






'''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      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							Dict					,      __lowercase	:							List[str]					,      __lowercase	:							Any=2					,      __lowercase	:							str=32					,      __lowercase	:							Optional[Any]=16					,      __lowercase	:							int=3					,      __lowercase	:							Tuple=True					,      __lowercase	:							str=True					,      __lowercase	:							Dict=32					,      __lowercase	:							Union[str, Any]=4					,      __lowercase	:							Dict=[0, 1, 2, 3]					,      __lowercase	:							Dict=4					,      __lowercase	:							List[str]=37					,      __lowercase	:							Any="gelu"					,      __lowercase	:							Dict=0.1					,      __lowercase	:							List[str]=0.1					,      __lowercase	:							Union[str, Any]=0.02					,      __lowercase	:							Optional[int]=3					,      __lowercase	:							Dict=[1, 3_84, 24, 24]					,      __lowercase	:							Tuple=True					,      __lowercase	:							List[Any]=None					,      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				str									=parent
       SCREAMING_SNAKE_CASE__     :				str									=batch_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=image_size
       SCREAMING_SNAKE_CASE__     :				int									=patch_size
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=num_channels
       SCREAMING_SNAKE_CASE__     :				Tuple									=is_training
       SCREAMING_SNAKE_CASE__     :				str									=use_labels
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=hidden_size
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=num_hidden_layers
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=backbone_out_indices
       SCREAMING_SNAKE_CASE__     :				List[Any]									=num_attention_heads
       SCREAMING_SNAKE_CASE__     :				List[Any]									=intermediate_size
       SCREAMING_SNAKE_CASE__     :				str									=hidden_act
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=hidden_dropout_prob
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__     :				List[Any]									=initializer_range
       SCREAMING_SNAKE_CASE__     :				Dict									=num_labels
       SCREAMING_SNAKE_CASE__     :				List[str]									=backbone_featmap_shape
       SCREAMING_SNAKE_CASE__     :				int									=scope
       SCREAMING_SNAKE_CASE__     :				List[str]									=is_hybrid
       # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=(image_size // patch_size) ** 2
       SCREAMING_SNAKE_CASE__     :				Any									=num_patches + 1




 def      __magic_name__	(      self	:							Dict      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				int									=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

       SCREAMING_SNAKE_CASE__     :				Dict									=self.get_config()

       return config, pixel_values, labels




 def      __magic_name__	(      self	:							int      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				str									={
           '''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=__lowercase					,      initializer_range=self.initializer_range					,      is_hybrid=self.is_hybrid					,      backbone_config=__lowercase					,      backbone_featmap_shape=self.backbone_featmap_shape					,      )




 def      __magic_name__	(      self	:							List[str]					,      __lowercase	:							Tuple					,      __lowercase	:							List[str]					,      __lowercase	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=DPTModel(config=__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				Dict									=model(__lowercase      )
       self.parent.assertEqual(result.last_hidden_state.shape					,      (self.batch_size, self.seq_length, self.hidden_size)      )




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Dict					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[Any]      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Any									=self.num_labels
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DPTForDepthEstimation(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				str									=model(__lowercase      )
       self.parent.assertEqual(result.predicted_depth.shape					,      (self.batch_size, self.image_size, self.image_size)      )




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Dict					,      __lowercase	:							Optional[int]					,      __lowercase	:							int      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.num_labels
       SCREAMING_SNAKE_CASE__     :				List[str]									=DPTForSemanticSegmentation(__lowercase      )
       model.to(__lowercase      )
       model.eval()
       SCREAMING_SNAKE_CASE__     :				int									=model(__lowercase					,      labels=__lowercase      )
       self.parent.assertEqual(
           result.logits.shape					,      (self.batch_size, self.num_labels, self.image_size, self.image_size)      )




 def      __magic_name__	(      self	:							int      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Tuple									=config_and_inputs
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									={'''pixel_values''': pixel_values}
       return config, inputs_dict






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

 snake_case_  							=							False
 snake_case_  							=							False
 snake_case_  							=							False




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=DPTModelTester(self      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=ConfigTester(self					,      config_class=__lowercase					,      has_text_modality=__lowercase					,      hidden_size=37      )




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Union[str, Any]:
       self.config_tester.run_common_tests()




 @unittest.skip(reason='''DPT does not use inputs_embeds'''      )
 def      __magic_name__	(      self	:							List[Any]      )	->						List[str]:
       pass




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						str:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Dict									=self.model_tester.prepare_config_and_inputs_for_common()

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




 def      __magic_name__	(      self	:							Optional[Any]      )	->						int:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs_for_common()

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

             SCREAMING_SNAKE_CASE__     :				Dict									=['''pixel_values''']
             self.assertListEqual(arg_names[:1]					,      __lowercase      )




 def      __magic_name__	(      self	:							List[Any]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__lowercase      )




 def      __magic_name__	(      self	:							List[Any]      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Dict									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*__lowercase      )




 def      __magic_name__	(      self	:							int      )	->						Dict:
       SCREAMING_SNAKE_CASE__     :				Any									=self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase      )




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

             SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE__     :				Tuple									=True

             if model_class in get_values(__lowercase      ):
                   continue

             SCREAMING_SNAKE_CASE__     :				Optional[int]									=model_class(__lowercase      )
             model.to(__lowercase      )
             model.train()
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=self._prepare_for_class(__lowercase					,      __lowercase					,      return_labels=__lowercase      )
             SCREAMING_SNAKE_CASE__     :				int									=model(**__lowercase      ).loss
             loss.backward()




 def      __magic_name__	(      self	:							Any      )	->						str:
       for model_class in self.all_model_classes:
             if model_class.__name__ == "DPTForDepthEstimation":
                   continue

             SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.model_tester.prepare_config_and_inputs_for_common()
             SCREAMING_SNAKE_CASE__     :				List[str]									=False
             SCREAMING_SNAKE_CASE__     :				List[Any]									=True

             if model_class in get_values(__lowercase      ) or not model_class.supports_gradient_checkpointing:
                   continue
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=model_class(__lowercase      )
             model.to(__lowercase      )
             model.gradient_checkpointing_enable()
             model.train()
             SCREAMING_SNAKE_CASE__     :				List[str]									=self._prepare_for_class(__lowercase					,      __lowercase					,      return_labels=__lowercase      )
             SCREAMING_SNAKE_CASE__     :				int									=model(**__lowercase      ).loss
             loss.backward()




 def      __magic_name__	(      self	:							Dict      )	->						Optional[int]:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[Any]									=self.model_tester.prepare_config_and_inputs_for_common()

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




 @slow
 def      __magic_name__	(      self	:							str      )	->						Optional[int]:
       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
             SCREAMING_SNAKE_CASE__     :				str									=DPTModel.from_pretrained(__lowercase      )
             self.assertIsNotNone(__lowercase      )




 def      __magic_name__	(      self	:							Dict      )	->						List[str]:
       # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				int									=self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__     :				int									='''add'''
       with self.assertRaises(__lowercase      ):
             SCREAMING_SNAKE_CASE__     :				str									=DPTForDepthEstimation(__lowercase      )





def  _a(  ):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									=Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image







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




 def      __magic_name__	(      self	:							Tuple      )	->						str:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=prepare_img()
       SCREAMING_SNAKE_CASE__     :				Dict									=image_processor(images=__lowercase					,      return_tensors='''pt'''      ).to(__lowercase      )

       # forward pass
       with torch.no_grad():
             SCREAMING_SNAKE_CASE__     :				List[Any]									=model(**__lowercase      )
             SCREAMING_SNAKE_CASE__     :				Any									=outputs.predicted_depth

       # verify the predicted depth
       SCREAMING_SNAKE_CASE__     :				int									=torch.Size((1, 3_84, 3_84)      )
       self.assertEqual(predicted_depth.shape					,      __lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=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] / 1_00					,      __lowercase					,      atol=1e-4      )      )







'''simple docstring'''
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
a_					= False
a_					= False





def  _a(  UpperCamelCase__				:						Namespace							):




      '''simple docstring'''






      return TrainCommand(UpperCamelCase__							)







class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 @staticmethod
 def      __magic_name__	(      __lowercase	:							ArgumentParser      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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	:							Tuple					,      __lowercase	:							Namespace      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=logging.get_logger('''transformers-cli/training'''      )

       SCREAMING_SNAKE_CASE__     :				int									='''tf''' if is_tf_available() else '''torch'''

       os.makedirs(args.output					,      exist_ok=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=args.output

       SCREAMING_SNAKE_CASE__     :				str									=args.column_label
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.column_text
       SCREAMING_SNAKE_CASE__     :				Tuple									=args.column_id

       self.logger.info(F"Loading {args.task} pipeline for {args.model}"      )
       if args.task == "text_classification":
             SCREAMING_SNAKE_CASE__     :				List[str]									=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}"      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None
       if args.validation_data:
             self.logger.info(F"Loading validation dataset from {args.validation_data}"      )
             SCREAMING_SNAKE_CASE__     :				List[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					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=args.validation_split
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.train_batch_size
       SCREAMING_SNAKE_CASE__     :				Any									=args.valid_batch_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=args.learning_rate
       SCREAMING_SNAKE_CASE__     :				int									=args.adam_epsilon




 def      __magic_name__	(      self	:							Any      )	->						str:
       if self.framework == "tf":
             return self.run_tf()
       return self.run_torch()




 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       raise NotImplementedError




 def      __magic_name__	(      self	:							Dict      )	->						List[Any]:
       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      )