




'''simple docstring'''


import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
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


lowerCAmelCase_      :  Optional[int]				     =  random.Random()



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					Any=1.0 ,    UpperCamelCase__    :					Any=None ,    UpperCamelCase__    :					Union[str, Any]=None				):




     """simple docstring"""


     if rng is None:
          a_     :							str     						=  global_rng

     a_     :							int     						=  []
     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      SCREAMING_SNAKE_CASE    (						unittest.TestCase				):






      def __init__( self   :  int						,						lowercase__   :  Optional[Any]						,						lowercase__   :  Tuple=7						,						lowercase__   :  str=400						,						lowercase__   :  List[Any]=2000						,						lowercase__   :  Optional[int]=1						,						lowercase__   :  Optional[Any]=0.0						,						lowercase__   :  Any=1_6000						,						lowercase__   :  str=True						,						lowercase__   :  Optional[int]=80						,						lowercase__   :  int=16						,						lowercase__   :  Union[str, Any]=64						,						lowercase__   :  str="hann_window"						,						lowercase__   :  str=80						,						lowercase__   :  Any=7600						,						lowercase__   :  int=1e-1_0						,						lowercase__   :  Union[str, Any]=True						,						):


           '''simple docstring'''

           a_     :							int     						=  parent
           a_     :							Tuple     						=  batch_size
           a_     :							List[str]     						=  min_seq_length
           a_     :							Any     						=  max_seq_length
           a_     :							List[Any]     						=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           a_     :							List[Any]     						=  feature_size
           a_     :							str     						=  padding_value
           a_     :							Dict     						=  sampling_rate
           a_     :							List[Any]     						=  do_normalize
           a_     :							List[Any]     						=  num_mel_bins
           a_     :							Optional[Any]     						=  hop_length
           a_     :							Any     						=  win_length
           a_     :							List[str]     						=  win_function
           a_     :							List[Any]     						=  fmin
           a_     :							Optional[int]     						=  fmax
           a_     :							Tuple     						=  mel_floor
           a_     :							Optional[int]     						=  return_attention_mask






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           return {
               "feature_size": self.feature_size,
               "padding_value": self.padding_value,
               "sampling_rate": self.sampling_rate,
               "do_normalize": self.do_normalize,
               "num_mel_bins": self.num_mel_bins,
               "hop_length": self.hop_length,
               "win_length": self.win_length,
               "win_function": self.win_function,
               "fmin": self.fmin,
               "fmax": self.fmax,
               "mel_floor": self.mel_floor,
               "return_attention_mask": self.return_attention_mask,
           }






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  str=False       ):


           '''simple docstring'''

           def _flatten(lowercase__   :  Any       ):
                return list(itertools.chain(*lowercase__       )       )

           if equal_length:
                a_     :							Any     						=  floats_list((self.batch_size, self.max_seq_length)       )
           else:
                # make sure that inputs increase in size
                a_     :							Union[str, Any]     						=  [
                    _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:
                a_     :							List[Any]     						=  [np.asarray(lowercase__       ) for x in speech_inputs]

           return speech_inputs






      def 							lowercase_						( self   :  str						,						lowercase__   :  Optional[Any]=False						,						lowercase__   :  Optional[Any]=False       ):


           '''simple docstring'''

           if equal_length:
                a_     :							List[Any]     						=  [floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
           else:
                # make sure that inputs increase in size
                a_     :							Optional[Any]     						=  [
                    floats_list((x, self.num_mel_bins)       )
                    for x in range(self.min_seq_length						,						self.max_seq_length						,						self.seq_length_diff       )
                ]

           if numpify:
                a_     :							Tuple     						=  [np.asarray(lowercase__       ) for x in speech_inputs]

           return speech_inputs







@require_torch
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Optional[int]   							=					SpeechTaFeatureExtractor






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Dict     						=  SpeechTaFeatureExtractionTester(self       )






      def 							lowercase_						( self   :  List[Any]						,						lowercase__   :  List[Any]       ):


           '''simple docstring'''

           self.assertTrue(np.all(np.mean(lowercase__						,						axis=0       ) < 1e-3       )       )
           self.assertTrue(np.all(np.abs(np.var(lowercase__						,						axis=0       ) - 1       ) < 1e-3       )       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_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(lowercase__       ) for speech_input in speech_inputs]

           # Test not batched input
           a_     :							Tuple     						=  feat_extract(speech_inputs[0]						,						return_tensors="""np"""       ).input_values
           a_     :							Optional[int]     						=  feat_extract(np_speech_inputs[0]						,						return_tensors="""np"""       ).input_values
           self.assertTrue(np.allclose(lowercase__						,						lowercase__						,						atol=1e-3       )       )

           # Test batched
           a_     :							int     						=  feat_extract(lowercase__						,						return_tensors="""np"""       ).input_values
           a_     :							Tuple     						=  feat_extract(lowercase__						,						return_tensors="""np"""       ).input_values
           for enc_seq_a, enc_seq_a in zip(lowercase__						,						lowercase__       ):
                self.assertTrue(np.allclose(lowercase__						,						lowercase__						,						atol=1e-3       )       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							int     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							List[Any]     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]

           a_     :							Optional[Any]     						=  ["""longest""", """max_length""", """do_not_pad"""]
           a_     :							Dict     						=  [None, 1600, None]
           for max_length, padding in zip(lowercase__						,						lowercase__       ):
                a_     :							List[str]     						=  feat_extract(lowercase__						,						padding=lowercase__						,						max_length=lowercase__						,						return_tensors="""np"""       )
                a_     :							Dict     						=  processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:800]       )
                self.assertTrue(input_values[0][800:].sum() < 1e-6       )
                self._check_zero_mean_unit_variance(input_values[1][:1000]       )
                self.assertTrue(input_values[0][1000:].sum() < 1e-6       )
                self._check_zero_mean_unit_variance(input_values[2][:1200]       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							List[Any]     						=  range(800						,						1400						,						200       )
           a_     :							str     						=  [floats_list((1, x)       )[0] for x in lengths]

           a_     :							List[Any]     						=  ["""longest""", """max_length""", """do_not_pad"""]
           a_     :							Optional[Any]     						=  [None, 1600, None]

           for max_length, padding in zip(lowercase__						,						lowercase__       ):
                a_     :							List[str]     						=  feat_extract(lowercase__						,						max_length=lowercase__						,						padding=lowercase__       )
                a_     :							List[str]     						=  processed.input_values

                self._check_zero_mean_unit_variance(input_values[0][:800]       )
                self._check_zero_mean_unit_variance(input_values[1][:1000]       )
                self._check_zero_mean_unit_variance(input_values[2][:1200]       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							Optional[Any]     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							int     						=  feat_extract(
               lowercase__						,						truncation=lowercase__						,						max_length=1000						,						padding="""max_length"""						,						return_tensors="""np"""       )
           a_     :							Optional[int]     						=  processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]       )
           self._check_zero_mean_unit_variance(input_values[1]       )
           self._check_zero_mean_unit_variance(input_values[2]       )






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							Any     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							Any     						=  feat_extract(
               lowercase__						,						truncation=lowercase__						,						max_length=1000						,						padding="""longest"""						,						return_tensors="""np"""       )
           a_     :							str     						=  processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]       )
           self._check_zero_mean_unit_variance(input_values[1, :1000]       )
           self._check_zero_mean_unit_variance(input_values[2]       )

           # make sure that if max_length < longest -> then pad to max_length
           self.assertTrue(input_values.shape == (3, 1000)       )

           a_     :							str     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							str     						=  feat_extract(
               lowercase__						,						truncation=lowercase__						,						max_length=2000						,						padding="""longest"""						,						return_tensors="""np"""       )
           a_     :							List[Any]     						=  processed.input_values

           self._check_zero_mean_unit_variance(input_values[0, :800]       )
           self._check_zero_mean_unit_variance(input_values[1, :1000]       )
           self._check_zero_mean_unit_variance(input_values[2]       )

           # make sure that if max_length > longest -> then pad to longest
           self.assertTrue(input_values.shape == (3, 1200)       )






      def 							lowercase_						( self   :  Dict       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           a_     :							Optional[Any]     						=  np.random.rand(100       ).astype(np.floataa       )
           a_     :							Tuple     						=  np_speech_inputs.tolist()

           for inputs in [py_speech_inputs, np_speech_inputs]:
                a_     :							List[str]     						=  feature_extractor.pad([{"""input_values""": inputs}]						,						return_tensors="""np"""       )
                self.assertTrue(np_processed.input_values.dtype == np.floataa       )
                a_     :							Union[str, Any]     						=  feature_extractor.pad([{"""input_values""": inputs}]						,						return_tensors="""pt"""       )
                self.assertTrue(pt_processed.input_values.dtype == torch.floataa       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							str     						=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
           # create three inputs of length 800, 1000, and 1200
           a_     :							List[str]     						=  [floats_list((1, x)       )[0] for x in range(800						,						1400						,						200       )]
           a_     :							List[str]     						=  [np.asarray(lowercase__       ) for speech_input in speech_inputs]

           # Test feature size
           a_     :							List[Any]     						=  feature_extractor(audio_target=lowercase__						,						padding=lowercase__						,						return_tensors="""np"""       ).input_values
           self.assertTrue(input_values.ndim == 3       )
           self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins       )

           # Test not batched input
           a_     :							int     						=  feature_extractor(speech_inputs[0]						,						return_tensors="""np"""       ).input_values
           a_     :							Optional[int]     						=  feature_extractor(np_speech_inputs[0]						,						return_tensors="""np"""       ).input_values
           self.assertTrue(np.allclose(lowercase__						,						lowercase__						,						atol=1e-3       )       )

           # Test batched
           a_     :							Union[str, Any]     						=  feature_extractor(lowercase__						,						return_tensors="""np"""       ).input_values
           a_     :							Optional[int]     						=  feature_extractor(lowercase__						,						return_tensors="""np"""       ).input_values
           for enc_seq_a, enc_seq_a in zip(lowercase__						,						lowercase__       ):
                self.assertTrue(np.allclose(lowercase__						,						lowercase__						,						atol=1e-3       )       )

           # Test 2-D numpy arrays are batched.
           a_     :							List[str]     						=  [floats_list((1, x)       )[0] for x in (800, 800, 800)]
           a_     :							Optional[int]     						=  np.asarray(lowercase__       )
           a_     :							Tuple     						=  feature_extractor(lowercase__						,						return_tensors="""np"""       ).input_values
           a_     :							Optional[Any]     						=  feature_extractor(lowercase__						,						return_tensors="""np"""       ).input_values
           for enc_seq_a, enc_seq_a in zip(lowercase__						,						lowercase__       ):
                self.assertTrue(np.allclose(lowercase__						,						lowercase__						,						atol=1e-3       )       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							int     						=  self.feat_extract_tester.prepare_inputs_for_target()
           a_     :							Union[str, Any]     						=  self.feature_extraction_class(**self.feat_extract_dict       )
           a_     :							str     						=  feat_extract.model_input_names[0]

           a_     :							List[Any]     						=  BatchFeature({input_name: speech_inputs}       )

           self.assertTrue(all(len(lowercase__       ) == len(lowercase__       ) for x, y in zip(lowercase__						,						processed_features[input_name]       )       )       )

           a_     :							Any     						=  self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase__       )
           a_     :							List[Any]     						=  BatchFeature({input_name: speech_inputs}						,						tensor_type="""np"""       )

           a_     :							List[Any]     						=  processed_features[input_name]

           if len(batch_features_input.shape       ) < 3:
                a_     :							str     						=  batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )






      @require_torch
      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							Tuple     						=  self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowercase__       )
           a_     :							Dict     						=  self.feature_extraction_class(**self.feat_extract_dict       )
           a_     :							Union[str, Any]     						=  feat_extract.model_input_names[0]

           a_     :							Dict     						=  BatchFeature({input_name: speech_inputs}						,						tensor_type="""pt"""       )

           a_     :							List[str]     						=  processed_features[input_name]

           if len(batch_features_input.shape       ) < 3:
                a_     :							Dict     						=  batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0]       ), self.feat_extract_tester.num_mel_bins)       )






      @require_torch
      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							Dict     						=  self.feature_extraction_class(**self.feat_extract_dict       )
           a_     :							str     						=  self.feat_extract_tester.prepare_inputs_for_target()
           a_     :							Dict     						=  feat_extract.model_input_names[0]

           a_     :							Optional[int]     						=  BatchFeature({input_name: speech_inputs}       )

           a_     :							List[str]     						=  feat_extract.num_mel_bins  # hack!

           a_     :							Union[str, Any]     						=  feat_extract.pad(lowercase__						,						padding="""longest"""						,						return_tensors="""np"""       )[input_name]
           a_     :							int     						=  feat_extract.pad(lowercase__						,						padding="""longest"""						,						return_tensors="""pt"""       )[input_name]

           self.assertTrue(abs(input_np.astype(np.floataa       ).sum() - input_pt.numpy().astype(np.floataa       ).sum()       ) < 1e-2       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.feat_extract_dict
           a_     :							List[str]     						=  True
           a_     :							List[Any]     						=  self.feature_extraction_class(**lowercase__       )
           a_     :							Union[str, Any]     						=  self.feat_extract_tester.prepare_inputs_for_target()
           a_     :							int     						=  [len(lowercase__       ) for x in speech_inputs]
           a_     :							Dict     						=  feat_extract.model_input_names[0]

           a_     :							str     						=  BatchFeature({input_name: speech_inputs}       )

           a_     :							List[Any]     						=  feat_extract.num_mel_bins  # hack!

           a_     :							str     						=  feat_extract.pad(lowercase__						,						padding="""longest"""						,						return_tensors="""np"""       )
           self.assertIn("""attention_mask"""						,						lowercase__       )
           self.assertListEqual(list(processed.attention_mask.shape       )						,						list(processed[input_name].shape[:2]       )       )
           self.assertListEqual(processed.attention_mask.sum(-1       ).tolist()						,						lowercase__       )






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.feat_extract_dict
           a_     :							int     						=  True
           a_     :							List[Any]     						=  self.feature_extraction_class(**lowercase__       )
           a_     :							int     						=  self.feat_extract_tester.prepare_inputs_for_target()
           a_     :							List[Any]     						=  [len(lowercase__       ) for x in speech_inputs]
           a_     :							Dict     						=  feat_extract.model_input_names[0]

           a_     :							Optional[int]     						=  BatchFeature({input_name: speech_inputs}       )
           a_     :							Tuple     						=  min(lowercase__       )

           a_     :							List[Any]     						=  feat_extract.num_mel_bins  # hack!

           a_     :							str     						=  feat_extract.pad(
               lowercase__						,						padding="""max_length"""						,						max_length=lowercase__						,						truncation=lowercase__						,						return_tensors="""np"""       )
           self.assertIn("""attention_mask"""						,						lowercase__       )
           self.assertListEqual(
               list(processed_pad.attention_mask.shape       )						,						[processed_pad[input_name].shape[0], max_length]       )
           self.assertListEqual(
               processed_pad.attention_mask[:, :max_length].sum(-1       ).tolist()						,						[max_length for x in speech_inputs]       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  List[Any]       ):


           '''simple docstring'''

           from datasets import load_dataset

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

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






      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  torch.tensor(
               [2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
                3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
                2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
                4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
                7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
                4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3]       )
           # fmt: on

           a_     :							Dict     						=  self._load_datasamples(1       )
           a_     :							Tuple     						=  SpeechTaFeatureExtractor()
           a_     :							List[Any]     						=  feature_extractor(lowercase__						,						return_tensors="""pt"""       ).input_values
           self.assertEquals(input_values.shape						,						(1, 9_3680)       )
           self.assertTrue(torch.allclose(input_values[0, :30]						,						lowercase__						,						atol=1e-6       )       )






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

           a_     :							int     						=  torch.tensor(
               [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
                -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
                -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
                -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]       )
           # fmt: on

           a_     :							Optional[int]     						=  self._load_datasamples(1       )
           a_     :							Optional[Any]     						=  SpeechTaFeatureExtractor()
           a_     :							str     						=  feature_extractor(audio_target=lowercase__						,						return_tensors="""pt"""       ).input_values
           self.assertEquals(input_values.shape						,						(1, 366, 80)       )
           self.assertTrue(torch.allclose(input_values[0, 0, :30]						,						lowercase__						,						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()
lowerCAmelCase_      :  Optional[int]				     =  logging.get_logger(__name__)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							List[Any]     						=  DPTConfig(embedding_type="""hybrid"""				)

     if "large" in checkpoint_url:
          a_     :							str     						=  1024
          a_     :							List[str]     						=  4096
          a_     :							int     						=  24
          a_     :							int     						=  16
          a_     :							Optional[Any]     						=  [5, 11, 17, 23]
          a_     :							Optional[int]     						=  [256, 512, 1024, 1024]
          a_     :							Union[str, Any]     						=  (1, 384, 384)

     if "nyu" or "midas" in checkpoint_url:
          a_     :							Dict     						=  768
          a_     :							str     						=  [1, 1, 1, 0.5]
          a_     :							Dict     						=  [256, 512, 768, 768]
          a_     :							int     						=  150
          a_     :							Any     						=  16
          a_     :							Optional[int]     						=  (1, 384, 384)
          a_     :							Optional[Any]     						=  False
          a_     :							Dict     						=  """project"""

     if "ade" in checkpoint_url:
          a_     :							int     						=  True
          a_     :							int     						=  768
          a_     :							Optional[int]     						=  [1, 1, 1, 0.5]
          a_     :							Dict     						=  150
          a_     :							Any     						=  16
          a_     :							Optional[int]     						=  """huggingface/label-files"""
          a_     :							Optional[int]     						=  """ade20k-id2label.json"""
          a_     :							Union[str, Any]     						=  json.load(open(cached_download(hf_hub_url(UpperCamelCase__ ,    UpperCamelCase__ ,    repo_type="""dataset"""				)				) ,    """r"""				)				)
          a_     :							str     						=  {int(UpperCamelCase__				): v for k, v in idalabel.items()}
          a_     :							Optional[int]     						=  idalabel
          a_     :							Optional[Any]     						=  {v: k for k, v in idalabel.items()}
          a_     :							int     						=  [1, 150, 480, 480]

     return config, expected_shape



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int]				):




     """simple docstring"""


     a_     :							Dict     						=  ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase__ ,    UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str				):




     """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
     ):
          a_     :							List[str]     						=  name.replace("""pretrained.model""" ,    """dpt.encoder"""				)
     if "pretrained.model" in name:
          a_     :							Union[str, Any]     						=  name.replace("""pretrained.model""" ,    """dpt.embeddings"""				)
     if "patch_embed" in name:
          a_     :							Optional[int]     						=  name.replace("""patch_embed""" ,    """"""				)
     if "pos_embed" in name:
          a_     :							List[str]     						=  name.replace("""pos_embed""" ,    """position_embeddings"""				)
     if "attn.proj" in name:
          a_     :							Any     						=  name.replace("""attn.proj""" ,    """attention.output.dense"""				)
     if "proj" in name and "project" not in name:
          a_     :							Dict     						=  name.replace("""proj""" ,    """projection"""				)
     if "blocks" in name:
          a_     :							Union[str, Any]     						=  name.replace("""blocks""" ,    """layer"""				)
     if "mlp.fc1" in name:
          a_     :							Optional[int]     						=  name.replace("""mlp.fc1""" ,    """intermediate.dense"""				)
     if "mlp.fc2" in name:
          a_     :							Any     						=  name.replace("""mlp.fc2""" ,    """output.dense"""				)
     if "norm1" in name and "backbone" not in name:
          a_     :							List[Any]     						=  name.replace("""norm1""" ,    """layernorm_before"""				)
     if "norm2" in name and "backbone" not in name:
          a_     :							int     						=  name.replace("""norm2""" ,    """layernorm_after"""				)
     if "scratch.output_conv" in name:
          a_     :							Tuple     						=  name.replace("""scratch.output_conv""" ,    """head"""				)
     if "scratch" in name:
          a_     :							str     						=  name.replace("""scratch""" ,    """neck"""				)
     if "layer1_rn" in name:
          a_     :							Optional[Any]     						=  name.replace("""layer1_rn""" ,    """convs.0"""				)
     if "layer2_rn" in name:
          a_     :							Dict     						=  name.replace("""layer2_rn""" ,    """convs.1"""				)
     if "layer3_rn" in name:
          a_     :							Tuple     						=  name.replace("""layer3_rn""" ,    """convs.2"""				)
     if "layer4_rn" in name:
          a_     :							str     						=  name.replace("""layer4_rn""" ,    """convs.3"""				)
     if "refinenet" in name:
          a_     :							List[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_     :							List[str]     						=  name.replace(F"refinenet{layer_idx}" ,    F"fusion_stage.layers.{abs(layer_idx-4				)}"				)
     if "out_conv" in name:
          a_     :							Union[str, Any]     						=  name.replace("""out_conv""" ,    """projection"""				)
     if "resConfUnit1" in name:
          a_     :							Any     						=  name.replace("""resConfUnit1""" ,    """residual_layer1"""				)
     if "resConfUnit2" in name:
          a_     :							List[str]     						=  name.replace("""resConfUnit2""" ,    """residual_layer2"""				)
     if "conv1" in name:
          a_     :							str     						=  name.replace("""conv1""" ,    """convolution1"""				)
     if "conv2" in name:
          a_     :							Dict     						=  name.replace("""conv2""" ,    """convolution2"""				)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          a_     :							str     						=  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_     :							int     						=  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_     :							List[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_     :							List[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_     :							str     						=  name.replace("""pretrained.act_postprocess2.3""" ,    """neck.reassemble_stage.layers.1.projection"""				)
     if "pretrained.act_postprocess2.4" in name:
          a_     :							Optional[int]     						=  name.replace("""pretrained.act_postprocess2.4""" ,    """neck.reassemble_stage.layers.1.resize"""				)
     if "pretrained.act_postprocess3.3" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess3.3""" ,    """neck.reassemble_stage.layers.2.projection"""				)
     if "pretrained.act_postprocess4.3" in name:
          a_     :							List[Any]     						=  name.replace("""pretrained.act_postprocess4.3""" ,    """neck.reassemble_stage.layers.3.projection"""				)
     if "pretrained.act_postprocess4.4" in name:
          a_     :							Optional[Any]     						=  name.replace("""pretrained.act_postprocess4.4""" ,    """neck.reassemble_stage.layers.3.resize"""				)
     if "pretrained" in name:
          a_     :							int     						=  name.replace("""pretrained""" ,    """dpt"""				)
     if "bn" in name:
          a_     :							Any     						=  name.replace("""bn""" ,    """batch_norm"""				)
     if "head" in name:
          a_     :							int     						=  name.replace("""head""" ,    """head.head"""				)
     if "encoder.norm" in name:
          a_     :							Any     						=  name.replace("""encoder.norm""" ,    """layernorm"""				)
     if "auxlayer" in name:
          a_     :							Union[str, Any]     						=  name.replace("""auxlayer""" ,    """auxiliary_head.head"""				)
     if "backbone" in name:
          a_     :							Any     						=  name.replace("""backbone""" ,    """backbone.bit.encoder"""				)

     if ".." in name:
          a_     :							Any     						=  name.replace("""..""" ,    """."""				)

     if "stem.conv" in name:
          a_     :							Optional[int]     						=  name.replace("""stem.conv""" ,    """bit.embedder.convolution"""				)
     if "blocks" in name:
          a_     :							Dict     						=  name.replace("""blocks""" ,    """layers"""				)
     if "convolution" in name and "backbone" in name:
          a_     :							str     						=  name.replace("""convolution""" ,    """conv"""				)
     if "layer" in name and "backbone" in name:
          a_     :							Any     						=  name.replace("""layer""" ,    """layers"""				)
     if "backbone.bit.encoder.bit" in name:
          a_     :							Dict     						=  name.replace("""backbone.bit.encoder.bit""" ,    """backbone.bit"""				)
     if "embedder.conv" in name:
          a_     :							List[str]     						=  name.replace("""embedder.conv""" ,    """embedder.convolution"""				)
     if "backbone.bit.encoder.stem.norm" in name:
          a_     :							int     						=  name.replace("""backbone.bit.encoder.stem.norm""" ,    """backbone.bit.embedder.norm"""				)
     return name



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					str				):




     """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)
          a_     :							List[str]     						=  state_dict.pop(F"dpt.encoder.layer.{i}.attn.qkv.weight"				)
          a_     :							Optional[Any]     						=  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_     :							Any     						=  in_proj_weight[: config.hidden_size, :]
          a_     :							int     						=  in_proj_bias[: config.hidden_size]
          a_     :							List[Any]     						=  in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          a_     :							int     						=  in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          a_     :							Union[str, Any]     						=  in_proj_weight[
              -config.hidden_size :, :
          ]
          a_     :							str     						=  in_proj_bias[-config.hidden_size :]



def  _SCREAMING_SNAKE_CASE     (      ):




     """simple docstring"""


     a_     :							Optional[Any]     						=  """http://images.cocodataset.org/val2017/000000039769.jpg"""
     a_     :							Union[str, Any]     						=  Image.open(requests.get(UpperCamelCase__ ,    stream=UpperCamelCase__				).raw				)
     return im



@torch.no_grad()
def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Tuple ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Any				):




     """simple docstring"""


     a_						,     a_     :							Optional[Any]     						=  get_dpt_config(UpperCamelCase__				)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     a_     :							int     						=  torch.load(UpperCamelCase__ ,    map_location="""cpu"""				)
     # remove certain keys
     remove_ignore_keys_(UpperCamelCase__				)
     # rename keys
     for key in state_dict.copy().keys():
          a_     :							Any     						=  state_dict.pop(UpperCamelCase__				)
          a_     :							Tuple     						=  val
     # read in qkv matrices
     read_in_q_k_v(UpperCamelCase__ ,    UpperCamelCase__				)

     # load HuggingFace model
     a_     :							Any     						=  DPTForSemanticSegmentation(UpperCamelCase__				) if """ade""" in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__				)
     model.load_state_dict(UpperCamelCase__				)
     model.eval()

     # Check outputs on an image
     a_     :							str     						=  480 if """ade""" in checkpoint_url else 384
     a_     :							Optional[int]     						=  DPTImageProcessor(size=UpperCamelCase__				)

     a_     :							Dict     						=  prepare_img()
     a_     :							Any     						=  image_processor(UpperCamelCase__ ,    return_tensors="""pt"""				)

     # forward pass
     a_     :							Dict     						=  model(**UpperCamelCase__				).logits if """ade""" in checkpoint_url else model(**UpperCamelCase__				).predicted_depth

     if show_prediction:
          a_     :							Any     						=  (
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1				) ,    size=(image.size[1], image.size[0]) ,    mode="""bicubic""" ,    align_corners=UpperCamelCase__ ,    )
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 255				).show()

     if pytorch_dump_folder_path is not None:
          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:
          model.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)
          image_processor.push_to_hub("""ybelkada/dpt-hybrid-midas"""				)


if __name__ == "__main__":
 lowerCAmelCase_      :  Dict				     =  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=False,
     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.',
 )
 parser.add_argument(
     '--show_prediction',
     action='store_true',
 )

 lowerCAmelCase_      :  Optional[Any]				     =  parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )
