




import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_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 transformers import MobileNetVaForImageClassification, MobileNetVaModel
						from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import MobileNetVaImageProcessor







class  UpperCAmelCase(	UpperCamelCase_	):



	"""simple docstring"""






	def        __a      (	self							)   ->     Any:





								"""simple docstring"""


								lowercase__    :			Tuple     =				self.config_class(**self.inputs_dict							)
								self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			, "tf_padding"							)							)
								self.parent.assertTrue(hasattr(__SCREAMING_SNAKE_CASE			, "depth_multiplier"							)							)







class  UpperCAmelCase:



	"""simple docstring"""






	def __init__(	self			, lowerCamelCase			, lowerCamelCase=13			, lowerCamelCase=3			, lowerCamelCase=32			, lowerCamelCase=0.25			, lowerCamelCase=8			, lowerCamelCase=True			, lowerCamelCase=1024			, lowerCamelCase=32			, lowerCamelCase="relu6"			, lowerCamelCase=0.1			, lowerCamelCase=0.02			, lowerCamelCase=True			, lowerCamelCase=True			, lowerCamelCase=10			, lowerCamelCase=None			, )   ->     str:





								"""simple docstring"""


								lowercase__    :			List[str]     =				parent
								lowercase__    :			List[str]     =				batch_size
								lowercase__    :			Optional[Any]     =				num_channels
								lowercase__    :			Dict     =				image_size
								lowercase__    :			int     =				depth_multiplier
								lowercase__    :			int     =				min_depth
								lowercase__    :			Any     =				tf_padding
								lowercase__    :			List[str]     =				int(last_hidden_size * depth_multiplier							)
								lowercase__    :			List[Any]     =				output_stride
								lowercase__    :			Any     =				hidden_act
								lowercase__    :			List[Any]     =				classifier_dropout_prob
								lowercase__    :			Dict     =				use_labels
								lowercase__    :			int     =				is_training
								lowercase__    :			Optional[int]     =				num_labels
								lowercase__    :			List[str]     =				initializer_range
								lowercase__    :			int     =				scope

	def        __a      (	self							)   ->     Optional[Any]:





								"""simple docstring"""


								lowercase__    :			Optional[int]     =				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

								lowercase__    :			Tuple     =				None
								lowercase__    :			Optional[int]     =				None
								if self.use_labels:
															lowercase__    :			Tuple     =				ids_tensor([self.batch_size]			, self.num_labels							)
															lowercase__    :			Optional[Any]     =				ids_tensor([self.batch_size, self.image_size, self.image_size]			, self.num_labels							)

								lowercase__    :			Any     =				self.get_config()

								return config, pixel_values, labels, pixel_labels

	def        __a      (	self							)   ->     Any:





								"""simple docstring"""


								return MobileNetVaConfig(
								    num_channels=self.num_channels			, image_size=self.image_size			, depth_multiplier=self.depth_multiplier			, min_depth=self.min_depth			, tf_padding=self.tf_padding			, hidden_act=self.hidden_act			, classifier_dropout_prob=self.classifier_dropout_prob			, initializer_range=self.initializer_range			, )

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase							)   ->     str:





								"""simple docstring"""


								lowercase__    :			str     =				MobileNetVaModel(config=__SCREAMING_SNAKE_CASE							)
								model.to(__SCREAMING_SNAKE_CASE							)
								model.eval()
								lowercase__    :			Optional[Any]     =				model(__SCREAMING_SNAKE_CASE							)
								self.parent.assertEqual(
								    result.last_hidden_state.shape			, (
								        self.batch_size,
								        self.last_hidden_size,
								        self.image_size // self.output_stride,
								        self.image_size // self.output_stride,
								    )			, )

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase							)   ->     str:





								"""simple docstring"""


								lowercase__    :			str     =				self.num_labels
								lowercase__    :			int     =				MobileNetVaForImageClassification(__SCREAMING_SNAKE_CASE							)
								model.to(__SCREAMING_SNAKE_CASE							)
								model.eval()
								lowercase__    :			List[str]     =				model(__SCREAMING_SNAKE_CASE			, labels=__SCREAMING_SNAKE_CASE							)
								self.parent.assertEqual(result.logits.shape			, (self.batch_size, self.num_labels)							)







	def        __a      (	self							)   ->     int:





								"""simple docstring"""


								lowercase__    :			str     =				self.prepare_config_and_inputs()
								lowercase__    :			Dict     =				config_and_inputs
								lowercase__    :			Union[str, Any]     =				{'''pixel_values''': pixel_values}
								return config, inputs_dict







@require_torch
class  UpperCAmelCase(	UpperCamelCase_       ,      UpperCamelCase_       ,      unittest.TestCase	):



	"""simple docstring"""






	a      :   Union[str, Any]			    =	(MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
	a      :   int			    =	(
	    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
	    if is_torch_available()
	    else {}
	)

	a      :   Optional[Any]			    =	False
	a      :   int			    =	False
	a      :   List[Any]			    =	False
	a      :   str			    =	False

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





								"""simple docstring"""


								lowercase__    :			List[str]     =				MobileNetVaModelTester(self							)
								lowercase__    :			str     =				MobileNetVaConfigTester(self			, config_class=__SCREAMING_SNAKE_CASE			, has_text_modality=__SCREAMING_SNAKE_CASE							)

	def        __a      (	self							)   ->     int:





								"""simple docstring"""


								self.config_tester.run_common_tests()

	@unittest.skip(reason="MobileNetV1 does not use inputs_embeds"							)
	def        __a      (	self							)   ->     Tuple:





								"""simple docstring"""


								pass

	@unittest.skip(reason="MobileNetV1 does not support input and output embeddings"							)
	def        __a      (	self							)   ->     Union[str, Any]:





								"""simple docstring"""


								pass

	@unittest.skip(reason="MobileNetV1 does not output attentions"							)
	def        __a      (	self							)   ->     str:





								"""simple docstring"""


								pass

	def        __a      (	self							)   ->     Dict:





								"""simple docstring"""


								lowercase__    :			Optional[int]     =				self.model_tester.prepare_config_and_inputs_for_common()

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

															lowercase__    :			List[str]     =				['''pixel_values''']
															self.assertListEqual(arg_names[:1]			, __SCREAMING_SNAKE_CASE							)

	def        __a      (	self							)   ->     List[Any]:





								"""simple docstring"""


								lowercase__    :			Tuple     =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE							)

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





								"""simple docstring"""


								def check_hidden_states_output(lowerCamelCase			, lowerCamelCase			, lowerCamelCase							):
															lowercase__    :			int     =				model_class(__SCREAMING_SNAKE_CASE							)
															model.to(__SCREAMING_SNAKE_CASE							)
															model.eval()

															with torch.no_grad():
																						lowercase__    :			str     =				model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE							)							)

															lowercase__    :			Optional[Any]     =				outputs.hidden_states

															lowercase__    :			Optional[Any]     =				26
															self.assertEqual(len(__SCREAMING_SNAKE_CASE							)			, __SCREAMING_SNAKE_CASE							)

								lowercase__    :			Dict     =				self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
															lowercase__    :			Any     =				True
															check_hidden_states_output(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE							)

															# check that output_hidden_states also work using config
															del inputs_dict["output_hidden_states"]
															lowercase__    :			Tuple     =				True

															check_hidden_states_output(__SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE			, __SCREAMING_SNAKE_CASE							)

	def        __a      (	self							)   ->     Dict:





								"""simple docstring"""


								lowercase__    :			Optional[int]     =				self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE							)







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





								"""simple docstring"""


								for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															lowercase__    :			Optional[int]     =				MobileNetVaModel.from_pretrained(__SCREAMING_SNAKE_CASE							)
															self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)

def    snake_case_	(						)     ->					str:
							lowercase__    :			Any     =				Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
							return image







@require_torch
@require_vision
class  UpperCAmelCase(	unittest.TestCase	):



	"""simple docstring"""






	@cached_property
	def        __a      (	self							)   ->     Union[str, Any]:





								"""simple docstring"""


								return (
								    MobileNetVaImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224"							) if is_vision_available() else None
								)







	@slow
	def        __a      (	self							)   ->     Optional[int]:





								"""simple docstring"""


								lowercase__    :			List[str]     =				MobileNetVaForImageClassification.from_pretrained("google/mobilenet_v1_1.0_224"							).to(__SCREAMING_SNAKE_CASE							)

								lowercase__    :			Union[str, Any]     =				self.default_image_processor
								lowercase__    :			str     =				prepare_img()
								lowercase__    :			Any     =				image_processor(images=__SCREAMING_SNAKE_CASE			, return_tensors="pt"							).to(__SCREAMING_SNAKE_CASE							)

								# forward pass
								with torch.no_grad():
															lowercase__    :			Any     =				model(**__SCREAMING_SNAKE_CASE							)

								# verify the logits
								lowercase__    :			Tuple     =				torch.Size((1, 1001)							)
								self.assertEqual(outputs.logits.shape			, __SCREAMING_SNAKE_CASE							)

								lowercase__    :			Optional[Any]     =				torch.tensor([-4.17_39, -1.12_33, 3.12_05]							).to(__SCREAMING_SNAKE_CASE							)

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

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

      return new_state_dict


def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__		):
      # 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)
                  UpperCamelCase__     : Optional[Any]											=					state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight'''		)
                  UpperCamelCase__     : int											=					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
                  UpperCamelCase__     : Union[str, Any]											=					kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  UpperCamelCase__     : Tuple											=					kv_bias[: config.hidden_sizes[i]]
                  UpperCamelCase__     : Tuple											=					kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  UpperCamelCase__     : Optional[Any]											=					kv_bias[config.hidden_sizes[i] :]


def 				SCREAMING_SNAKE_CASE_   (      ):
      UpperCamelCase__     : int											=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
      UpperCamelCase__     : Any											=					Image.open(requests.get(UpperCamelCase__			,		stream=UpperCamelCase__		).raw		)

      return image


@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_   (      UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__=False			,		UpperCamelCase__=None		):
      UpperCamelCase__     : Any											=					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)
      UpperCamelCase__     : str											=					GLPNImageProcessor()

      # prepare image
      UpperCamelCase__     : List[str]											=					prepare_img()
      UpperCamelCase__     : List[str]											=					image_processor(images=UpperCamelCase__			,		return_tensors='''pt'''		).pixel_values

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

      # load original state dict
      UpperCamelCase__     : Tuple											=					torch.load(UpperCamelCase__			,		map_location=torch.device('''cpu'''		)		)

      # rename keys
      UpperCamelCase__     : Optional[Any]											=					rename_keys(UpperCamelCase__		)

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

      # create HuggingFace model and load state dict
      UpperCamelCase__     : str											=					GLPNForDepthEstimation(UpperCamelCase__		)
      model.load_state_dict(UpperCamelCase__		)
      model.eval()

      # forward pass
      UpperCamelCase__     : Union[str, Any]											=					model(UpperCamelCase__		)
      UpperCamelCase__     : Optional[int]											=					outputs.predicted_depth

      # verify output
      if model_name is not None:
            if "nyu" in model_name:
                  UpperCamelCase__     : Tuple											=					torch.tensor(
                      [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]]		)
            elif "kitti" in model_name:
                  UpperCamelCase__     : Union[str, Any]											=					torch.tensor(
                      [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]		)
            else:
                  raise ValueError(f'''Unknown model name: {model_name}'''		)

            UpperCamelCase__     : Optional[Any]											=					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)


