



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   snake_case_ :



       def __init__(     self     ,lowercase     ,lowercase=2     ,lowercase=32     ,lowercase=16     ,lowercase=3     ,lowercase=True     ,lowercase=True     ,lowercase=32     ,lowercase=4     ,lowercase=[0, 1, 2, 3]     ,lowercase=4     ,lowercase=37     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=0.02     ,lowercase=3     ,lowercase=[1, 384, 24, 24]     ,lowercase=True     ,lowercase=None     ,):

             """simple docstring"""





             UpperCAmelCase_  :	Any				      =		parent
             UpperCAmelCase_  :	Any				      =		batch_size
             UpperCAmelCase_  :	str				      =		image_size
             UpperCAmelCase_  :	Tuple				      =		patch_size
             UpperCAmelCase_  :	Any				      =		num_channels
             UpperCAmelCase_  :	Optional[int]				      =		is_training
             UpperCAmelCase_  :	Dict				      =		use_labels
             UpperCAmelCase_  :	Union[str, Any]				      =		hidden_size
             UpperCAmelCase_  :	List[str]				      =		num_hidden_layers
             UpperCAmelCase_  :	Any				      =		backbone_out_indices
             UpperCAmelCase_  :	Dict				      =		num_attention_heads
             UpperCAmelCase_  :	int				      =		intermediate_size
             UpperCAmelCase_  :	Optional[int]				      =		hidden_act
             UpperCAmelCase_  :	str				      =		hidden_dropout_prob
             UpperCAmelCase_  :	Optional[int]				      =		attention_probs_dropout_prob
             UpperCAmelCase_  :	Optional[Any]				      =		initializer_range
             UpperCAmelCase_  :	Optional[Any]				      =		num_labels
             UpperCAmelCase_  :	Any				      =		backbone_featmap_shape
             UpperCAmelCase_  :	Dict				      =		scope
             UpperCAmelCase_  :	Optional[Any]				      =		is_hybrid
             # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
             UpperCAmelCase_  :	Dict				      =		(image_size // patch_size) ** 2
             UpperCAmelCase_  :	Optional[Any]				      =		num_patches + 1



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

             UpperCAmelCase_  :	Tuple				      =		self.get_config()

             return config, pixel_values, labels



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		{
                 "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=lowercase     ,initializer_range=self.initializer_range     ,is_hybrid=self.is_hybrid     ,backbone_config=lowercase     ,backbone_featmap_shape=self.backbone_featmap_shape     ,)



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		DPTModel(config=lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	Tuple				      =		model(lowercase)
             self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		self.num_labels
             UpperCAmelCase_  :	Optional[Any]				      =		DPTForDepthEstimation(lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	List[Any]				      =		model(lowercase)
             self.parent.assertEqual(result.predicted_depth.shape     ,(self.batch_size, self.image_size, self.image_size))



       def  A_   (     self     ,lowercase     ,lowercase     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		self.num_labels
             UpperCAmelCase_  :	Optional[int]				      =		DPTForSemanticSegmentation(lowercase)
             model.to(lowercase)
             model.eval()
             UpperCAmelCase_  :	int				      =		model(lowercase     ,labels=lowercase)
             self.parent.assertEqual(
                 result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size, self.image_size))



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_  :	List[Any]				      =		config_and_inputs
             UpperCAmelCase_  :	Union[str, Any]				      =		{"pixel_values": pixel_values}
             return config, inputs_dict


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

       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False
       _lowerCamelCase			    =				False



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		DPTModelTester(self)
             UpperCAmelCase_  :	int				      =		ConfigTester(self     ,config_class=lowercase     ,has_text_modality=lowercase     ,hidden_size=37)



       def  A_   (     self):

             """simple docstring"""





             self.config_tester.run_common_tests()



       @unittest.skip(reason="DPT does not use inputs_embeds")
       def  A_   (     self):

             """simple docstring"""





             pass



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	List[str]				      =		model_class(lowercase)
                   self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module))
                   UpperCAmelCase_  :	Union[str, Any]				      =		model.get_output_embeddings()
                   self.assertTrue(x is None or isinstance(lowercase     ,nn.Linear))



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.model_tester.prepare_config_and_inputs_for_common()

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

                   UpperCAmelCase_  :	Optional[Any]				      =		["pixel_values"]
                   self.assertListEqual(arg_names[:1]     ,lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Tuple				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_depth_estimation(*lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Union[str, Any]				      =		self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_semantic_segmentation(*lowercase)



       def  A_   (     self):

             """simple docstring"""





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

                   UpperCAmelCase_  :	Dict				      =		self.model_tester.prepare_config_and_inputs_for_common()
                   UpperCAmelCase_  :	Union[str, Any]				      =		True

                   if model_class in get_values(lowercase):
                         continue

                   UpperCAmelCase_  :	Dict				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.train()
                   UpperCAmelCase_  :	Any				      =		self._prepare_for_class(lowercase     ,lowercase     ,return_labels=lowercase)
                   UpperCAmelCase_  :	Union[str, Any]				      =		model(**lowercase).loss
                   loss.backward()



       def  A_   (     self):

             """simple docstring"""





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

                   UpperCAmelCase_  :	Any				      =		self.model_tester.prepare_config_and_inputs_for_common()
                   UpperCAmelCase_  :	Optional[Any]				      =		False
                   UpperCAmelCase_  :	Tuple				      =		True

                   if model_class in get_values(lowercase) or not model_class.supports_gradient_checkpointing:
                         continue
                   UpperCAmelCase_  :	Dict				      =		model_class(lowercase)
                   model.to(lowercase)
                   model.gradient_checkpointing_enable()
                   model.train()
                   UpperCAmelCase_  :	List[Any]				      =		self._prepare_for_class(lowercase     ,lowercase     ,return_labels=lowercase)
                   UpperCAmelCase_  :	List[str]				      =		model(**lowercase).loss
                   loss.backward()



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		self.model_tester.prepare_config_and_inputs_for_common()

             UpperCAmelCase_  :	Union[str, Any]				      =		_config_zero_init(lowercase)
             for model_class in self.all_model_classes:
                   UpperCAmelCase_  :	List[str]				      =		model_class(config=lowercase)
                   # Skip the check for the backbone
                   UpperCAmelCase_  :	List[Any]				      =		[]
                   for name, module in model.named_modules():
                         if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                               UpperCAmelCase_  :	Optional[Any]				      =		[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):

             """simple docstring"""





             pass



       @slow
       def  A_   (     self):

             """simple docstring"""





             for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                   UpperCAmelCase_  :	Optional[int]				      =		DPTModel.from_pretrained(lowercase)
                   self.assertIsNotNone(lowercase)



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Dict				      =		self.model_tester.prepare_config_and_inputs_for_common()
             UpperCAmelCase_  :	Tuple				      =		"add"
             with self.assertRaises(lowercase):
                   UpperCAmelCase_  :	Optional[Any]				      =		DPTForDepthEstimation(lowercase)







def    _snake_case (       )     ->     int:






      '''simple docstring'''







      UpperCAmelCase_  :	Optional[int]				      =		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
      return image





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas")
             UpperCAmelCase_  :	Any				      =		DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas").to(lowercase)

             UpperCAmelCase_  :	Union[str, Any]				      =		prepare_img()
             UpperCAmelCase_  :	int				      =		image_processor(images=lowercase     ,return_tensors="pt").to(lowercase)

             # forward pass
             with torch.no_grad():
                   UpperCAmelCase_  :	Optional[Any]				      =		model(**lowercase)
                   UpperCAmelCase_  :	Union[str, Any]				      =		outputs.predicted_depth

             # verify the predicted depth
             UpperCAmelCase_  :	List[Any]				      =		torch.Size((1, 384, 384))
             self.assertEqual(predicted_depth.shape     ,lowercase)

             UpperCAmelCase_  :	int				      =		torch.tensor(
                 [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]).to(lowercase)

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





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
   raise RuntimeError('''At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training''')

# TF training parameters
__lowerCamelCase         =			False
__lowerCamelCase         =			False







def    _snake_case (       __snake_case    )     ->     Any:






      '''simple docstring'''







      return TrainCommand(__snake_case    )





class   snake_case_ (lowercase__  ):




       """simple docstring"""






       @staticmethod
       def  A_   (     lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		parser.add_parser("train"     ,help="CLI tool to train a model on a task.")

             train_parser.add_argument(
                 "--train_data"     ,type=lowercase     ,required=lowercase     ,help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences."     ,)
             train_parser.add_argument(
                 "--column_label"     ,type=lowercase     ,default=0     ,help="Column of the dataset csv file with example labels.")
             train_parser.add_argument(
                 "--column_text"     ,type=lowercase     ,default=1     ,help="Column of the dataset csv file with example texts.")
             train_parser.add_argument(
                 "--column_id"     ,type=lowercase     ,default=2     ,help="Column of the dataset csv file with example ids.")
             train_parser.add_argument(
                 "--skip_first_row"     ,action="store_true"     ,help="Skip the first row of the csv file (headers).")

             train_parser.add_argument("--validation_data"     ,type=lowercase     ,default=""     ,help="path to validation dataset.")
             train_parser.add_argument(
                 "--validation_split"     ,type=lowercase     ,default=0.1     ,help="if validation dataset is not provided, fraction of train dataset to use as validation dataset."     ,)

             train_parser.add_argument("--output"     ,type=lowercase     ,default="./"     ,help="path to saved the trained model.")

             train_parser.add_argument(
                 "--task"     ,type=lowercase     ,default="text_classification"     ,help="Task to train the model on.")
             train_parser.add_argument(
                 "--model"     ,type=lowercase     ,default="bert-base-uncased"     ,help="Model's name or path to stored model.")
             train_parser.add_argument("--train_batch_size"     ,type=lowercase     ,default=32     ,help="Batch size for training.")
             train_parser.add_argument("--valid_batch_size"     ,type=lowercase     ,default=64     ,help="Batch size for validation.")
             train_parser.add_argument("--learning_rate"     ,type=lowercase     ,default=3E-5     ,help="Learning rate.")
             train_parser.add_argument("--adam_epsilon"     ,type=lowercase     ,default=1E-08     ,help="Epsilon for Adam optimizer.")
             train_parser.set_defaults(func=lowercase)



       def __init__(     self     ,lowercase):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[Any]				      =		logging.get_logger("transformers-cli/training")

             UpperCAmelCase_  :	List[Any]				      =		"tf" if is_tf_available() else "torch"

             os.makedirs(args.output     ,exist_ok=lowercase)
             UpperCAmelCase_  :	Union[str, Any]				      =		args.output

             UpperCAmelCase_  :	Optional[int]				      =		args.column_label
             UpperCAmelCase_  :	Dict				      =		args.column_text
             UpperCAmelCase_  :	Optional[Any]				      =		args.column_id

             self.logger.info(F"""Loading {args.task} pipeline for {args.model}""")
             if args.task == "text_classification":
                   UpperCAmelCase_  :	List[Any]				      =		TextClassificationPipeline.from_pretrained(args.model)
             elif args.task == "token_classification":
                   raise NotImplementedError
             elif args.task == "question_answering":
                   raise NotImplementedError

             self.logger.info(F"""Loading dataset from {args.train_data}""")
             UpperCAmelCase_  :	str				      =		Processor.create_from_csv(
                 args.train_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)
             UpperCAmelCase_  :	str				      =		None
             if args.validation_data:
                   self.logger.info(F"""Loading validation dataset from {args.validation_data}""")
                   UpperCAmelCase_  :	Union[str, Any]				      =		Processor.create_from_csv(
                       args.validation_data     ,column_label=args.column_label     ,column_text=args.column_text     ,column_id=args.column_id     ,skip_first_row=args.skip_first_row     ,)

             UpperCAmelCase_  :	List[str]				      =		args.validation_split
             UpperCAmelCase_  :	Tuple				      =		args.train_batch_size
             UpperCAmelCase_  :	Optional[int]				      =		args.valid_batch_size
             UpperCAmelCase_  :	Dict				      =		args.learning_rate
             UpperCAmelCase_  :	List[str]				      =		args.adam_epsilon



       def  A_   (     self):

             """simple docstring"""





             if self.framework == "tf":
                   return self.run_tf()
             return self.run_torch()



       def  A_   (     self):

             """simple docstring"""





             raise NotImplementedError



       def  A_   (     self):

             """simple docstring"""





             self.pipeline.fit(
                 self.train_dataset     ,validation_data=self.valid_dataset     ,validation_split=self.validation_split     ,learning_rate=self.learning_rate     ,adam_epsilon=self.adam_epsilon     ,train_batch_size=self.train_batch_size     ,valid_batch_size=self.valid_batch_size     ,)

             # Save trained pipeline
             self.pipeline.save_pretrained(self.output)
