






"""simple docstring"""



import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
						import torch

if is_datasets_available():
						from datasets import load_dataset

A_      =							random.Random()




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Tuple,  UpperCAmelCase__    :   Any=1.0,  UpperCAmelCase__    :   Any=None,  UpperCAmelCase__    :   int=None )						->Optional[int]:
						if rng is None:
												A__  :					Union[str, Any]											=					global_rng

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

						return values






class 							__SCREAMING_SNAKE_CASE  (			unittest.TestCase						):


							def __init__(     self :  Union[str, Any]     ,      snake_case :  List[Any]     ,      snake_case :  List[Any]=7     ,      snake_case :  Dict=400     ,      snake_case :  Union[str, Any]=2000     ,      snake_case :  List[str]=2048     ,      snake_case :  Optional[Any]=128     ,      snake_case :  Optional[int]=1     ,      snake_case :  str=512     ,      snake_case :  Dict=30     ,      snake_case :  List[Any]=4_4100     ,      ):
													'''simple docstring'''





													A__  :					int											=					parent
													A__  :					Optional[int]											=					batch_size
													A__  :					Dict											=					min_seq_length
													A__  :					Any											=					max_seq_length
													A__  :					Union[str, Any]											=					(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
													A__  :					List[Any]											=					spectrogram_length
													A__  :					Tuple											=					feature_size
													A__  :					int											=					num_audio_channels
													A__  :					Union[str, Any]											=					hop_length
													A__  :					Union[str, Any]											=					chunk_length
													A__  :					List[Any]											=					sampling_rate


							def   _UpperCamelCase    (     self :  str							):
													'''simple docstring'''





													return {
													    "spectrogram_length": self.spectrogram_length,
													    "feature_size": self.feature_size,
													    "num_audio_channels": self.num_audio_channels,
													    "hop_length": self.hop_length,
													    "chunk_length": self.chunk_length,
													    "sampling_rate": self.sampling_rate,
													}


							def   _UpperCamelCase    (     self :  Optional[int]     ,      snake_case :  Optional[int]=False     ,      snake_case :  List[str]=False							):
													'''simple docstring'''





													def _flatten(snake_case :  Dict							):
																			return list(itertools.chain(*snake_case							)							)

													if equal_length:
																			A__  :					Optional[Any]											=					[floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
													else:
																			# make sure that inputs increase in size
																			A__  :					str											=					[
																			    floats_list((x, self.feature_size)							)
																			    for x in range(self.min_seq_length     ,      self.max_seq_length     ,      self.seq_length_diff							)
																			]
													if numpify:
																			A__  :					Dict											=					[np.asarray(snake_case							) for x in speech_inputs]
													return speech_inputs






@require_torch
@require_torchaudio
class 							__SCREAMING_SNAKE_CASE  (			UpperCamelCase    ,						unittest.TestCase						):
							snake_case_       =							TvltFeatureExtractor


							def   _UpperCamelCase    (     self :  Dict							):
													'''simple docstring'''





													A__  :					Tuple											=					TvltFeatureExtractionTester(self							)


							def   _UpperCamelCase    (     self :  List[Any]							):
													'''simple docstring'''





													A__  :					List[str]											=					self.feature_extraction_class(**self.feat_extract_dict							)
													self.assertTrue(hasattr(snake_case     ,      """spectrogram_length"""							)							)
													self.assertTrue(hasattr(snake_case     ,      """feature_size"""							)							)
													self.assertTrue(hasattr(snake_case     ,      """num_audio_channels"""							)							)
													self.assertTrue(hasattr(snake_case     ,      """hop_length"""							)							)
													self.assertTrue(hasattr(snake_case     ,      """chunk_length"""							)							)
													self.assertTrue(hasattr(snake_case     ,      """sampling_rate"""							)							)


							def   _UpperCamelCase    (     self :  int							):
													'''simple docstring'''





													A__  :					Any											=					self.feature_extraction_class(**self.feat_extract_dict							)

													with tempfile.TemporaryDirectory() as tmpdirname:
																			A__  :					str											=					feat_extract_first.save_pretrained(snake_case							)[0]
																			check_json_file_has_correct_format(snake_case							)
																			A__  :					str											=					self.feature_extraction_class.from_pretrained(snake_case							)

													A__  :					Union[str, Any]											=					feat_extract_first.to_dict()
													A__  :					Union[str, Any]											=					feat_extract_second.to_dict()
													A__  :					List[str]											=					dict_first.pop("""mel_filters"""							)
													A__  :					Optional[int]											=					dict_second.pop("""mel_filters"""							)
													self.assertTrue(np.allclose(snake_case     ,      snake_case							)							)
													self.assertEqual(snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__  :					str											=					self.feature_extraction_class(**self.feat_extract_dict							)

													with tempfile.TemporaryDirectory() as tmpdirname:
																			A__  :					List[Any]											=					os.path.join(snake_case     ,      """feat_extract.json"""							)
																			feat_extract_first.to_json_file(snake_case							)
																			A__  :					Any											=					self.feature_extraction_class.from_json_file(snake_case							)

													A__  :					List[str]											=					feat_extract_first.to_dict()
													A__  :					Optional[int]											=					feat_extract_second.to_dict()
													A__  :					List[str]											=					dict_first.pop("""mel_filters"""							)
													A__  :					Union[str, Any]											=					dict_second.pop("""mel_filters"""							)
													self.assertTrue(np.allclose(snake_case     ,      snake_case							)							)
													self.assertEqual(snake_case     ,      snake_case							)


							def   _UpperCamelCase    (     self :  Optional[int]							):
													'''simple docstring'''





													A__  :					Any											=					self.feature_extraction_class(**self.feat_extract_dict							)

													# create three inputs of length 800, 1000, and 1200
													A__  :					Optional[int]											=					[floats_list((1, x)							)[0] for x in range(800     ,      1400     ,      200							)]
													A__  :					List[Any]											=					[np.asarray(snake_case							) for speech_input in speech_inputs]

													# Test not batched input
													A__  :					Tuple											=					feature_extractor(np_speech_inputs[0]     ,      return_tensors="""np"""     ,      sampling_rate=4_4100							).audio_values

													self.assertTrue(encoded_audios.ndim == 4							)
													self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
													self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
													self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

													# Test batched
													A__  :					Union[str, Any]											=					feature_extractor(snake_case     ,      return_tensors="""np"""     ,      sampling_rate=4_4100							).audio_values

													self.assertTrue(encoded_audios.ndim == 4							)
													self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
													self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
													self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

													# Test audio masking
													A__  :					Any											=					feature_extractor(
													    snake_case     ,      return_tensors="""np"""     ,      sampling_rate=4_4100     ,      mask_audio=snake_case							).audio_values

													self.assertTrue(encoded_audios.ndim == 4							)
													self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
													self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
													self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)

													# Test 2-D numpy arrays are batched.
													A__  :					Union[str, Any]											=					[floats_list((1, x)							)[0] for x in (800, 800, 800)]
													A__  :					Dict											=					np.asarray(snake_case							)
													A__  :					Optional[int]											=					feature_extractor(snake_case     ,      return_tensors="""np"""     ,      sampling_rate=4_4100							).audio_values
													self.assertTrue(encoded_audios.ndim == 4							)
													self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size							)
													self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length							)
													self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels							)


							def   _UpperCamelCase    (     self :  List[str]     ,      snake_case :  Optional[int]							):
													'''simple docstring'''





													A__  :					Dict											=					load_dataset("""hf-internal-testing/librispeech_asr_dummy"""     ,      """clean"""     ,      split="""validation"""							)
													# automatic decoding with librispeech
													A__  :					int											=					ds.sort("""id"""							).select(range(snake_case							)							)[:num_samples]["""audio"""]

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


							def   _UpperCamelCase    (     self :  List[str]							):
													'''simple docstring'''





													A__  :					Tuple											=					self._load_datasamples(1							)
													A__  :					str											=					TvltFeatureExtractor()
													A__  :					Dict											=					feature_extractor(snake_case     ,      return_tensors="""pt"""							).audio_values

													self.assertEquals(audio_values.shape     ,      (1, 1, 192, 128)							)

													A__  :					Optional[Any]											=					torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]							)
													self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]     ,      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 DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


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




def        _lowerCAmelCase						(							UpperCAmelCase__    :   List[Any] )						->List[str]:
						A__  :					Union[str, Any]											=					DPTConfig()

						if "large" in checkpoint_url:
												A__  :					int											=					1_0_2_4
												A__  :					Union[str, Any]											=					4_0_9_6
												A__  :					Optional[int]											=					2_4
												A__  :					int											=					1_6
												A__  :					Union[str, Any]											=					[5, 1_1, 1_7, 2_3]
												A__  :					Tuple											=					[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
												A__  :					Tuple											=					(1, 3_8_4, 3_8_4)

						if "ade" in checkpoint_url:
												A__  :					Optional[int]											=					True

												A__  :					int											=					1_5_0
												A__  :					Union[str, Any]											=					"""huggingface/label-files"""
												A__  :					List[Any]											=					"""ade20k-id2label.json"""
												A__  :					Union[str, Any]											=					json.load(open(cached_download(hf_hub_url(UpperCAmelCase__,  UpperCAmelCase__,  repo_type="""dataset""" ) ),  """r""" ) )
												A__  :					List[Any]											=					{int(UpperCAmelCase__ ): v for k, v in idalabel.items()}
												A__  :					Dict											=					idalabel
												A__  :					List[Any]											=					{v: k for k, v in idalabel.items()}
												A__  :					Optional[Any]											=					[1, 1_5_0, 4_8_0, 4_8_0]

						return config, expected_shape




def        _lowerCAmelCase						(							UpperCAmelCase__    :   int )						->Any:
						A__  :					List[Any]											=					["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
						for k in ignore_keys:
												state_dict.pop(UpperCAmelCase__,  UpperCAmelCase__ )




def        _lowerCAmelCase						(							UpperCAmelCase__    :   Union[str, Any] )						->List[str]:
						if (
						    "pretrained.model" in name
						    and "cls_token" not in name
						    and "pos_embed" not in name
						    and "patch_embed" not in name
						):
												A__  :					str											=					name.replace("""pretrained.model""",  """dpt.encoder""" )
						if "pretrained.model" in name:
												A__  :					Dict											=					name.replace("""pretrained.model""",  """dpt.embeddings""" )
						if "patch_embed" in name:
												A__  :					List[Any]											=					name.replace("""patch_embed""",  """patch_embeddings""" )
						if "pos_embed" in name:
												A__  :					int											=					name.replace("""pos_embed""",  """position_embeddings""" )
						if "attn.proj" in name:
												A__  :					Tuple											=					name.replace("""attn.proj""",  """attention.output.dense""" )
						if "proj" in name and "project" not in name:
												A__  :					List[Any]											=					name.replace("""proj""",  """projection""" )
						if "blocks" in name:
												A__  :					Optional[Any]											=					name.replace("""blocks""",  """layer""" )
						if "mlp.fc1" in name:
												A__  :					int											=					name.replace("""mlp.fc1""",  """intermediate.dense""" )
						if "mlp.fc2" in name:
												A__  :					List[str]											=					name.replace("""mlp.fc2""",  """output.dense""" )
						if "norm1" in name:
												A__  :					Any											=					name.replace("""norm1""",  """layernorm_before""" )
						if "norm2" in name:
												A__  :					List[str]											=					name.replace("""norm2""",  """layernorm_after""" )
						if "scratch.output_conv" in name:
												A__  :					Optional[int]											=					name.replace("""scratch.output_conv""",  """head""" )
						if "scratch" in name:
												A__  :					List[str]											=					name.replace("""scratch""",  """neck""" )
						if "layer1_rn" in name:
												A__  :					List[str]											=					name.replace("""layer1_rn""",  """convs.0""" )
						if "layer2_rn" in name:
												A__  :					Optional[int]											=					name.replace("""layer2_rn""",  """convs.1""" )
						if "layer3_rn" in name:
												A__  :					Any											=					name.replace("""layer3_rn""",  """convs.2""" )
						if "layer4_rn" in name:
												A__  :					Any											=					name.replace("""layer4_rn""",  """convs.3""" )
						if "refinenet" in name:
												A__  :					Union[str, Any]											=					int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
												# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
												A__  :					str											=					name.replace(f'refinenet{layer_idx}',  f'fusion_stage.layers.{abs(layer_idx-4 )}' )
						if "out_conv" in name:
												A__  :					Optional[Any]											=					name.replace("""out_conv""",  """projection""" )
						if "resConfUnit1" in name:
												A__  :					List[Any]											=					name.replace("""resConfUnit1""",  """residual_layer1""" )
						if "resConfUnit2" in name:
												A__  :					Tuple											=					name.replace("""resConfUnit2""",  """residual_layer2""" )
						if "conv1" in name:
												A__  :					Tuple											=					name.replace("""conv1""",  """convolution1""" )
						if "conv2" in name:
												A__  :					List[Any]											=					name.replace("""conv2""",  """convolution2""" )
						# readout blocks
						if "pretrained.act_postprocess1.0.project.0" in name:
												A__  :					Union[str, Any]											=					name.replace("""pretrained.act_postprocess1.0.project.0""",  """neck.reassemble_stage.readout_projects.0.0""" )
						if "pretrained.act_postprocess2.0.project.0" in name:
												A__  :					Tuple											=					name.replace("""pretrained.act_postprocess2.0.project.0""",  """neck.reassemble_stage.readout_projects.1.0""" )
						if "pretrained.act_postprocess3.0.project.0" in name:
												A__  :					Optional[Any]											=					name.replace("""pretrained.act_postprocess3.0.project.0""",  """neck.reassemble_stage.readout_projects.2.0""" )
						if "pretrained.act_postprocess4.0.project.0" in name:
												A__  :					Optional[Any]											=					name.replace("""pretrained.act_postprocess4.0.project.0""",  """neck.reassemble_stage.readout_projects.3.0""" )
						# resize blocks
						if "pretrained.act_postprocess1.3" in name:
												A__  :					Any											=					name.replace("""pretrained.act_postprocess1.3""",  """neck.reassemble_stage.layers.0.projection""" )
						if "pretrained.act_postprocess1.4" in name:
												A__  :					List[Any]											=					name.replace("""pretrained.act_postprocess1.4""",  """neck.reassemble_stage.layers.0.resize""" )
						if "pretrained.act_postprocess2.3" in name:
												A__  :					Dict											=					name.replace("""pretrained.act_postprocess2.3""",  """neck.reassemble_stage.layers.1.projection""" )
						if "pretrained.act_postprocess2.4" in name:
												A__  :					Optional[Any]											=					name.replace("""pretrained.act_postprocess2.4""",  """neck.reassemble_stage.layers.1.resize""" )
						if "pretrained.act_postprocess3.3" in name:
												A__  :					Union[str, Any]											=					name.replace("""pretrained.act_postprocess3.3""",  """neck.reassemble_stage.layers.2.projection""" )
						if "pretrained.act_postprocess4.3" in name:
												A__  :					Optional[int]											=					name.replace("""pretrained.act_postprocess4.3""",  """neck.reassemble_stage.layers.3.projection""" )
						if "pretrained.act_postprocess4.4" in name:
												A__  :					Dict											=					name.replace("""pretrained.act_postprocess4.4""",  """neck.reassemble_stage.layers.3.resize""" )
						if "pretrained" in name:
												A__  :					Union[str, Any]											=					name.replace("""pretrained""",  """dpt""" )
						if "bn" in name:
												A__  :					Union[str, Any]											=					name.replace("""bn""",  """batch_norm""" )
						if "head" in name:
												A__  :					Dict											=					name.replace("""head""",  """head.head""" )
						if "encoder.norm" in name:
												A__  :					Optional[int]											=					name.replace("""encoder.norm""",  """layernorm""" )
						if "auxlayer" in name:
												A__  :					List[str]											=					name.replace("""auxlayer""",  """auxiliary_head.head""" )

						return name




def        _lowerCAmelCase						(							UpperCAmelCase__    :   int,  UpperCAmelCase__    :   Dict )						->str:
						for i in range(config.num_hidden_layers ):
												# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
												A__  :					Any											=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight' )
												A__  :					Tuple											=					state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias' )
												# next, add query, keys and values (in that order) to the state dict
												A__  :					List[str]											=					in_proj_weight[: config.hidden_size, :]
												A__  :					int											=					in_proj_bias[: config.hidden_size]
												A__  :					Tuple											=					in_proj_weight[
												    config.hidden_size : config.hidden_size * 2, :
												]
												A__  :					Any											=					in_proj_bias[
												    config.hidden_size : config.hidden_size * 2
												]
												A__  :					str											=					in_proj_weight[
												    -config.hidden_size :, :
												]
												A__  :					Optional[Any]											=					in_proj_bias[-config.hidden_size :]




def        _lowerCAmelCase						(							)						->List[str]:
						A__  :					int											=					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
						A__  :					int											=					Image.open(requests.get(UpperCAmelCase__,  stream=UpperCAmelCase__ ).raw )
						return im




@torch.no_grad()
def        _lowerCAmelCase						(							UpperCAmelCase__    :   int,  UpperCAmelCase__    :   Optional[int],  UpperCAmelCase__    :   str,  UpperCAmelCase__    :   int )						->str:
						A__					,					A__  :					Dict											=					get_dpt_config(UpperCAmelCase__ )
						# load original state_dict from URL
						A__  :					Any											=					torch.hub.load_state_dict_from_url(UpperCAmelCase__,  map_location="""cpu""" )
						# remove certain keys
						remove_ignore_keys_(UpperCAmelCase__ )
						# rename keys
						for key in state_dict.copy().keys():
												A__  :					int											=					state_dict.pop(UpperCAmelCase__ )
												A__  :					str											=					val
						# read in qkv matrices
						read_in_q_k_v(UpperCAmelCase__,  UpperCAmelCase__ )

						# load HuggingFace model
						A__  :					Optional[Any]											=					DPTForSemanticSegmentation(UpperCAmelCase__ ) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCAmelCase__ )
						model.load_state_dict(UpperCAmelCase__ )
						model.eval()

						# Check outputs on an image
						A__  :					Optional[Any]											=					4_8_0 if """ade""" in checkpoint_url else 3_8_4
						A__  :					Dict											=					DPTImageProcessor(size=UpperCAmelCase__ )

						A__  :					Optional[int]											=					prepare_img()
						A__  :					Any											=					image_processor(UpperCAmelCase__,  return_tensors="""pt""" )

						# forward pass
						A__  :					List[str]											=					model(**UpperCAmelCase__ ).logits if """ade""" in checkpoint_url else model(**UpperCAmelCase__ ).predicted_depth

						# Assert logits
						A__  :					Optional[Any]											=					torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
						if "ade" in checkpoint_url:
												A__  :					Optional[int]											=					torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
						assert outputs.shape == torch.Size(UpperCAmelCase__ )
						assert (
						    torch.allclose(outputs[0, 0, :3, :3],  UpperCAmelCase__,  atol=1e-4 )
						    if "ade" in checkpoint_url
						    else torch.allclose(outputs[0, :3, :3],  UpperCAmelCase__ )
						)

						Path(UpperCAmelCase__ ).mkdir(exist_ok=UpperCAmelCase__ )
						print(f'Saving model to {pytorch_dump_folder_path}' )
						model.save_pretrained(UpperCAmelCase__ )
						print(f'Saving image processor to {pytorch_dump_folder_path}' )
						image_processor.save_pretrained(UpperCAmelCase__ )

						if push_to_hub:
												print("""Pushing model to hub...""" )
												model.push_to_hub(
												    repo_path_or_name=Path(UpperCAmelCase__,  UpperCAmelCase__ ),  organization="""nielsr""",  commit_message="""Add model""",  use_temp_dir=UpperCAmelCase__,  )
												image_processor.push_to_hub(
												    repo_path_or_name=Path(UpperCAmelCase__,  UpperCAmelCase__ ),  organization="""nielsr""",  commit_message="""Add image processor""",  use_temp_dir=UpperCAmelCase__,  )


if __name__ == "__main__":
						A_      =							argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--checkpoint_url''',
						    default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
						    type=str,
						    help='''URL of the original DPT checkpoint you\'d like to convert.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_folder_path''',
						    default=None,
						    type=str,
						    required=True,
						    help='''Path to the output PyTorch model directory.''',
						)
						parser.add_argument(
						    '''--push_to_hub''',
						    action='''store_true''',
						)
						parser.add_argument(
						    '''--model_name''',
						    default='''dpt-large''',
						    type=str,
						    help='''Name of the model, in case you\'re pushing to the hub.''',
						)

						A_      =							parser.parse_args()
						convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)






