





"""simple docstring"""





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 							_lowerCamelCase   :


						def __init__(self ,				__a ,				__a=2 ,				__a=32 ,				__a=16 ,				__a=3 ,				__a=True ,				__a=True ,				__a=32 ,				__a=4 ,				__a=[0, 1, 2, 3] ,				__a=4 ,				__a=37 ,				__a="gelu" ,				__a=0.1 ,				__a=0.1 ,				__a=0.02 ,				__a=3 ,				__a=False ,				__a=None ,				)			-> List[str]:
								UpperCamelCase =						parent
								UpperCamelCase =						batch_size
								UpperCamelCase =						image_size
								UpperCamelCase =						patch_size
								UpperCamelCase =						num_channels
								UpperCamelCase =						is_training
								UpperCamelCase =						use_labels
								UpperCamelCase =						hidden_size
								UpperCamelCase =						num_hidden_layers
								UpperCamelCase =						backbone_out_indices
								UpperCamelCase =						num_attention_heads
								UpperCamelCase =						intermediate_size
								UpperCamelCase =						hidden_act
								UpperCamelCase =						hidden_dropout_prob
								UpperCamelCase =						attention_probs_dropout_prob
								UpperCamelCase =						initializer_range
								UpperCamelCase =						num_labels
								UpperCamelCase =						scope
								UpperCamelCase =						is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCamelCase =						(image_size // patch_size) ** 2
								UpperCamelCase =						num_patches + 1


						def      snake_case_ (self		)			-> Union[str, Any]:
								UpperCamelCase =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

								UpperCamelCase =						self.get_config()

								return config, pixel_values, labels


						def      snake_case_ (self		)			-> int:
								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=__a ,				initializer_range=self.initializer_range ,				is_hybrid=self.is_hybrid ,				)


						def      snake_case_ (self ,				__a ,				__a ,				__a		)			-> Optional[int]:
								UpperCamelCase =						DPTModel(config=__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase =						model(__a		)
								self.parent.assertEqual(result.last_hidden_state.shape ,				(self.batch_size, self.seq_length, self.hidden_size)		)


						def      snake_case_ (self ,				__a ,				__a ,				__a		)			-> Dict:
								UpperCamelCase =						self.num_labels
								UpperCamelCase =						DPTForDepthEstimation(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase =						model(__a		)
								self.parent.assertEqual(result.predicted_depth.shape ,				(self.batch_size, self.image_size, self.image_size)		)


						def      snake_case_ (self ,				__a ,				__a ,				__a		)			-> List[str]:
								UpperCamelCase =						self.num_labels
								UpperCamelCase =						DPTForSemanticSegmentation(__a		)
								model.to(__a		)
								model.eval()
								UpperCamelCase =						model(__a ,				labels=__a		)
								self.parent.assertEqual(
								    result.logits.shape ,				(self.batch_size, self.num_labels, self.image_size, self.image_size)		)




						def      snake_case_ (self		)			-> Tuple:
								UpperCamelCase =						self.prepare_config_and_inputs()
								UpperCamelCase ,					UpperCamelCase ,					UpperCamelCase =						config_and_inputs
								UpperCamelCase =						{"pixel_values": pixel_values}
								return config, inputs_dict





@require_torch
class 							_lowerCamelCase   (							_lowercase	,      _lowercase	,      unittest.TestCase			):
						UpperCAmelCase_			=      (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						UpperCAmelCase_			=      (
						    {
						        "depth-estimation": DPTForDepthEstimation,
						        "feature-extraction": DPTModel,
						        "image-segmentation": DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						UpperCAmelCase_			=      False
						UpperCAmelCase_			=      False
						UpperCAmelCase_			=      False


						def      snake_case_ (self		)			-> Tuple:
								UpperCamelCase =						DPTModelTester(self		)
								UpperCamelCase =						ConfigTester(self ,				config_class=__a ,				has_text_modality=__a ,				hidden_size=37		)


						def      snake_case_ (self		)			-> Tuple:
								self.config_tester.run_common_tests()


						@unittest.skip(reason="DPT does not use inputs_embeds"		)
						def      snake_case_ (self		)			-> int:
								pass


						def      snake_case_ (self		)			-> Any:
								UpperCamelCase ,					UpperCamelCase =						self.model_tester.prepare_config_and_inputs_for_common()

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


						def      snake_case_ (self		)			-> Dict:
								UpperCamelCase ,					UpperCamelCase =						self.model_tester.prepare_config_and_inputs_for_common()

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

										UpperCamelCase =						["pixel_values"]
										self.assertListEqual(arg_names[:1] ,				__a		)


						def      snake_case_ (self		)			-> List[str]:
								UpperCamelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*__a		)


						def      snake_case_ (self		)			-> Any:
								UpperCamelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*__a		)


						def      snake_case_ (self		)			-> List[str]:
								UpperCamelCase =						self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*__a		)


						def      snake_case_ (self		)			-> Optional[int]:
								for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
												continue

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

										if model_class in get_values(__a		):
												continue

										UpperCamelCase =						model_class(__a		)
										model.to(__a		)
										model.train()
										UpperCamelCase =						self._prepare_for_class(__a ,				__a ,				return_labels=__a		)
										UpperCamelCase =						model(**__a		).loss
										loss.backward()


						def      snake_case_ (self		)			-> int:
								for model_class in self.all_model_classes:
										if model_class.__name__ == "DPTForDepthEstimation":
												continue

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

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


						def      snake_case_ (self		)			-> Union[str, Any]:
								UpperCamelCase ,					UpperCamelCase =						self.model_tester.prepare_config_and_inputs_for_common()

								UpperCamelCase =						_config_zero_init(__a		)
								for model_class in self.all_model_classes:
										UpperCamelCase =						model_class(config=__a		)
										# Skip the check for the backbone
										UpperCamelCase =						[]
										for name, module in model.named_modules():
												if module.__class__.__name__ == "DPTViTHybridEmbeddings":
														UpperCamelCase =						[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		)			-> Optional[int]:
								pass




						@slow
						def      snake_case_ (self		)			-> List[Any]:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										UpperCamelCase =						DPTModel.from_pretrained(__a		)
										self.assertIsNotNone(__a		)



def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
		return image






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


						def      snake_case_ (self		)			-> Optional[Any]:
								UpperCamelCase =						DPTImageProcessor.from_pretrained("Intel/dpt-large"		)
								UpperCamelCase =						DPTForDepthEstimation.from_pretrained("Intel/dpt-large"		).to(__a		)

								UpperCamelCase =						prepare_img()
								UpperCamelCase =						image_processor(images=__a ,				return_tensors="pt"		).to(__a		)

								# forward pass
								with torch.no_grad():
										UpperCamelCase =						model(**__a		)
										UpperCamelCase =						outputs.predicted_depth

								# verify the predicted depth
								UpperCamelCase =						torch.Size((1, 3_84, 3_84)		)
								self.assertEqual(predicted_depth.shape ,				__a		)

								UpperCamelCase =						torch.tensor(
								    [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]		).to(__a		)

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


						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase =						DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"		)
								UpperCamelCase =						DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"		).to(__a		)

								UpperCamelCase =						prepare_img()
								UpperCamelCase =						image_processor(images=__a ,				return_tensors="pt"		).to(__a		)

								# forward pass
								with torch.no_grad():
										UpperCamelCase =						model(**__a		)

								# verify the logits
								UpperCamelCase =						torch.Size((1, 1_50, 4_80, 4_80)		)
								self.assertEqual(outputs.logits.shape ,				__a		)

								UpperCamelCase =						torch.tensor(
								    [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]		).to(__a		)

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




						def      snake_case_ (self		)			-> Optional[Any]:
								UpperCamelCase =						DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"		)
								UpperCamelCase =						DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"		).to(__a		)

								UpperCamelCase =						prepare_img()
								UpperCamelCase =						image_processor(images=__a ,				return_tensors="pt"		).to(__a		)

								# forward pass
								with torch.no_grad():
										UpperCamelCase =						model(**__a		)

								UpperCamelCase =						outputs.logits.detach().cpu()

								UpperCamelCase =						image_processor.post_process_semantic_segmentation(outputs=__a ,				target_sizes=[(5_00, 3_00)]		)
								UpperCamelCase =						torch.Size((5_00, 3_00)		)
								self.assertEqual(segmentation[0].shape ,				__a		)

								UpperCamelCase =						image_processor.post_process_semantic_segmentation(outputs=__a		)
								UpperCamelCase =						torch.Size((4_80, 4_80)		)
								self.assertEqual(segmentation[0].shape ,				__a		)













"""simple docstring"""





import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser


def    a__       (       _SCREAMING_SNAKE_CASE=None	,						_SCREAMING_SNAKE_CASE=None     ):




		"""simple docstring"""


		return field(default_factory=lambda: default	,						metadata=_SCREAMING_SNAKE_CASE     )





@dataclass
class 							_lowerCamelCase   :
						UpperCAmelCase_			=      field(
						    metadata={"help": "The csv file to plot."}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Disable logarithmic scale when plotting"}	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={
						        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
						    }	,      )
						UpperCAmelCase_			=      field(
						    default=_lowercase	,      metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}	,      )
						UpperCAmelCase_			=      list_field(
						    default=_lowercase	,      metadata={"help": "List of model names that are used instead of the ones in the csv file."}			)



def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		try:
				int(_SCREAMING_SNAKE_CASE     )
				return True
		except ValueError:
				return False


def    a__       (       _SCREAMING_SNAKE_CASE     ):




		"""simple docstring"""


		try:
				float(_SCREAMING_SNAKE_CASE     )
				return True
		except ValueError:
				return False





class 							_lowerCamelCase   :


						def __init__(self ,				__a		)			-> Tuple:
								UpperCamelCase =						args
								UpperCamelCase =						defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}		)

								with open(self.args.csv_file ,				newline=""		) as csv_file:
										UpperCamelCase =						csv.DictReader(__a		)
										for row in reader:
												UpperCamelCase =						row["model"]
												self.result_dict[model_name]["bsz"].append(int(row["batch_size"]		)		)
												self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]		)		)
												if can_convert_to_int(row["result"]		):
														# value is not None
														UpperCamelCase =						int(row["result"]		)
												elif can_convert_to_float(row["result"]		):
														# value is not None
														UpperCamelCase =						float(row["result"]		)




						def      snake_case_ (self		)			-> Optional[int]:
								UpperCamelCase ,					UpperCamelCase =						plt.subplots()
								UpperCamelCase =						"Time usage" if self.args.is_time else "Memory usage"
								UpperCamelCase =						title_str + " for training" if self.args.is_train else title_str + " for inference"

								if not self.args.no_log_scale:
										# set logarithm scales
										ax.set_xscale("log"		)
										ax.set_yscale("log"		)

								for axis in [ax.xaxis, ax.yaxis]:
										axis.set_major_formatter(ScalarFormatter()		)

								for model_name_idx, model_name in enumerate(self.result_dict.keys()		):
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["bsz"]		)		)
										UpperCamelCase =						sorted(set(self.result_dict[model_name]["seq_len"]		)		)
										UpperCamelCase =						self.result_dict[model_name]["result"]

										((UpperCamelCase) ,					(UpperCamelCase)) =						(
										    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
										)

										UpperCamelCase =						(
										    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
										)

										for inner_loop_value in inner_loop_array:
												if self.args.plot_along_batch:
														UpperCamelCase =						np.asarray(
														    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results] ,				dtype=__a ,				)
												else:
														UpperCamelCase =						np.asarray(
														    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results] ,				dtype=np.floataa ,				)

												((UpperCamelCase) ,					(UpperCamelCase)) =						(
												    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
												)

												UpperCamelCase =						np.asarray(__a ,				__a		)[: len(__a		)]
												plt.scatter(
												    __a ,				__a ,				label=F"{label_model_name} - {inner_loop_label}: {inner_loop_value}"		)
												plt.plot(__a ,				__a ,				"--"		)

										title_str += F" {label_model_name} vs."

								UpperCamelCase =						title_str[:-4]
								UpperCamelCase =						"Time in s" if self.args.is_time else "Memory in MB"

								# plot
								plt.title(__a		)
								plt.xlabel(__a		)
								plt.ylabel(__a		)
								plt.legend()

								if self.args.figure_png_file is not None:
										plt.savefig(self.args.figure_png_file		)
								else:
										plt.show()



def    a__       (       ):




		"""simple docstring"""


		UpperCamelCase =						HfArgumentParser(_SCREAMING_SNAKE_CASE     )
		UpperCamelCase =						parser.parse_args_into_dataclasses()[0]
		UpperCamelCase =						Plot(args=_SCREAMING_SNAKE_CASE     )
		plot.plot()


if __name__ == "__main__":
						main()






