


'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


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

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







     """simple docstring"""
     _A				=							OrderedDict()
     for key, value in state_dict.items():
          if key.startswith('module.encoder'		):
               _A				=							key.replace('module.encoder'							,    'glpn.encoder'		)
          if key.startswith('module.decoder'		):
               _A				=							key.replace('module.decoder'							,    'decoder.stages'		)
          if "patch_embed" in key:
               # replace for example patch_embed1 by patch_embeddings.0
               _A				=							key[key.find('patch_embed'		) + len('patch_embed'		)]
               _A				=							key.replace(F'patch_embed{idx}'							,    F'patch_embeddings.{int(__UpperCamelCase		)-1}'		)
          if "norm" in key:
               _A				=							key.replace('norm'							,    'layer_norm'		)
          if "glpn.encoder.layer_norm" in key:
               # replace for example layer_norm1 by layer_norm.0
               _A				=							key[key.find('glpn.encoder.layer_norm'		) + len('glpn.encoder.layer_norm'		)]
               _A				=							key.replace(F'layer_norm{idx}'							,    F'layer_norm.{int(__UpperCamelCase		)-1}'		)
          if "layer_norm1" in key:
               _A				=							key.replace('layer_norm1'							,    'layer_norm_1'		)
          if "layer_norm2" in key:
               _A				=							key.replace('layer_norm2'							,    'layer_norm_2'		)
          if "block" in key:
               # replace for example block1 by block.0
               _A				=							key[key.find('block'		) + len('block'		)]
               _A				=							key.replace(F'block{idx}'							,    F'block.{int(__UpperCamelCase		)-1}'		)
          if "attn.q" in key:
               _A				=							key.replace('attn.q'							,    'attention.self.query'		)
          if "attn.proj" in key:
               _A				=							key.replace('attn.proj'							,    'attention.output.dense'		)
          if "attn" in key:
               _A				=							key.replace('attn'							,    'attention.self'		)
          if "fc1" in key:
               _A				=							key.replace('fc1'							,    'dense1'		)
          if "fc2" in key:
               _A				=							key.replace('fc2'							,    'dense2'		)
          if "linear_pred" in key:
               _A				=							key.replace('linear_pred'							,    'classifier'		)
          if "linear_fuse" in key:
               _A				=							key.replace('linear_fuse.conv'							,    'linear_fuse'		)
               _A				=							key.replace('linear_fuse.bn'							,    'batch_norm'		)
          if "linear_c" in key:
               # replace for example linear_c4 by linear_c.3
               _A				=							key[key.find('linear_c'		) + len('linear_c'		)]
               _A				=							key.replace(F'linear_c{idx}'							,    F'linear_c.{int(__UpperCamelCase		)-1}'		)
          if "bot_conv" in key:
               _A				=							key.replace('bot_conv'							,    '0.convolution'		)
          if "skip_conv1" in key:
               _A				=							key.replace('skip_conv1'							,    '1.convolution'		)
          if "skip_conv2" in key:
               _A				=							key.replace('skip_conv2'							,    '2.convolution'		)
          if "fusion1" in key:
               _A				=							key.replace('fusion1'							,    '1.fusion'		)
          if "fusion2" in key:
               _A				=							key.replace('fusion2'							,    '2.fusion'		)
          if "fusion3" in key:
               _A				=							key.replace('fusion3'							,    '3.fusion'		)
          if "fusion" in key and "conv" in key:
               _A				=							key.replace('conv'							,    'convolutional_layer'		)
          if key.startswith('module.last_layer_depth'		):
               _A				=							key.replace('module.last_layer_depth'							,    'head.head'		)
          _A				=							value

     return new_state_dict

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







     """simple docstring"""
     # for each of the encoder blocks:
     for i in range(config.num_encoder_blocks		):
          for j in range(config.depths[i]		):
               # read in weights + bias of keys and values (which is a single matrix in the original implementation)
               _A				=							state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight'		)
               _A				=							state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias'		)
               # next, add keys and values (in that order) to the state dict
               _A				=							kv_weight[
                   : config.hidden_sizes[i], :
               ]
               _A				=							kv_bias[: config.hidden_sizes[i]]
               _A				=							kv_weight[
                   config.hidden_sizes[i] :, :
               ]
               _A				=							kv_bias[config.hidden_sizes[i] :]

def 					lowerCamelCase_ ( )					->	List[Any]:







     """simple docstring"""
     _A				=							'http://images.cocodataset.org/val2017/000000039769.jpg'
     _A				=							Image.open(requests.get(__UpperCamelCase							,    stream=__UpperCamelCase		).raw		)

     return image

@torch.no_grad()
def 					lowerCamelCase_ ( __UpperCamelCase					:       int							,    __UpperCamelCase					:       int							,    __UpperCamelCase					:       str=False							,    __UpperCamelCase					:       List[Any]=None		)					->	Any:







     """simple docstring"""
     _A				=							GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]							,    decoder_hidden_size=6_4							,    depths=[3, 8, 2_7, 3]		)

     # load image processor (only resize + rescale)
     _A				=							GLPNImageProcessor()

     # prepare image
     _A				=							prepare_img()
     _A				=							image_processor(images=__UpperCamelCase							,    return_tensors='pt'		).pixel_values

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

     # load original state dict
     _A				=							torch.load(__UpperCamelCase							,    map_location=torch.device('cpu'		)		)

     # rename keys
     _A				=							rename_keys(__UpperCamelCase		)

     # key and value matrices need special treatment
     read_in_k_v(__UpperCamelCase							,    __UpperCamelCase		)

     # create HuggingFace model and load state dict
     _A				=							GLPNForDepthEstimation(__UpperCamelCase		)
     model.load_state_dict(__UpperCamelCase		)
     model.eval()

     # forward pass
     _A				=							model(__UpperCamelCase		)
     _A				=							outputs.predicted_depth

     # verify output
     if model_name is not None:
          if "nyu" in model_name:
               _A				=							torch.tensor(
                   [[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]]		)
          elif "kitti" in model_name:
               _A				=							torch.tensor(
                   [[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]]		)
          else:
               raise ValueError(F'Unknown model name: {model_name}'		)

          _A				=							torch.Size([1, 4_8_0, 6_4_0]		)

          assert predicted_depth.shape == expected_shape
          assert torch.allclose(predicted_depth[0, :3, :3]							,    __UpperCamelCase							,    atol=1e-4		)
          print('Looks ok!'		)

     # finally, push to hub if required
     if push_to_hub:
          logger.info('Pushing model and image processor to the 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__":
    lowerCAmelCase						=					argparse.ArgumentParser()

    parser.add_argument(
        """--checkpoint_path""",
        default=None,
        type=str,
        help="""Path to the original PyTorch checkpoint (.pth file).""",
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
    )
    parser.add_argument(
        """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
    )
    parser.add_argument(
        """--model_name""",
        default="""glpn-kitti""",
        type=str,
        help="""Name of the model in case you're pushing to the hub.""",
    )
    lowerCAmelCase						=					parser.parse_args()
    convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)









'''simple docstring'''
import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available

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 torch import nn

    from transformers import ASTForAudioClassification, ASTModel
    from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
        AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
    )


if is_torchaudio_available():
    import torchaudio

    from transformers import ASTFeatureExtractor






class        lowerCAmelCase_  :






       def __init__( self			,     _UpperCamelCase			,     _UpperCamelCase=13			,     _UpperCamelCase=2			,     _UpperCamelCase=24			,     _UpperCamelCase=16			,     _UpperCamelCase=True			,     _UpperCamelCase=True			,     _UpperCamelCase=32			,     _UpperCamelCase=5			,     _UpperCamelCase=4			,     _UpperCamelCase=37			,     _UpperCamelCase="gelu"			,     _UpperCamelCase=0.1			,     _UpperCamelCase=0.1			,     _UpperCamelCase=10			,     _UpperCamelCase=0.02			,     _UpperCamelCase=None			,     _UpperCamelCase=2			,     _UpperCamelCase=2			,     )->      Tuple:
            _A				=							parent
            _A				=							batch_size
            _A				=							patch_size
            _A				=							max_length
            _A				=							num_mel_bins
            _A				=							is_training
            _A				=							use_labels
            _A				=							hidden_size
            _A				=							num_hidden_layers
            _A				=							num_attention_heads
            _A				=							intermediate_size
            _A				=							hidden_act
            _A				=							hidden_dropout_prob
            _A				=							attention_probs_dropout_prob
            _A				=							type_sequence_label_size
            _A				=							initializer_range
            _A				=							scope
            _A				=							frequency_stride
            _A				=							time_stride

            # in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
            _A				=							(self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
            _A				=							(self.max_length - self.patch_size) // self.time_stride + 1
            _A				=							frequency_out_dimension * time_out_dimension
            _A				=							num_patches + 2






       def   UpperCamelCase						( self   )->      int:
            _A				=							floats_tensor([self.batch_size, self.max_length, self.num_mel_bins]   )

            _A				=							None
            if self.use_labels:
                 _A				=							ids_tensor([self.batch_size]			,     self.type_sequence_label_size   )

            _A				=							self.get_config()

            return config, input_values, labels






       def   UpperCamelCase						( self   )->      int:
            return ASTConfig(
                patch_size=self.patch_size			,     max_length=self.max_length			,     num_mel_bins=self.num_mel_bins			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     is_decoder=_UpperCamelCase			,     initializer_range=self.initializer_range			,     frequency_stride=self.frequency_stride			,     time_stride=self.time_stride			,     )






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Optional[Any]:
            _A				=							ASTModel(config=_UpperCamelCase   )
            model.to(_UpperCamelCase   )
            model.eval()
            _A				=							model(_UpperCamelCase   )
            self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)   )






       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.prepare_config_and_inputs()
            (
                (
                _A
            )		,				(
                _A
            )		,				(
                _A
            )		,				
            )				=							config_and_inputs
            _A				=							{'input_values': input_values}
            return config, inputs_dict






@require_torch
class        lowerCAmelCase_  (     UpperCAmelCase      ,							UpperCAmelCase      ,							unittest.TestCase	):
       __UpperCAmelCase					=(
           (
               ASTModel,
               ASTForAudioClassification,
           )
           if is_torch_available()
           else ()
       )
       __UpperCAmelCase					=(
           {'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
           if is_torch_available()
           else {}
       )
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False
       __UpperCAmelCase					=False






       def   UpperCamelCase						( self			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase			,     _UpperCamelCase   )->      Union[str, Any]:
            if pipeline_test_casse_name == "AudioClassificationPipelineTests":
                 return True

            return False






       def   UpperCamelCase						( self   )->      List[str]:
            _A				=							ASTModelTester(self   )
            _A				=							ConfigTester(self			,     config_class=_UpperCamelCase			,     has_text_modality=_UpperCamelCase			,     hidden_size=37   )






       def   UpperCamelCase						( self   )->      int:
            self.config_tester.run_common_tests()






       @unittest.skip(reason='AST does not use inputs_embeds'   )
       def   UpperCamelCase						( self   )->      List[Any]:
            pass






       def   UpperCamelCase						( self   )->      str:
            _A		,				_A				=							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _A				=							model_class(_UpperCamelCase   )
                 self.assertIsInstance(model.get_input_embeddings()			,     (nn.Module)   )
                 _A				=							model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_UpperCamelCase			,     nn.Linear   )   )






       def   UpperCamelCase						( self   )->      List[str]:
            _A		,				_A				=							self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _A				=							model_class(_UpperCamelCase   )
                 _A				=							inspect.signature(model.forward   )
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _A				=							[*signature.parameters.keys()]

                 _A				=							['input_values']
                 self.assertListEqual(arg_names[:1]			,     _UpperCamelCase   )






       def   UpperCamelCase						( self   )->      Optional[Any]:
            _A				=							self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*_UpperCamelCase   )






       @slow
       def   UpperCamelCase						( self   )->      Tuple:
            for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _A				=							ASTModel.from_pretrained(_UpperCamelCase   )
                 self.assertIsNotNone(_UpperCamelCase   )






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







     """simple docstring"""
     _A				=							hf_hub_download(
         repo_id='nielsr/audio-spectogram-transformer-checkpoint'							,    filename='sample_audio.flac'							,    repo_type='dataset'		)

     _A		,				_A				=							torchaudio.load(__UpperCamelCase		)

     return audio, sampling_rate






@require_torch
@require_torchaudio
class        lowerCAmelCase_  (     unittest.TestCase	):






       @cached_property
       def   UpperCamelCase						( self   )->      Dict:
            return (
                ASTFeatureExtractor.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'   )
                if is_torchaudio_available()
                else None
            )






       @slow
       def   UpperCamelCase						( self   )->      Any:
            _A				=							self.default_feature_extractor
            _A				=							ASTForAudioClassification.from_pretrained('MIT/ast-finetuned-audioset-10-10-0.4593'   ).to(_UpperCamelCase   )

            _A				=							self.default_feature_extractor
            _A		,				_A				=							prepare_audio()
            _A				=							audio.squeeze().numpy()
            _A				=							feature_extractor(_UpperCamelCase			,     sampling_rate=_UpperCamelCase			,     return_tensors='pt'   ).to(_UpperCamelCase   )

            # forward pass
            with torch.no_grad():
                 _A				=							model(**_UpperCamelCase   )

            # verify the logits
            _A				=							torch.Size((1, 527)   )
            self.assertEqual(outputs.logits.shape			,     _UpperCamelCase   )

            _A				=							torch.tensor([-0.8760, -7.0042, -8.6602]   ).to(_UpperCamelCase   )

            self.assertTrue(torch.allclose(outputs.logits[0, :3]			,     _UpperCamelCase			,     atol=1e-4   )   )





