



import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch
	from torch import nn

	from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
	from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
	from PIL import Image

	from transformers import DPTImageProcessor

class   _A   :


						'''simple docstring'''





						def __init__(	self	:							List[Any]							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							List[Any]=2							,      lowerCamelCase	:							Optional[Any]=32							,      lowerCamelCase	:							Union[str, Any]=16							,      lowerCamelCase	:							Union[str, Any]=3							,      lowerCamelCase	:							Optional[Any]=True							,      lowerCamelCase	:							Optional[int]=True							,      lowerCamelCase	:							Dict=32							,      lowerCamelCase	:							Any=4							,      lowerCamelCase	:							List[Any]=[0, 1, 2, 3]							,      lowerCamelCase	:							Union[str, Any]=4							,      lowerCamelCase	:							Dict=37							,      lowerCamelCase	:							List[str]="gelu"							,      lowerCamelCase	:							Optional[int]=0.1							,      lowerCamelCase	:							Tuple=0.1							,      lowerCamelCase	:							Dict=0.02							,      lowerCamelCase	:							str=3							,      lowerCamelCase	:							int=[1, 384, 24, 24]							,      lowerCamelCase	:							Optional[Any]=True							,      lowerCamelCase	:							Union[str, Any]=None							,      ):






							'''simple docstring'''



							__lowercase		       =			parent
							__lowercase		       =			batch_size
							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			is_training
							__lowercase		       =			use_labels
							__lowercase		       =			hidden_size
							__lowercase		       =			num_hidden_layers
							__lowercase		       =			backbone_out_indices
							__lowercase		       =			num_attention_heads
							__lowercase		       =			intermediate_size
							__lowercase		       =			hidden_act
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			initializer_range
							__lowercase		       =			num_labels
							__lowercase		       =			backbone_featmap_shape
							__lowercase		       =			scope
							__lowercase		       =			is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							__lowercase		       =			(image_size // patch_size) ** 2
							__lowercase		       =			num_patches + 1



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

							__lowercase		       =			None
							if self.use_labels:
								__lowercase		       =			ids_tensor([self.batch_size, self.image_size, self.image_size]							,      self.num_labels	)

							__lowercase		       =			self.get_config()

							return config, pixel_values, labels



						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							__lowercase		       =			{
							    "global_padding": "same",
							    "layer_type": "bottleneck",
							    "depths": [3, 4, 9],
							    "out_features": ["stage1", "stage2", "stage3"],
							    "embedding_dynamic_padding": True,
							    "hidden_sizes": [96, 192, 384, 768],
							    "num_groups": 2,
							}

							return DPTConfig(
							    image_size=self.image_size							,      patch_size=self.patch_size							,      num_channels=self.num_channels							,      hidden_size=self.hidden_size							,      num_hidden_layers=self.num_hidden_layers							,      backbone_out_indices=self.backbone_out_indices							,      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=lowerCamelCase							,      initializer_range=self.initializer_range							,      is_hybrid=self.is_hybrid							,      backbone_config=lowerCamelCase							,      backbone_featmap_shape=self.backbone_featmap_shape							,      )



						def     _snake_case	(	self	:							Tuple							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							Dict	):






							'''simple docstring'''



							__lowercase		       =			DPTModel(config=lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase	)
							self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.seq_length, self.hidden_size)	)



						def     _snake_case	(	self	:							List[Any]							,      lowerCamelCase	:							Any							,      lowerCamelCase	:							Optional[Any]							,      lowerCamelCase	:							Any	):






							'''simple docstring'''



							__lowercase		       =			self.num_labels
							__lowercase		       =			DPTForDepthEstimation(lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase	)
							self.parent.assertEqual(result.predicted_depth.shape							,      (self.batch_size, self.image_size, self.image_size)	)



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							Union[str, Any]							,      lowerCamelCase	:							Dict							,      lowerCamelCase	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			self.num_labels
							__lowercase		       =			DPTForSemanticSegmentation(lowerCamelCase	)
							model.to(lowerCamelCase	)
							model.eval()
							__lowercase		       =			model(lowerCamelCase							,      labels=lowerCamelCase	)
							self.parent.assertEqual(
							    result.logits.shape							,      (self.batch_size, self.num_labels, self.image_size, self.image_size)	)



						def     _snake_case	(	self	:							str	):






							'''simple docstring'''



							__lowercase		       =			self.prepare_config_and_inputs()
							__lowercase       ,    __lowercase       ,    __lowercase		       =			config_and_inputs
							__lowercase		       =			{"pixel_values": pixel_values}
							return config, inputs_dict

@require_torch
class   _A   ( _lowercase  ,						_lowercase  ,						unittest.TestCase	):


						'''simple docstring'''





						_snake_case			:     Tuple      							=      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						_snake_case			:     Dict      							=      (
						    {
						        """depth-estimation""": DPTForDepthEstimation,
						        """feature-extraction""": DPTModel,
						        """image-segmentation""": DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						_snake_case			:     Tuple      							=      False
						_snake_case			:     Tuple      							=      False
						_snake_case			:     List[Any]      							=      False



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			DPTModelTester(self	)
							__lowercase		       =			ConfigTester(self							,      config_class=lowerCamelCase							,      has_text_modality=lowerCamelCase							,      hidden_size=37	)



						def     _snake_case	(	self	:							Any	):






							'''simple docstring'''



							self.config_tester.run_common_tests()



						@unittest.skip(reason="DPT does not use inputs_embeds"	)
						def     _snake_case	(	self	:							Optional[int]	):






							'''simple docstring'''



							pass



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

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



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

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

								__lowercase		       =			["pixel_values"]
								self.assertListEqual(arg_names[:1]							,      lowerCamelCase	)



						def     _snake_case	(	self	:							List[str]	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase	)



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






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase		       =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase	)



						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							for model_class in self.all_model_classes:
								if model_class.__name__ == "DPTForDepthEstimation":
									continue

								__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()
								__lowercase		       =			True

								if model_class in get_values(lowerCamelCase	):
									continue

								__lowercase		       =			model_class(lowerCamelCase	)
								model.to(lowerCamelCase	)
								model.train()
								__lowercase		       =			self._prepare_for_class(lowerCamelCase							,      lowerCamelCase							,      return_labels=lowerCamelCase	)
								__lowercase		       =			model(**lowerCamelCase	).loss
								loss.backward()



						def     _snake_case	(	self	:							int	):






							'''simple docstring'''



							for model_class in self.all_model_classes:
								if model_class.__name__ == "DPTForDepthEstimation":
									continue

								__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()
								__lowercase		       =			False
								__lowercase		       =			True

								if model_class in get_values(lowerCamelCase	) or not model_class.supports_gradient_checkpointing:
									continue
								__lowercase		       =			model_class(lowerCamelCase	)
								model.to(lowerCamelCase	)
								model.gradient_checkpointing_enable()
								model.train()
								__lowercase		       =			self._prepare_for_class(lowerCamelCase							,      lowerCamelCase							,      return_labels=lowerCamelCase	)
								__lowercase		       =			model(**lowerCamelCase	).loss
								loss.backward()



						def     _snake_case	(	self	:							Optional[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()

							__lowercase		       =			_config_zero_init(lowerCamelCase	)
							for model_class in self.all_model_classes:
								__lowercase		       =			model_class(config=lowerCamelCase	)
								# Skip the check for the backbone
								__lowercase		       =			[]
								for name, module in model.named_modules():
									if module.__class__.__name__ == "DPTViTHybridEmbeddings":
										__lowercase		       =			[f"""{name}.{key}""" for key in module.state_dict().keys()]
										break

								for name, param in model.named_parameters():
									if param.requires_grad:
										if name in backbone_params:
											continue
										self.assertIn(
										    ((param.data.mean() * 1e9).round() / 1e9).item()							,      [0.0, 1.0]							,      msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""							,      )



						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."	)
						def     _snake_case	(	self	:							Tuple	):






							'''simple docstring'''



							pass



						@slow
						def     _snake_case	(	self	:							Dict	):






							'''simple docstring'''



							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
								__lowercase		       =			DPTModel.from_pretrained(lowerCamelCase	)
								self.assertIsNotNone(lowerCamelCase	)



						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase       ,    __lowercase		       =			self.model_tester.prepare_config_and_inputs_for_common()
							__lowercase		       =			"add"
							with self.assertRaises(lowerCamelCase	):
								__lowercase		       =			DPTForDepthEstimation(lowerCamelCase	)



def    snake_case_				(    ):
	__lowercase		       =			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
	return image

@require_torch
@require_vision
@slow
class   _A   ( unittest.TestCase	):


						'''simple docstring'''





						def     _snake_case	(	self	:							List[Any]	):






							'''simple docstring'''



							__lowercase		       =			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
							__lowercase		       =			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(lowerCamelCase	)

							__lowercase		       =			prepare_img()
							__lowercase		       =			image_processor(images=lowerCamelCase							,      return_tensors="pt"	).to(lowerCamelCase	)

							# forward pass
							with torch.no_grad():
								__lowercase		       =			model(**lowerCamelCase	)
								__lowercase		       =			outputs.predicted_depth

							# verify the predicted depth
							__lowercase		       =			torch.Size((1, 384, 384)	)
							self.assertEqual(predicted_depth.shape							,      lowerCamelCase	)

							__lowercase		       =			torch.tensor(
							    [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]	).to(lowerCamelCase	)

							self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100							,      lowerCamelCase							,      atol=1e-4	)	)








import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__					:     Optional[int]    		=   logging.get_logger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

	if "_quant" in model_name:
		raise ValueError("Quantized models are not supported."   )

	__lowercase		       =			re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"  ,	_SCREAMING_SNAKE_CASE   )
	if matches:
		__lowercase		       =			float(matches[1]   )
		__lowercase		       =			int(matches[2]   )

	# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
	# the usual 1000. The first class (index 0) is "background".
	__lowercase		       =			1_0_0_1
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ) + 1: v for k, v in idalabel.items()}
	__lowercase		       =			"background"
	__lowercase		       =			idalabel
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	return config



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_mobilenet_va_config(_SCREAMING_SNAKE_CASE   )

	# Load 🤗 model
	__lowercase		       =			MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()

	# Load weights from TensorFlow checkpoint
	load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	# Check outputs on an image, prepared by MobileNetV1ImageProcessor
	__lowercase		       =			MobileNetVaImageProcessor(
	    crop_size={"width": config.image_size, "height": config.image_size}  ,	size={"shortest_edge": config.image_size + 3_2}  ,	)
	__lowercase		       =			image_processor(images=prepare_img()  ,	return_tensors="pt"   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			outputs.logits

	assert logits.shape == (1, 1_0_0_1)

	if model_name == "mobilenet_v1_1.0_224":
		__lowercase		       =			torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   )
	elif model_name == "mobilenet_v1_0.75_192":
		__lowercase		       =			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
	else:
		__lowercase		       =			None

	if expected_logits is not None:
		assert torch.allclose(logits[0, :3]  ,	_SCREAMING_SNAKE_CASE  ,	atol=1E-4   )

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

	if push_to_hub:
		print("Pushing to the hub..."   )
		__lowercase		       =			"google/" + model_name
		image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   )
		model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""mobilenet_v1_1.0_224""",
	    type=str,
	    help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
	)
	parser.add_argument(
	    """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	snake_case__					:     Dict    		=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)



