





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()
__UpperCamelCase:  Tuple   =   logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   str )      ->       List[Any]:




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

		return orig_key


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Tuple )      ->       Optional[int]:




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


def 			SCREAMING_SNAKE_CASE__     (					)      ->       Tuple:




		'''simple docstring'''
		lowercase__				:			str            =		'http://images.cocodataset.org/val2017/000000039769.jpg'
		lowercase__				:			Optional[int]            =		Image.open(requests.get(_lowercase      ,						stream=_lowercase ).raw )

		return im


@torch.no_grad()
def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Dict      ,						_lowercase    :   Tuple      ,						_lowercase    :   List[Any]      ,						_lowercase    :   Optional[Any]      ,						_lowercase    :   List[str]      ,						_lowercase    :   List[str]      ,						_lowercase    :   Union[str, Any]      ,						)      ->       List[str]:




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

		# load image processor
		lowercase__				:			Tuple            =		DeformableDetrImageProcessor(format='coco_detection' )

		# prepare image
		lowercase__				:			List[Any]            =		prepare_img()
		lowercase__				:			Optional[int]            =		image_processor(images=_lowercase      ,						return_tensors='pt' )
		lowercase__				:			int            =		encoding['pixel_values']

		logger.info('Converting model...' )

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

		lowercase__				:			str            =		'cuda' if torch.cuda.is_available() else 'cpu'
		model.to(_lowercase )
		# verify our conversion
		lowercase__				:			Dict            =		model(pixel_values.to(_lowercase ) )

		lowercase__				:			List[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]] )
		lowercase__				:			str            =		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:
				lowercase__				:			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]] )
				lowercase__				:			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:
				lowercase__				:			Optional[int]            =		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]] )
				lowercase__				:			Dict            =		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:
				lowercase__				:			List[Any]            =		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]] )
				lowercase__				:			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:
				lowercase__				:			Union[str, 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]] )
				lowercase__				:			List[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(_lowercase )      ,						atol=1e-4 )
		assert torch.allclose(outputs.pred_boxes[0, :3, :3]      ,						expected_boxes.to(_lowercase )      ,						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(_lowercase ).mkdir(exist_ok=_lowercase )
		model.save_pretrained(_lowercase )
		image_processor.save_pretrained(_lowercase )

		# Push to hub
		if push_to_hub:
				lowercase__				:			Any            =		'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=_lowercase      ,						organization='nielsr'      ,						commit_message='Add model' )


if __name__ == "__main__":
			__UpperCamelCase:  List[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."""
			)
			__UpperCamelCase:  List[str]   =   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,
			)









import itertools
import random
import unittest

import numpy as np

from transformers import EncodecFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
			import torch


__UpperCamelCase:  int   =   random.Random()


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   int      ,						_lowercase    :   List[Any]=1.0      ,						_lowercase    :   Dict=None      ,						_lowercase    :   List[str]=None )      ->       List[str]:




		'''simple docstring'''
		if rng is None:
				lowercase__				:			int            =		global_rng

		lowercase__				:			Any            =		[]
		for batch_idx in range(shape[0] ):
				values.append([] )
				for _ in range(shape[1] ):
						values[-1].append(rng.random() * scale )

		return values


@require_torch
class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	def __init__(	self:	Union[str, Any],				lowerCamelCase_:	Union[str, Any],				lowerCamelCase_:	int=7,				lowerCamelCase_:	Tuple=400,				lowerCamelCase_:	Union[str, Any]=2000,				lowerCamelCase_:	Optional[int]=1,				lowerCamelCase_:	int=0.0,				lowerCamelCase_:	Any=24000,				lowerCamelCase_:	Dict=True,				):
			lowercase__				:			List[str]            =		parent
			lowercase__				:			Any            =		batch_size
			lowercase__				:			List[str]            =		min_seq_length
			lowercase__				:			Tuple            =		max_seq_length
			lowercase__				:			Optional[Any]            =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowercase__				:			int            =		feature_size
			lowercase__				:			Optional[Any]            =		padding_value
			lowercase__				:			Optional[int]            =		sampling_rate
			lowercase__				:			str            =		return_attention_mask

	def       snake_case__(	self:	str ):
			return {
			    "feature_size": self.feature_size,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			}





	def       snake_case__(	self:	Dict,				lowerCamelCase_:	int=False,				lowerCamelCase_:	Optional[Any]=False ):
			def _flatten(lowerCamelCase_:	int ):
					return list(itertools.chain(*lowerCamelCase_ ) )

			if equal_length:
					lowercase__				:			Tuple            =		floats_list((self.batch_size, self.max_seq_length) )
			else:
					# make sure that inputs increase in size
					lowercase__				:			Dict            =		[
					    _flatten(floats_list((x, self.feature_size) ) )
					    for x in range(self.min_seq_length,				self.max_seq_length,				self.seq_length_diff )
					]

			if numpify:
					lowercase__				:			Any            =		[np.asarray(lowerCamelCase_ ) for x in audio_inputs]

			return audio_inputs


@require_torch
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							EncodecFeatureExtractor

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			List[Any]            =		EnCodecFeatureExtractionTester(self )

	def       snake_case__(	self:	Optional[int] ):
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowercase__				:			List[str]            =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			# create three inputs of length 800, 1000, and 1200
			lowercase__				:			Dict            =		[floats_list((1, x) )[0] for x in range(800,				1400,				200 )]
			lowercase__				:			List[Any]            =		[np.asarray(lowerCamelCase_ ) for audio_input in audio_inputs]

			# Test not batched input
			lowercase__				:			List[str]            =		feat_extract(audio_inputs[0],				return_tensors='np' ).input_values
			lowercase__				:			List[str]            =		feat_extract(np_audio_inputs[0],				return_tensors='np' ).input_values
			self.assertTrue(np.allclose(lowerCamelCase_,				lowerCamelCase_,				atol=1E-3 ) )

			# Test batched
			lowercase__				:			Any            =		feat_extract(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='np' ).input_values
			lowercase__				:			List[str]            =		feat_extract(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='np' ).input_values
			for enc_seq_a, enc_seq_a in zip(lowerCamelCase_,				lowerCamelCase_ ):
					self.assertTrue(np.allclose(lowerCamelCase_,				lowerCamelCase_,				atol=1E-3 ) )

	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			Union[str, Any]            =		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
			lowercase__				:			Optional[int]            =		np.random.rand(100 ).astype(np.floataa )
			lowercase__				:			Optional[int]            =		np_audio_inputs.tolist()

			for inputs in [py_audio_inputs, np_audio_inputs]:
					lowercase__				:			int            =		feature_extractor.pad([{'input_values': inputs}],				return_tensors='np' )
					self.assertTrue(np_processed.input_values.dtype == np.floataa )
					lowercase__				:			int            =		feature_extractor.pad([{'input_values': inputs}],				return_tensors='pt' )
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa )

	def       snake_case__(	self:	Any,				lowerCamelCase_:	Any ):
			from datasets import load_dataset

			lowercase__				:			Dict            =		load_dataset('hf-internal-testing/librispeech_asr_dummy',				'clean',				split='validation' )
			# automatic decoding with librispeech
			lowercase__				:			Union[str, Any]            =		ds.sort('id' ).select(range(lowerCamelCase_ ) )[:num_samples]['audio']

			return [x["array"] for x in audio_samples]

	def       snake_case__(	self:	List[str] ):
			# fmt: off
			lowercase__				:			Optional[int]            =		torch.tensor(
			    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
			     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
			     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
			     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
			     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
			     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
			# fmt: on
			lowercase__				:			Tuple            =		self._load_datasamples(1 )
			lowercase__				:			List[Any]            =		EncodecFeatureExtractor()
			lowercase__				:			Optional[int]            =		feature_extractor(lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(input_values.shape,				(1, 1, 93680) )
			self.assertTrue(torch.allclose(input_values[0, 0, :30],				lowerCamelCase_,				atol=1E-6 ) )

	def       snake_case__(	self:	Dict ):
			# fmt: off
			lowercase__				:			Union[str, Any]            =		torch.tensor(
			    [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
			     3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
			     2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
			     4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
			     7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
			     4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03] )
			# fmt: on
			lowercase__				:			Dict            =		self._load_datasamples(1 )
			lowercase__				:			Tuple            =		[np.tile(input_audio[0][None],				reps=(2, 1) )]
			input_audio[0][1] *= 0.5
			lowercase__				:			Tuple            =		EncodecFeatureExtractor(feature_size=2 )
			lowercase__				:			Dict            =		feature_extractor(lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(input_values.shape,				(1, 2, 93680) )
			self.assertTrue(torch.allclose(input_values[0, 0, :30],				lowerCamelCase_,				atol=1E-6 ) )
			self.assertTrue(torch.allclose(input_values[0, 1, :30],				EXPECTED_INPUT_VALUES * 0.5,				atol=1E-6 ) )





	def       snake_case__(	self:	List[str] ):
			lowercase__				:			List[Any]            =		self._load_datasamples(2 )
			# would be easier if the stride was like
			lowercase__				:			List[str]            =		EncodecFeatureExtractor(feature_size=1,				chunk_length_s=1,				overlap=0.0_1 )

			# pad and trunc raise an error ?
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Both padding and truncation were set. Make sure you only set one.$',				):
					lowercase__				:			Tuple            =		feature_extractor(
					    lowerCamelCase_,				padding='max_length',				truncation=lowerCamelCase_,				return_tensors='pt' ).input_values

			# truncate to chunk
			lowercase__				:			List[str]            =		feature_extractor(lowerCamelCase_,				truncation=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 71520) )  # 2 chunks

			# force truncate to max_length
			lowercase__				:			int            =		feature_extractor(
			    lowerCamelCase_,				truncation=lowerCamelCase_,				max_length=48000,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 48000) )

			# pad to chunk
			lowercase__				:			str            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(padded_outputs.shape,				(2, 1, 95280) )

			# pad to chunk
			lowercase__				:			Tuple            =		feature_extractor(lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 95280) )

			# force pad to max length
			lowercase__				:			Any            =		feature_extractor(
			    lowerCamelCase_,				padding='max_length',				max_length=100000,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(2, 1, 100000) )

			# force no pad
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',				):
					lowercase__				:			List[str]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			List[str]            =		feature_extractor(input_audio[0],				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(1, 1, 93680) )

			# no pad if no chunk_length_s
			lowercase__				:			Dict            =		None
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',				):
					lowercase__				:			List[Any]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			Any            =		feature_extractor(input_audio[0],				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(1, 1, 93680) )

			# no pad if no overlap
			lowercase__				:			str            =		2
			lowercase__				:			Tuple            =		None
			with self.assertRaisesRegex(
			    lowerCamelCase_,				'^Unable to create tensor, you should probably activate padding with \'padding=True\' to have batched tensors with the same length.$',				):
					lowercase__				:			Optional[Any]            =		feature_extractor(lowerCamelCase_,				padding=lowerCamelCase_,				return_tensors='pt' ).input_values

			lowercase__				:			List[str]            =		feature_extractor(input_audio[0],				padding=lowerCamelCase_,				return_tensors='pt' ).input_values
			self.assertEquals(truncated_outputs.shape,				(1, 1, 93680) )


