
'''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()
__A								=			logging.get_logger(__name__)





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

						return new_state_dict





def        _A						(							lowercase__						,						lowercase__		):
						# 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)
																		lowercase__							       =				state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
																		lowercase__							       =				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
																		lowercase__							       =				kv_weight[
																		    : config.hidden_sizes[i], :
																		]
																		lowercase__							       =				kv_bias[: config.hidden_sizes[i]]
																		lowercase__							       =				kv_weight[
																		    config.hidden_sizes[i] :, :
																		]
																		lowercase__							       =				kv_bias[config.hidden_sizes[i] :]





def        _A						(							):
						lowercase__							       =				'http://images.cocodataset.org/val2017/000000039769.jpg'
						lowercase__							       =				Image.open(requests.get(_SCREAMING_SNAKE_CASE						,						stream=_SCREAMING_SNAKE_CASE		).raw		)

						return image





@torch.no_grad()
def        _A						(							lowercase__						,						lowercase__						,						lowercase__=False						,						lowercase__=None		):
						lowercase__							       =				GLPNConfig(hidden_sizes=[64, 128, 320, 512]						,						decoder_hidden_size=64						,						depths=[3, 8, 27, 3]		)

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

						# prepare image
						lowercase__							       =				prepare_img()
						lowercase__							       =				image_processor(images=_SCREAMING_SNAKE_CASE						,						return_tensors="""pt"""		).pixel_values

						logger.info("""Converting model..."""		)

						# load original state dict
						lowercase__							       =				torch.load(_SCREAMING_SNAKE_CASE						,						map_location=torch.device("""cpu"""		)		)

						# rename keys
						lowercase__							       =				rename_keys(_SCREAMING_SNAKE_CASE		)

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

						# create HuggingFace model and load state dict
						lowercase__							       =				GLPNForDepthEstimation(_SCREAMING_SNAKE_CASE		)
						model.load_state_dict(_SCREAMING_SNAKE_CASE		)
						model.eval()

						# forward pass
						lowercase__							       =				model(_SCREAMING_SNAKE_CASE		)
						lowercase__							       =				outputs.predicted_depth

						# verify output
						if model_name is not None:
												if "nyu" in model_name:
																		lowercase__							       =				torch.tensor(
																		    [[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]]		)
												elif "kitti" in model_name:
																		lowercase__							       =				torch.tensor(
																		    [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]		)
												else:
																		raise ValueError(f'''Unknown model name: {model_name}'''		)

												lowercase__							       =				torch.Size([1, 480, 640]		)

												assert predicted_depth.shape == expected_shape
												assert torch.allclose(predicted_depth[0, :3, :3]						,						_SCREAMING_SNAKE_CASE						,						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(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE		)						,						organization="""nielsr"""						,						commit_message="""Add model"""						,						use_temp_dir=_SCREAMING_SNAKE_CASE						,						)
												image_processor.push_to_hub(
												    repo_path_or_name=Path(_SCREAMING_SNAKE_CASE						,						_SCREAMING_SNAKE_CASE		)						,						organization="""nielsr"""						,						commit_message="""Add image processor"""						,						use_temp_dir=_SCREAMING_SNAKE_CASE						,						)


if __name__ == "__main__":
				__A								=			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.",
				)
				__A								=			parser.parse_args()
				convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase__					       =	random.Random()







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=1.0		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None   )	->Optional[int]:
  if rng is None:
    a__:     Any										=	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




class    __snake_case       (     unittest.TestCase				):



      def __init__(						self    ,			lowercase    ,			lowercase=7    ,			lowercase=4_00    ,			lowercase=20_00    ,			lowercase=1    ,			lowercase=0.0    ,			lowercase=1_60_00    ,			lowercase=True    ,			lowercase=True    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	parent
        a__:     Optional[int]										=	batch_size
        a__:     Optional[Any]										=	min_seq_length
        a__:     Optional[int]										=	max_seq_length
        a__:     Tuple										=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__:     Dict										=	feature_size
        a__:     Any										=	padding_value
        a__:     Optional[Any]										=	sampling_rate
        a__:     Optional[Any]										=	return_attention_mask
        a__:     str										=	do_normalize



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }



      def   lowerCamelCase_   (						self    ,			lowercase=False    ,			lowercase=False)	->			Tuple:





        '''simple docstring'''





        def _flatten(lowercase):
          return list(itertools.chain(*lowercase))

        if equal_length:
          a__:     Dict										=	floats_list((self.batch_size, self.max_seq_length))
        else:
          # make sure that inputs increase in size
          a__:     List[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__:     str										=	[np.asarray(lowercase) for x in speech_inputs]

        return speech_inputs







class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		WavaVecaFeatureExtractor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[int]										=	WavaVecaFeatureExtractionTester(self)



      def   lowerCamelCase_   (						self    ,			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   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     List[Any]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:     Optional[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     List[str]										=	[np.asarray(lowercase) for speech_input in speech_inputs]

        # Test not batched input
        a__:     Optional[Any]										=	feat_extract(speech_inputs[0]    ,			return_tensors='np').input_values
        a__:     Dict										=	feat_extract(np_speech_inputs[0]    ,			return_tensors='np').input_values
        self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))

        # Test batched
        a__:     Dict										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     int										=	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))

        # Test 2-D numpy arrays are batched.
        a__:     int										=	[floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
        a__:     Union[str, Any]										=	np.asarray(lowercase)
        a__:     int										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     Any										=	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   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     List[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]

        a__:     Optional[int]										=	['longest', 'max_length', 'do_not_pad']
        a__:     List[Any]										=	[None, 16_00, None]
        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     Dict										=	feat_extract(lowercase    ,			padding=lowercase    ,			max_length=lowercase    ,			return_tensors='np')
          a__:     Union[str, Any]										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     str										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Optional[int]										=	range(8_00    ,			14_00    ,			2_00)
        a__:     List[str]										=	[floats_list((1, x))[0] for x in lengths]

        a__:     Tuple										=	['longest', 'max_length', 'do_not_pad']
        a__:     Dict										=	[None, 16_00, None]

        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     int										=	feat_extract(lowercase    ,			max_length=lowercase    ,			padding=lowercase)
          a__:     Any										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Any										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Any										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Dict										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='max_length'    ,			return_tensors='np')
        a__:     int										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1])
        self._check_zero_mean_unit_variance(input_values[2])



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     int										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     str										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     Any										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        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, 10_00))

        a__:     Dict										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Tuple										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=20_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     str										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        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, 12_00))



      @require_torch
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        import torch

        a__:     Optional[int]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Tuple										=	np.random.rand(1_00).astype(np.floataa)
        a__:     Tuple										=	np_speech_inputs.tolist()

        for inputs in [py_speech_inputs, np_speech_inputs]:
          a__:     Any										=	feature_extractor.pad([{'input_values': inputs}]    ,			return_tensors='np')
          self.assertTrue(np_processed.input_values.dtype == np.floataa)
          a__:     Optional[Any]										=	feature_extractor.pad([{'input_values': inputs}]    ,			return_tensors='pt')
          self.assertTrue(pt_processed.input_values.dtype == torch.floataa)



      @slow
      @require_torch
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          a__:     str										=	WavaVecaConfig.from_pretrained(lowercase)
          a__:     str										=	WavaVecaFeatureExtractor.from_pretrained(lowercase)

          # only "layer" feature extraction norm should make use of
          # attention_mask
          self.assertEqual(feat_extract.return_attention_mask    ,			config.feat_extract_norm == 'layer')


