


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        __lowercase       :



		"""simple docstring"""

		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=[1, 3_84, 24, 24]		,      A=True		,      A=None		,      )  -> Tuple:







					'''simple docstring'''







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







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







					'''simple docstring'''







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

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

					lowerCamelCase    		=  self.get_config()

					return config, pixel_values, labels







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  {
					    """global_padding""": """same""",
					    """layer_type""": """bottleneck""",
					    """depths""": [3, 4, 9],
					    """out_features""": ["""stage1""", """stage2""", """stage3"""],
					    """embedding_dynamic_padding""": True,
					    """hidden_sizes""": [96, 1_92, 3_84, 7_68],
					    """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=A		,      initializer_range=self.initializer_range		,      is_hybrid=self.is_hybrid		,      backbone_config=A		,      backbone_featmap_shape=self.backbone_featmap_shape		,      )







		def 					__A   ( self		,      A		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A		,      A	)  -> int:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A		,      A	)  -> List[Any]:







					'''simple docstring'''







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







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







					'''simple docstring'''







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







@require_torch
class        __lowercase       (							a_			,							a_			,							unittest.TestCase  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[int]			       =		(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
		UpperCamelCase     :					Tuple			       =		(
		    {
		        "depth-estimation": DPTForDepthEstimation,
		        "feature-extraction": DPTModel,
		        "image-segmentation": DPTForSemanticSegmentation,
		    }
		    if is_torch_available()
		    else {}
		)

		UpperCamelCase     :					Optional[Any]			       =		False
		UpperCamelCase     :					Optional[int]			       =		False
		UpperCamelCase     :					Optional[int]			       =		False







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







					'''simple docstring'''







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







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







					'''simple docstring'''







					self.config_tester.run_common_tests()







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







					'''simple docstring'''







					pass







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







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase    		=  self.model_tester.prepare_config_and_inputs_for_common()

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







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







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase    		=  self.model_tester.prepare_config_and_inputs_for_common()

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

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







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







					'''simple docstring'''







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







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







					'''simple docstring'''







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







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







					'''simple docstring'''







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







		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







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

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

								if model_class in get_values(A	):
											continue

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







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







					'''simple docstring'''







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

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

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







		def 					__A   ( self	)  -> int:







					'''simple docstring'''







					lowerCamelCase		,		lowerCamelCase    		=  self.model_tester.prepare_config_and_inputs_for_common()

					lowerCamelCase    		=  _config_zero_init(A	)
					for model_class in self.all_model_classes:
								lowerCamelCase    		=  model_class(config=A	)
								# Skip the check for the backbone
								lowerCamelCase    		=  []
								for name, module in model.named_modules():
											if module.__class__.__name__ == "DPTViTHybridEmbeddings":
														lowerCamelCase    		=  [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 					__A   ( self	)  -> List[str]:







					'''simple docstring'''







					pass







		@slow
		def 					__A   ( self	)  -> Any:







					'''simple docstring'''







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







		def 					__A   ( self	)  -> Union[str, Any]:







					'''simple docstring'''







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






def 	__lowerCamelCase						(							):





			'''simple docstring'''




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







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



		"""simple docstring"""

		def 					__A   ( self	)  -> Dict:







					'''simple docstring'''







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

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

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

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

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

					self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00		,      A		,      atol=1e-4	)	)





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

