
"""simple docstring"""

import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)
class      _UpperCAmelCase							(		lowercase_							):
      __SCREAMING_SNAKE_CASE   :							List[Any]							      =	['''input_features''']




      def __init__(							self   ,       lowercase_=8_0   ,       lowercase_=1_6_0_0_0   ,       lowercase_=1_6_0   ,       lowercase_=3_0   ,       lowercase_=4_0_0   ,       lowercase_=0.0   ,       lowercase_=False   ,       **lowercase_   ,       )						->					Tuple:
          super().__init__(
              feature_size=lowercase_   ,       sampling_rate=lowercase_   ,       padding_value=lowercase_   ,       return_attention_mask=lowercase_   ,       **lowercase_   ,       )
          UpperCAmelCase									=					n_fft
          UpperCAmelCase									=					hop_length
          UpperCAmelCase									=					chunk_length
          UpperCAmelCase									=					chunk_length * sampling_rate
          UpperCAmelCase									=					self.n_samples // hop_length
          UpperCAmelCase									=					sampling_rate
          UpperCAmelCase									=					mel_filter_bank(
              num_frequency_bins=1 + n_fft // 2   ,       num_mel_filters=lowercase_   ,       min_frequency=0.0   ,       max_frequency=8_0_0_0.0   ,       sampling_rate=lowercase_   ,       norm='slaney'   ,       mel_scale='slaney'   ,       )




      def     a_      (							self   ,       lowercase_							)						->					Optional[int]:
          UpperCAmelCase									=					spectrogram(
              lowercase_   ,       window_function(self.n_fft   ,       'hann'							)   ,       frame_length=self.n_fft   ,       hop_length=self.hop_length   ,       power=2.0   ,       mel_filters=self.mel_filters   ,       log_mel='log10'   ,       )
          UpperCAmelCase									=					log_spec[:, :-1]
          UpperCAmelCase									=					np.maximum(lowercase_   ,       log_spec.max() - 8.0							)
          UpperCAmelCase									=					(log_spec + 4.0) / 4.0
          return log_spec




      @staticmethod
      # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
      def     a_      (							lowercase_   ,       lowercase_   ,       lowercase_ = 0.0							)						->					int:
          if attention_mask is not None:
              UpperCAmelCase									=					np.array(lowercase_   ,       np.intaa							)
              UpperCAmelCase									=					[]

              for vector, length in zip(lowercase_   ,       attention_mask.sum(-1							)							):
                  UpperCAmelCase									=					(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1E-7							)
                  if length < normed_slice.shape[0]:
                      UpperCAmelCase									=					padding_value

                  normed_input_values.append(lowercase_							)
          else:
              UpperCAmelCase									=					[(x - x.mean()) / np.sqrt(x.var() + 1E-7							) for x in input_values]

          return normed_input_values




      def __call__(							self   ,       lowercase_   ,       lowercase_ = True   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = "max_length"   ,       lowercase_ = None   ,       lowercase_ = None   ,       lowercase_ = None   ,       **lowercase_   ,       )						->					Optional[int]:

          if sampling_rate is not None:
              if sampling_rate != self.sampling_rate:
                  raise ValueError(
                      F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
                      F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
                      F" was sampled with {self.sampling_rate} and not {sampling_rate}."							)
          else:
              logger.warning(
                  'It is strongly recommended to pass the `sampling_rate` argument to this function. '
                  'Failing to do so can result in silent errors that might be hard to debug.'							)

          UpperCAmelCase									=					isinstance(lowercase_   ,       np.ndarray							) and len(raw_speech.shape							) > 1
          if is_batched_numpy and len(raw_speech.shape							) > 2:
              raise ValueError(F"Only mono-channel audio is supported for input to {self}"							)
          UpperCAmelCase									=					is_batched_numpy or (
              isinstance(lowercase_   ,       (list, tuple)							) and (isinstance(raw_speech[0]   ,       (np.ndarray, tuple, list)							))
          )

          if is_batched:
              UpperCAmelCase									=					[np.asarray([speech]   ,       dtype=np.floataa							).T for speech in raw_speech]
          elif not is_batched and not isinstance(lowercase_   ,       np.ndarray							):
              UpperCAmelCase									=					np.asarray(lowercase_   ,       dtype=np.floataa							)
          elif isinstance(lowercase_   ,       np.ndarray							) and raw_speech.dtype is np.dtype(np.floataa							):
              UpperCAmelCase									=					raw_speech.astype(np.floataa							)

          # always return batch
          if not is_batched:
              UpperCAmelCase									=					[np.asarray([raw_speech]							).T]

          UpperCAmelCase									=					BatchFeature({'input_features': raw_speech}							)

          # convert into correct format for padding

          UpperCAmelCase									=					self.pad(
              lowercase_   ,       padding=lowercase_   ,       max_length=max_length if max_length else self.n_samples   ,       truncation=lowercase_   ,       pad_to_multiple_of=lowercase_   ,       return_attention_mask=return_attention_mask or do_normalize   ,       )

          # zero-mean and unit-variance normalization
          if do_normalize:
              UpperCAmelCase									=					self.zero_mean_unit_var_norm(
                  padded_inputs['input_features']   ,       attention_mask=padded_inputs['attention_mask']   ,       padding_value=self.padding_value   ,       )
              UpperCAmelCase									=					np.stack(padded_inputs['input_features']   ,       axis=0							)

          # make sure list is in array format
          UpperCAmelCase									=					padded_inputs.get('input_features'							).transpose(2   ,       0   ,       1							)

          UpperCAmelCase									=					[self._np_extract_fbank_features(lowercase_							) for waveform in input_features[0]]

          if isinstance(input_features[0]   ,       lowercase_							):
              UpperCAmelCase									=					[np.asarray(lowercase_   ,       dtype=np.floataa							) for feature in input_features]
          else:
              UpperCAmelCase									=					input_features

          if return_attention_mask:
              # rescale from sample (48000) to feature (3000)
              UpperCAmelCase									=					padded_inputs['attention_mask'][:, :: self.hop_length]

          if return_tensors is not None:
              UpperCAmelCase									=					padded_inputs.convert_to_tensors(lowercase_							)

          return padded_inputs




      def     a_      (							self							)						->					int:
          UpperCAmelCase									=					copy.deepcopy(self.__dict__							)
          UpperCAmelCase									=					self.__class__.__name__
          if "mel_filters" in output:
              del output["mel_filters"]
          return output





"""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()
SCREAMING_SNAKE_CASE_    					=						logging.get_logger(__name__)




def     lowercase__						(					lowerCAmelCase				:					int ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					DPTConfig()

    if "large" in checkpoint_url:
        UpperCAmelCase									=					1_024
        UpperCAmelCase									=					4_096
        UpperCAmelCase									=					24
        UpperCAmelCase									=					16
        UpperCAmelCase									=					[5, 11, 17, 23]
        UpperCAmelCase									=					[256, 512, 1_024, 1_024]
        UpperCAmelCase									=					(1, 384, 384)

    if "ade" in checkpoint_url:
        UpperCAmelCase									=					True

        UpperCAmelCase									=					150
        UpperCAmelCase									=					'huggingface/label-files'
        UpperCAmelCase									=					'ade20k-id2label.json'
        UpperCAmelCase									=					json.load(open(cached_download(hf_hub_url(lowerCAmelCase ,				lowerCAmelCase ,				repo_type='dataset' ) ) ,				'r' ) )
        UpperCAmelCase									=					{int(lowerCAmelCase ): v for k, v in idalabel.items()}
        UpperCAmelCase									=					idalabel
        UpperCAmelCase									=					{v: k for k, v in idalabel.items()}
        UpperCAmelCase									=					[1, 150, 480, 480]

    return config, expected_shape




def     lowercase__						(					lowerCAmelCase				:					str ) ->      Any:






    """simple docstring"""




    UpperCAmelCase									=					['pretrained.model.head.weight', 'pretrained.model.head.bias']
    for k in ignore_keys:
        state_dict.pop(lowerCAmelCase ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					Union[str, Any] ) ->      List[Any]:






    """simple docstring"""




    if (
        "pretrained.model" in name
        and "cls_token" not in name
        and "pos_embed" not in name
        and "patch_embed" not in name
    ):
        UpperCAmelCase									=					name.replace('pretrained.model' ,				'dpt.encoder' )
    if "pretrained.model" in name:
        UpperCAmelCase									=					name.replace('pretrained.model' ,				'dpt.embeddings' )
    if "patch_embed" in name:
        UpperCAmelCase									=					name.replace('patch_embed' ,				'patch_embeddings' )
    if "pos_embed" in name:
        UpperCAmelCase									=					name.replace('pos_embed' ,				'position_embeddings' )
    if "attn.proj" in name:
        UpperCAmelCase									=					name.replace('attn.proj' ,				'attention.output.dense' )
    if "proj" in name and "project" not in name:
        UpperCAmelCase									=					name.replace('proj' ,				'projection' )
    if "blocks" in name:
        UpperCAmelCase									=					name.replace('blocks' ,				'layer' )
    if "mlp.fc1" in name:
        UpperCAmelCase									=					name.replace('mlp.fc1' ,				'intermediate.dense' )
    if "mlp.fc2" in name:
        UpperCAmelCase									=					name.replace('mlp.fc2' ,				'output.dense' )
    if "norm1" in name:
        UpperCAmelCase									=					name.replace('norm1' ,				'layernorm_before' )
    if "norm2" in name:
        UpperCAmelCase									=					name.replace('norm2' ,				'layernorm_after' )
    if "scratch.output_conv" in name:
        UpperCAmelCase									=					name.replace('scratch.output_conv' ,				'head' )
    if "scratch" in name:
        UpperCAmelCase									=					name.replace('scratch' ,				'neck' )
    if "layer1_rn" in name:
        UpperCAmelCase									=					name.replace('layer1_rn' ,				'convs.0' )
    if "layer2_rn" in name:
        UpperCAmelCase									=					name.replace('layer2_rn' ,				'convs.1' )
    if "layer3_rn" in name:
        UpperCAmelCase									=					name.replace('layer3_rn' ,				'convs.2' )
    if "layer4_rn" in name:
        UpperCAmelCase									=					name.replace('layer4_rn' ,				'convs.3' )
    if "refinenet" in name:
        UpperCAmelCase									=					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
        UpperCAmelCase									=					name.replace(F"refinenet{layer_idx}" ,				F"fusion_stage.layers.{abs(layer_idx-4 )}" )
    if "out_conv" in name:
        UpperCAmelCase									=					name.replace('out_conv' ,				'projection' )
    if "resConfUnit1" in name:
        UpperCAmelCase									=					name.replace('resConfUnit1' ,				'residual_layer1' )
    if "resConfUnit2" in name:
        UpperCAmelCase									=					name.replace('resConfUnit2' ,				'residual_layer2' )
    if "conv1" in name:
        UpperCAmelCase									=					name.replace('conv1' ,				'convolution1' )
    if "conv2" in name:
        UpperCAmelCase									=					name.replace('conv2' ,				'convolution2' )
    # readout blocks
    if "pretrained.act_postprocess1.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess1.0.project.0' ,				'neck.reassemble_stage.readout_projects.0.0' )
    if "pretrained.act_postprocess2.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess2.0.project.0' ,				'neck.reassemble_stage.readout_projects.1.0' )
    if "pretrained.act_postprocess3.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess3.0.project.0' ,				'neck.reassemble_stage.readout_projects.2.0' )
    if "pretrained.act_postprocess4.0.project.0" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess4.0.project.0' ,				'neck.reassemble_stage.readout_projects.3.0' )
    # resize blocks
    if "pretrained.act_postprocess1.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess1.3' ,				'neck.reassemble_stage.layers.0.projection' )
    if "pretrained.act_postprocess1.4" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess1.4' ,				'neck.reassemble_stage.layers.0.resize' )
    if "pretrained.act_postprocess2.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess2.3' ,				'neck.reassemble_stage.layers.1.projection' )
    if "pretrained.act_postprocess2.4" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess2.4' ,				'neck.reassemble_stage.layers.1.resize' )
    if "pretrained.act_postprocess3.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess3.3' ,				'neck.reassemble_stage.layers.2.projection' )
    if "pretrained.act_postprocess4.3" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess4.3' ,				'neck.reassemble_stage.layers.3.projection' )
    if "pretrained.act_postprocess4.4" in name:
        UpperCAmelCase									=					name.replace('pretrained.act_postprocess4.4' ,				'neck.reassemble_stage.layers.3.resize' )
    if "pretrained" in name:
        UpperCAmelCase									=					name.replace('pretrained' ,				'dpt' )
    if "bn" in name:
        UpperCAmelCase									=					name.replace('bn' ,				'batch_norm' )
    if "head" in name:
        UpperCAmelCase									=					name.replace('head' ,				'head.head' )
    if "encoder.norm" in name:
        UpperCAmelCase									=					name.replace('encoder.norm' ,				'layernorm' )
    if "auxlayer" in name:
        UpperCAmelCase									=					name.replace('auxlayer' ,				'auxiliary_head.head' )

    return name




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					int ) ->      int:






    """simple docstring"""




    for i in range(config.num_hidden_layers ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCAmelCase									=					state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight" )
        UpperCAmelCase									=					state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.bias" )
        # next, add query, keys and values (in that order) to the state dict
        UpperCAmelCase									=					in_proj_weight[: config.hidden_size, :]
        UpperCAmelCase									=					in_proj_bias[: config.hidden_size]
        UpperCAmelCase									=					in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase									=					in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCAmelCase									=					in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase									=					in_proj_bias[-config.hidden_size :]




def     lowercase__						(					) ->      Optional[Any]:






    """simple docstring"""




    UpperCAmelCase									=					'http://images.cocodataset.org/val2017/000000039769.jpg'
    UpperCAmelCase									=					Image.open(requests.get(lowerCAmelCase ,				stream=lowerCAmelCase ).raw )
    return im




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Dict ,				lowerCAmelCase				:					Optional[int] ,				lowerCAmelCase				:					List[str] ,				lowerCAmelCase				:					Union[str, Any] ) ->      Union[str, Any]:






    """simple docstring"""




    UpperCAmelCase			,							UpperCAmelCase									=					get_dpt_config(lowerCAmelCase )
    # load original state_dict from URL
    UpperCAmelCase									=					torch.hub.load_state_dict_from_url(lowerCAmelCase ,				map_location='cpu' )
    # remove certain keys
    remove_ignore_keys_(lowerCAmelCase )
    # rename keys
    for key in state_dict.copy().keys():
        UpperCAmelCase									=					state_dict.pop(lowerCAmelCase )
        UpperCAmelCase									=					val
    # read in qkv matrices
    read_in_q_k_v(lowerCAmelCase ,				lowerCAmelCase )

    # load HuggingFace model
    UpperCAmelCase									=					DPTForSemanticSegmentation(lowerCAmelCase ) if 'ade' in checkpoint_url else DPTForDepthEstimation(lowerCAmelCase )
    model.load_state_dict(lowerCAmelCase )
    model.eval()

    # Check outputs on an image
    UpperCAmelCase									=					480 if 'ade' in checkpoint_url else 384
    UpperCAmelCase									=					DPTImageProcessor(size=lowerCAmelCase )

    UpperCAmelCase									=					prepare_img()
    UpperCAmelCase									=					image_processor(lowerCAmelCase ,				return_tensors='pt' )

    # forward pass
    UpperCAmelCase									=					model(**lowerCAmelCase ).logits if 'ade' in checkpoint_url else model(**lowerCAmelCase ).predicted_depth

    # Assert logits
    UpperCAmelCase									=					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:
        UpperCAmelCase									=					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(lowerCAmelCase )
    assert (
        torch.allclose(outputs[0, 0, :3, :3] ,				lowerCAmelCase ,				atol=1E-4 )
        if "ade" in checkpoint_url
        else torch.allclose(outputs[0, :3, :3] ,				lowerCAmelCase )
    )

    Path(lowerCAmelCase ).mkdir(exist_ok=lowerCAmelCase )
    print(F"Saving model to {pytorch_dump_folder_path}" )
    model.save_pretrained(lowerCAmelCase )
    print(F"Saving image processor to {pytorch_dump_folder_path}" )
    image_processor.save_pretrained(lowerCAmelCase )

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


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_    					=						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.''',
   )

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



