
"""simple docstring"""
import inspect
import unittest

from transformers import is_torch_available, is_vision_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
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
			from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import PoolFormerImageProcessor




class  _snake_case	(    _lowercase							):






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    int		     =       self.config_class(**self.inputs_dict     )
												self.parent.assertTrue(hasattr(snake_case__    ,			"""hidden_sizes"""     )     )
												self.parent.assertTrue(hasattr(snake_case__    ,			"""num_encoder_blocks"""     )     )






class  _snake_case	:






							def __init__(       self    :      Union[str, Any]    ,			snake_case__    :      List[Any]    ,			snake_case__    :      Optional[Any]=1_3    ,			snake_case__    :      Optional[Any]=6_4    ,			snake_case__    :      str=3    ,			snake_case__    :      Union[str, Any]=4    ,			snake_case__    :      Dict=[2, 2, 2, 2]    ,			snake_case__    :      Any=[8, 4, 2, 1]    ,			snake_case__    :      Dict=[1_6, 3_2, 6_4, 1_2_8]    ,			snake_case__    :      Any=[1, 4, 8, 1_6]    ,			snake_case__    :      Optional[Any]=False    ,			snake_case__    :      Tuple=True    ,			snake_case__    :      int="gelu"    ,			snake_case__    :      Union[str, Any]=0.1    ,			snake_case__    :      Optional[Any]=0.02    ,			snake_case__    :      str=3    ,			snake_case__    :      Union[str, Any]=None    ,			):






												"""simple docstring"""



												__a:    List[str]		     =       parent
												__a:    Any		     =       batch_size
												__a:    Optional[Any]		     =       image_size
												__a:    Dict		     =       num_channels
												__a:    Optional[Any]		     =       num_encoder_blocks
												__a:    Dict		     =       sr_ratios
												__a:    List[Any]		     =       depths
												__a:    Dict		     =       hidden_sizes
												__a:    int		     =       downsampling_rates
												__a:    Tuple		     =       is_training
												__a:    str		     =       use_labels
												__a:    int		     =       hidden_act
												__a:    Optional[Any]		     =       hidden_dropout_prob
												__a:    Union[str, Any]		     =       initializer_range
												__a:    Union[str, Any]		     =       num_labels
												__a:    str		     =       scope






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    Optional[Any]		     =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

												__a:    Dict		     =       None
												if self.use_labels:
																	__a:    Dict		     =       ids_tensor([self.batch_size, self.image_size, self.image_size]    ,			self.num_labels     )

												__a:    Optional[Any]		     =       PoolFormerConfig(
												    image_size=self.image_size    ,			num_channels=self.num_channels    ,			num_encoder_blocks=self.num_encoder_blocks    ,			depths=self.depths    ,			hidden_sizes=self.hidden_sizes    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			initializer_range=self.initializer_range    ,			)

												return config, pixel_values, labels






							def 				__lowercase	(       self    :      List[Any]    ,			snake_case__    :      Optional[int]    ,			snake_case__    :      Union[str, Any]    ,			snake_case__    :      List[Any]     ):






												"""simple docstring"""



												__a:    List[str]		     =       PoolFormerModel(config=snake_case__     )
												model.to(snake_case__     )
												model.eval()
												__a:    Tuple		     =       model(snake_case__     )
												__a:    Union[str, Any]		     =       self.image_size // 32.0
												self.parent.assertEqual(
												    result.last_hidden_state.shape    ,			(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)     )






							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    List[Any]		     =       self.prepare_config_and_inputs()
												__a     ,					__a     ,					__a:    Dict		     =       config_and_inputs
												__a:    Optional[int]		     =       {"""pixel_values""": pixel_values}
												return config, inputs_dict






@require_torch
class  _snake_case	(    _lowercase			,					_lowercase			,					unittest.TestCase							):
							SCREAMING_SNAKE_CASE		  =    (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
							SCREAMING_SNAKE_CASE		  =    (
							    {'''feature-extraction''': PoolFormerModel, '''image-classification''': PoolFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							SCREAMING_SNAKE_CASE		  =    False
							SCREAMING_SNAKE_CASE		  =    False
							SCREAMING_SNAKE_CASE		  =    False
							SCREAMING_SNAKE_CASE		  =    False
							SCREAMING_SNAKE_CASE		  =    False






							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												__a:    Tuple		     =       PoolFormerModelTester(self     )
												__a:    Optional[int]		     =       PoolFormerConfigTester(self    ,			config_class=snake_case__     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												self.config_tester.run_common_tests()






							def 				__lowercase	(       self    :      Dict     ):






												"""simple docstring"""



												__a:    List[str]		     =       self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*snake_case__     )






							@unittest.skip("""PoolFormer does not use inputs_embeds"""     )
							def 				__lowercase	(       self    :      List[Any]     ):






												"""simple docstring"""



												pass






							@unittest.skip("""PoolFormer does not have get_input_embeddings method and get_output_embeddings methods"""     )
							def 				__lowercase	(       self    :      List[str]     ):






												"""simple docstring"""



												pass






							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												def check_hidden_states_output(snake_case__    :      Optional[Any]    ,			snake_case__    :      Optional[Any]    ,			snake_case__    :      Dict     ):
																	__a:    List[Any]		     =       model_class(snake_case__     )
																	model.to(snake_case__     )
																	model.eval()

																	with torch.no_grad():
																						__a:    Union[str, Any]		     =       model(**self._prepare_for_class(snake_case__    ,			snake_case__     )     )

																	__a:    Dict		     =       outputs.hidden_states

																	__a:    Dict		     =       self.model_tester.num_encoder_blocks
																	self.assertEqual(len(snake_case__     )    ,			snake_case__     )

																	# verify the first hidden states (first block)
																	self.assertListEqual(
																	    list(hidden_states[0].shape[-3:]     )    ,			[
																	        self.model_tester.hidden_sizes[0],
																	        self.model_tester.image_size // 4,
																	        self.model_tester.image_size // 4,
																	    ]    ,			)

												__a     ,					__a:    Tuple		     =       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__a:    str		     =       True
																	check_hidden_states_output(snake_case__    ,			snake_case__    ,			snake_case__     )

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	__a:    Optional[int]		     =       True

																	check_hidden_states_output(snake_case__    ,			snake_case__    ,			snake_case__     )






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												if not self.model_tester.is_training:
																	return

												__a     ,					__a:    Any		     =       self.model_tester.prepare_config_and_inputs_for_common()
												__a:    List[str]		     =       True

												for model_class in self.all_model_classes:
																	if model_class in get_values(snake_case__     ):
																						continue
																	__a:    Any		     =       model_class(snake_case__     )
																	model.to(snake_case__     )
																	model.train()
																	__a:    List[str]		     =       self._prepare_for_class(snake_case__    ,			snake_case__    ,			return_labels=snake_case__     )
																	__a:    Any		     =       model(**snake_case__     ).loss
																	loss.backward()






							def 				__lowercase	(       self    :      int     ):






												"""simple docstring"""



												__a     ,					__a:    str		     =       self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	__a:    List[Any]		     =       model_class(snake_case__     )
																	__a:    Optional[int]		     =       inspect.signature(model.forward     )
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	__a:    Dict		     =       [*signature.parameters.keys()]

																	__a:    Optional[Any]		     =       ["""pixel_values"""]
																	self.assertListEqual(arg_names[:1]    ,			snake_case__     )






							@slow
							def 				__lowercase	(       self    :      Any     ):






												"""simple docstring"""



												for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	__a:    int		     =       PoolFormerModel.from_pretrained(snake_case__     )
																	self.assertIsNotNone(snake_case__     )






def      lowerCamelCase_     (						)  ->      List[Any]:


					'''simple docstring'''
					__a:    Any		     =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""			)
					return image




@require_torch
class  _snake_case	(    unittest.TestCase							):






							@slow
							def 				__lowercase	(       self    :      str     ):






												"""simple docstring"""



												__a:    Tuple		     =       PoolFormerImageProcessor()
												__a:    Dict		     =       PoolFormerForImageClassification.from_pretrained("""sail/poolformer_s12"""     ).to(snake_case__     )

												__a:    List[Any]		     =       image_processor(images=prepare_img()    ,			return_tensors="""pt"""     ).to(snake_case__     )

												# forward pass
												with torch.no_grad():
																	__a:    List[str]		     =       model(**snake_case__     )

												# verify the logits
												__a:    str		     =       torch.Size((1, 1_0_0_0)     )
												self.assertEqual(outputs.logits.shape    ,			snake_case__     )

												__a:    List[Any]		     =       torch.tensor([-0.6_113, 0.1_685, -0.0_492]     ).to(snake_case__     )
												self.assertTrue(torch.allclose(outputs.logits[0, :3]    ,			snake_case__    ,			atol=1e-4     )     )


"""simple docstring"""
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()
lowercase		:    List[str]			=       logging.get_logger(__name__)


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      str:


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

					return orig_key


def      lowerCamelCase_     (						lowerCamelCase_			)  ->      List[str]:


					'''simple docstring'''
					# transformer decoder self-attention layers
					for i in range(6			):
										# read in weights + bias of input projection layer of self-attention
										__a:    Optional[Any]		     =       state_dict.pop(f"""decoder.layers.{i}.self_attn.in_proj_weight"""			)
										__a:    List[str]		     =       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
										__a:    Optional[int]		     =       in_proj_weight[:2_56, :]
										__a:    int		     =       in_proj_bias[:2_56]
										__a:    Tuple		     =       in_proj_weight[2_56:5_12, :]
										__a:    Union[str, Any]		     =       in_proj_bias[2_56:5_12]
										__a:    Optional[int]		     =       in_proj_weight[-2_56:, :]
										__a:    Any		     =       in_proj_bias[-2_56:]


def      lowerCamelCase_     (						)  ->      Union[str, Any]:


					'''simple docstring'''
					__a:    List[Any]		     =       """http://images.cocodataset.org/val2017/000000039769.jpg"""
					__a:    Tuple		     =       Image.open(requests.get(lowerCamelCase_						,					stream=lowerCamelCase_			).raw			)

					return im


@torch.no_grad()
def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					)  ->      Union[str, Any]:


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

					# load image processor
					__a:    Tuple		     =       DeformableDetrImageProcessor(format="""coco_detection"""			)

					# prepare image
					__a:    Tuple		     =       prepare_img()
					__a:    Optional[int]		     =       image_processor(images=lowerCamelCase_						,					return_tensors="""pt"""			)
					__a:    Any		     =       encoding["""pixel_values"""]

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

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

					__a:    List[str]		     =       """cuda""" if torch.cuda.is_available() else """cpu"""
					model.to(lowerCamelCase_			)
					# verify our conversion
					__a:    str		     =       model(pixel_values.to(lowerCamelCase_			)			)

					__a:    Union[str, Any]		     =       torch.tensor(
					    [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]			)
					__a:    Union[str, Any]		     =       torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]			)

					if single_scale:
										__a:    Optional[Any]		     =       torch.tensor(
										    [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]			)
										__a:    Any		     =       torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]			)

					if single_scale and dilation:
										__a:    Dict		     =       torch.tensor(
										    [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]			)
										__a:    int		     =       torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]			)

					if with_box_refine:
										__a:    List[str]		     =       torch.tensor(
										    [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]			)
										__a:    Optional[int]		     =       torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]			)

					if with_box_refine and two_stage:
										__a:    List[Any]		     =       torch.tensor(
										    [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]			)
										__a:    Union[str, Any]		     =       torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]			)

					print("""Logits:"""						,					outputs.logits[0, :3, :3]			)

					assert torch.allclose(outputs.logits[0, :3, :3]						,					expected_logits.to(lowerCamelCase_			)						,					atol=1e-4			)
					assert torch.allclose(outputs.pred_boxes[0, :3, :3]						,					expected_boxes.to(lowerCamelCase_			)						,					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(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
					model.save_pretrained(lowerCamelCase_			)
					image_processor.save_pretrained(lowerCamelCase_			)

					# Push to hub
					if push_to_hub:
										__a:    Dict		     =       """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=lowerCamelCase_						,					organization="""nielsr"""						,					commit_message="""Add model"""			)


if __name__ == "__main__":
			lowercase		:    int			=       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.'
			)
			lowercase		:    Union[str, 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,
			)
