    model(__A						)

									self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length, self.vocab_size)						)




						def  _snake_case					(	self						:   Dict   ,     __A						:   Any   ,     __A						:   Any   ,     __A						:   Dict   ,     __A						:   Optional[int]   ,     __A						:   Dict   ,     __A						:   List[Any]   ,     __A						:   str   ,     __A						:   Optional[Any]   ,     __A						:   Any   ,     )      ->List[Any]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    TFFlaubertForQuestionAnsweringSimple(__A						)

									a__ :Optional[int]						  =    {"input_ids": input_ids, "lengths": input_lengths}

									a__ :Optional[Any]						  =    model(__A						)

									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  _snake_case					(	self						:   Any   ,     __A						:   Union[str, Any]   ,     __A						:   str   ,     __A						:   Tuple   ,     __A						:   Dict   ,     __A						:   List[str]   ,     __A						:   List[Any]   ,     __A						:   Optional[Any]   ,     __A						:   Optional[Any]   ,     __A						:   List[str]   ,     )      ->int:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    TFFlaubertForSequenceClassification(__A						)

									a__ :List[Any]						  =    {"input_ids": input_ids, "lengths": input_lengths}

									a__ :Dict						  =    model(__A						)

									self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.type_sequence_label_size)						)




						def  _snake_case					(	self						:   Tuple   ,     __A						:   Optional[Any]   ,     __A						:   List[str]   ,     __A						:   Any   ,     __A						:   str   ,     __A						:   Any   ,     __A						:   Tuple   ,     __A						:   Any   ,     __A						:   List[str]   ,     __A						:   int   ,     )      ->Tuple:






									"""simple docstring"""






									a__ :Optional[Any]						  =    self.num_labels
									a__ :Union[str, Any]						  =    TFFlaubertForTokenClassification(config=__A						)
									a__ :str						  =    {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
									a__ :Optional[int]						  =    model(__A						)
									self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.seq_length, self.num_labels)						)




						def  _snake_case					(	self						:   Tuple   ,     __A						:   Optional[int]   ,     __A						:   List[Any]   ,     __A						:   Optional[int]   ,     __A						:   Any   ,     __A						:   Any   ,     __A						:   Dict   ,     __A						:   List[Any]   ,     __A						:   Any   ,     __A						:   Tuple   ,     )      ->Dict:






									"""simple docstring"""






									a__ :int						  =    self.num_choices
									a__ :Union[str, Any]						  =    TFFlaubertForMultipleChoice(config=__A						)
									a__ :Optional[int]						  =    tf.tile(tf.expand_dims(__A   ,     1						)   ,     (1, self.num_choices, 1)						)
									a__ :Optional[Any]						  =    tf.tile(tf.expand_dims(__A   ,     1						)   ,     (1, self.num_choices, 1)						)
									a__ :Tuple						  =    tf.tile(tf.expand_dims(__A   ,     1						)   ,     (1, self.num_choices, 1)						)
									a__ :str						  =    {
									    "input_ids": multiple_choice_inputs_ids,
									    "attention_mask": multiple_choice_input_mask,
									    "token_type_ids": multiple_choice_token_type_ids,
									}
									a__ :Tuple						  =    model(__A						)
									self.parent.assertEqual(result.logits.shape   ,     (self.batch_size, self.num_choices)						)






						def  _snake_case					(	self						:   List[Any]						)      ->int:






									"""simple docstring"""






									a__ :int						  =    self.prepare_config_and_inputs()
									(
									    (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  (
									    a__
									)  ,  
									) :Optional[Any]						  =    config_and_inputs
									a__ :str						  =    {
									    "input_ids": input_ids,
									    "token_type_ids": token_type_ids,
									    "langs": token_type_ids,
									    "lengths": input_lengths,
									}
									return config, inputs_dict






@require_tf
class 					lowerCAmelCase_ (       _a			,_a			,unittest.TestCase):
						lowerCamelCase_         =     (
						    (
						        TFFlaubertModel,
						        TFFlaubertWithLMHeadModel,
						        TFFlaubertForSequenceClassification,
						        TFFlaubertForQuestionAnsweringSimple,
						        TFFlaubertForTokenClassification,
						        TFFlaubertForMultipleChoice,
						    )
						    if is_tf_available()
						    else ()
						)
						lowerCamelCase_         =     (
						    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
						)  # TODO (PVP): Check other models whether language generation is also applicable
						lowerCamelCase_         =     (
						    {
						        'feature-extraction': TFFlaubertModel,
						        'fill-mask': TFFlaubertWithLMHeadModel,
						        'question-answering': TFFlaubertForQuestionAnsweringSimple,
						        'text-classification': TFFlaubertForSequenceClassification,
						        'token-classification': TFFlaubertForTokenClassification,
						        'zero-shot': TFFlaubertForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						lowerCamelCase_         =     False
						lowerCamelCase_         =     False




						def  _snake_case					(	self						:   Union[str, Any]   ,     __A						:   List[str]   ,     __A						:   int   ,     __A						:   Optional[Any]   ,     __A						:   Optional[Any]   ,     __A						:   Any						)      ->List[str]:






									"""simple docstring"""






									if (
									    pipeline_test_casse_name == "QAPipelineTests"
									    and tokenizer_name is not None
									    and not tokenizer_name.endswith("Fast"						)
									):
												# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
												# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
												# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
												return True

									return False




						def  _snake_case					(	self						:   Optional[int]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :int						  =    TFFlaubertModelTester(self						)
									a__ :str						  =    ConfigTester(self   ,     config_class=__A   ,     emb_dim=37						)




						def  _snake_case					(	self						:   Tuple						)      ->int:






									"""simple docstring"""






									self.config_tester.run_common_tests()




						def  _snake_case					(	self						:   List[str]						)      ->Optional[int]:






									"""simple docstring"""






									a__ :List[Any]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*__A						)




						def  _snake_case					(	self						:   Any						)      ->str:






									"""simple docstring"""






									a__ :List[Any]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*__A						)




						def  _snake_case					(	self						:   Dict						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*__A						)




						def  _snake_case					(	self						:   Any						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Optional[int]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*__A						)




						def  _snake_case					(	self						:   str						)      ->str:






									"""simple docstring"""






									a__ :List[str]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_token_classification(*__A						)




						def  _snake_case					(	self						:   Tuple						)      ->Optional[Any]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_multiple_choice(*__A						)






						@slow
						def  _snake_case					(	self						:   Optional[Any]						)      ->Optional[int]:






									"""simple docstring"""






									for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												a__ :str						  =    TFFlaubertModel.from_pretrained(__A						)
												self.assertIsNotNone(__A						)






@require_tf
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase_ (       unittest.TestCase):




						@slow
						def  _snake_case					(	self						:   int						)      ->Optional[int]:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    TFFlaubertModel.from_pretrained("jplu/tf-flaubert-small-cased"						)

									a__ :str						  =    tf.convert_to_tensor(
									    [[0, 158, 735, 2592, 1424, 6727, 82, 1]]   ,     dtype=tf.intaa   ,     )  # "J'aime flaubert !"

									a__ :Dict						  =    model(__A						)[0]
									a__ :List[str]						  =    tf.TensorShape((1, 8, 512)						)
									self.assertEqual(output.shape   ,     __A						)
									# compare the actual values for a slice.
									a__ :str						  =    tf.convert_to_tensor(
									    [
									        [
									            [-1.8_768_773, -1.566_555, 0.27_072_418],
									            [-1.6_920_038, -0.5_873_505, 1.9_329_599],
									            [-2.9_563_985, -1.6_993_835, 1.7_972_052],
									        ]
									    ]   ,     dtype=tf.floataa   ,     )

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()   ,     expected_slice.numpy()   ,     atol=1E-4						)						)






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()
snake_case__			 =						logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
snake_case__			 =						[]
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 				lowerCamelCase__	(      a	:   Any	,  a	:   Any	,  a	:   List[Any]				)    ->	Any:





			"""simple docstring"""
			a__ :Optional[Any]						  =    state_dict.pop(a				)
			a__ :Tuple						  =    val


def 				lowerCamelCase__	(      a	:   List[str]				)    ->	str:





			"""simple docstring"""
			a__ :Any						  =    OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									a__ :List[Any]						  =    key.replace("backbone.0.body"	,  "backbone.conv_encoder.model"				)
									a__ :List[Any]						  =    value
						else:
									a__ :Dict						  =    value

			return new_state_dict


def 				lowerCamelCase__	(      a	:   str	,  a	:   Dict=False				)    ->	List[Any]:





			"""simple docstring"""
			a__ :Optional[int]						  =    ""
			if is_panoptic:
						a__ :str						  =    "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__ :str						  =    state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''				)
						a__ :Optional[int]						  =    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__ :int						  =    in_proj_weight[:256, :]
						a__ :List[str]						  =    in_proj_bias[:256]
						a__ :List[str]						  =    in_proj_weight[256:512, :]
						a__ :Optional[Any]						  =    in_proj_bias[256:512]
						a__ :Dict						  =    in_proj_weight[-256:, :]
						a__ :Tuple						  =    in_proj_bias[-256:]


def 				lowerCamelCase__	(      )    ->	Optional[Any]:





			"""simple docstring"""
			a__ :str						  =    "http://images.cocodataset.org/val2017/000000039769.jpg"
			a__ :Optional[Any]						  =    Image.open(requests.get(a	,  stream=a				).raw				)

			return im


@torch.no_grad()
def 				lowerCamelCase__	(      a	:   Tuple	,  a	:   Union[str, Any]				)    ->	List[Any]:





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

			# load image processor
			a__ :Union[str, Any]						  =    "coco_panoptic" if is_panoptic else "coco_detection"
			a__ :Any						  =    ConditionalDetrImageProcessor(format=a				)

			# prepare image
			a__ :int						  =    prepare_img()
			a__ :Optional[int]						  =    image_processor(images=a	,  return_tensors="pt"				)
			a__ :int						  =    encoding["pixel_values"]

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

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


if __name__ == "__main__":
		snake_case__			 =						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.'''
		)
		snake_case__			 =						parser.parse_args()
		convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)


