

'''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    ,					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__=False    ,					lowercase__=None    ,					):






          """simple docstring"""






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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.get_config()

          return config, pixel_values, labels




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          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    ,					)




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DPTModel(config=lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	model(lowercase__      )
          self.parent.assertEqual(result.last_hidden_state.shape    ,					(self.batch_size, self.seq_length, self.hidden_size)      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






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




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__    ,					lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.num_labels
          SCREAMING_SNAKE_CASE_				:      Tuple					=	DPTForSemanticSegmentation(lowercase__      )
          model.to(lowercase__      )
          model.eval()
          SCREAMING_SNAKE_CASE_				:      str					=	model(lowercase__    ,					labels=lowercase__      )
          self.parent.assertEqual(
              result.logits.shape    ,					(self.batch_size, self.num_labels, self.image_size, self.image_size)      )







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	self.prepare_config_and_inputs()
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	config_and_inputs
          SCREAMING_SNAKE_CASE_				:      str					=	{"pixel_values": pixel_values}
          return config, inputs_dict






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

    _A         =							False
    _A         =							False
    _A         =							False




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	DPTModelTester(self      )
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	ConfigTester(self    ,					config_class=lowercase__    ,					has_text_modality=lowercase__    ,					hidden_size=37      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          self.config_tester.run_common_tests()




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






          """simple docstring"""






          pass




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[str]					=	self.model_tester.prepare_config_and_inputs_for_common()

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

                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	["pixel_values"]
                self.assertListEqual(arg_names[:1]    ,					lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Any					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_depth_estimation(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_semantic_segmentation(*lowercase__      )




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	self.model_tester.prepare_config_and_inputs_for_common()
                SCREAMING_SNAKE_CASE_				:      Dict					=	True

                if model_class in get_values(lowercase__      ):
                      continue

                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model_class(lowercase__      )
                model.to(lowercase__      )
                model.train()
                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self._prepare_for_class(lowercase__    ,					lowercase__    ,					return_labels=lowercase__      )
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model(**lowercase__      ).loss
                loss.backward()




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

                SCREAMING_SNAKE_CASE_				:      List[Any]					=	self.model_tester.prepare_config_and_inputs_for_common()
                SCREAMING_SNAKE_CASE_				:      int					=	False
                SCREAMING_SNAKE_CASE_				:      List[str]					=	True

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_				:      Dict					=	_config_zero_init(lowercase__      )
          for model_class in self.all_model_classes:
                SCREAMING_SNAKE_CASE_				:      int					=	model_class(config=lowercase__      )
                # Skip the check for the backbone
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[]
                for name, module in model.named_modules():
                      if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                            SCREAMING_SNAKE_CASE_				:      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   __lowerCamelCase				( self      ):






          """simple docstring"""






          pass







    @slow
    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                SCREAMING_SNAKE_CASE_				:      int					=	DPTModel.from_pretrained(lowercase__      )
                self.assertIsNotNone(lowercase__      )






def  __lowerCamelCase      (				)							->       Optional[Any]:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"   )
      return image





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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	DPTImageProcessor.from_pretrained("Intel/dpt-large"      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DPTForDepthEstimation.from_pretrained("Intel/dpt-large"      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	prepare_img()
          SCREAMING_SNAKE_CASE_				:      int					=	image_processor(images=lowercase__    ,					return_tensors="pt"      ).to(lowercase__      )

          # forward pass
          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      Dict					=	model(**lowercase__      )
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	outputs.predicted_depth

          # verify the predicted depth
          SCREAMING_SNAKE_CASE_				:      Any					=	torch.Size((1, 384, 384)      )
          self.assertEqual(predicted_depth.shape    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Any					=	torch.tensor(
              [[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]]      ).to(lowercase__      )

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




    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
          SCREAMING_SNAKE_CASE_				:      str					=	DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	prepare_img()
          SCREAMING_SNAKE_CASE_				:      str					=	image_processor(images=lowercase__    ,					return_tensors="pt"      ).to(lowercase__      )

          # forward pass
          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	model(**lowercase__      )

          # verify the logits
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	torch.Size((1, 150, 480, 480)      )
          self.assertEqual(outputs.logits.shape    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	torch.tensor(
              [[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]]      ).to(lowercase__      )

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







    def   __lowerCamelCase				( self      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      str					=	DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"      ).to(lowercase__      )

          SCREAMING_SNAKE_CASE_				:      List[str]					=	prepare_img()
          SCREAMING_SNAKE_CASE_				:      int					=	image_processor(images=lowercase__    ,					return_tensors="pt"      ).to(lowercase__      )

          # forward pass
          with torch.no_grad():
                SCREAMING_SNAKE_CASE_				:      Optional[int]					=	model(**lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	outputs.logits.detach().cpu()

          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	image_processor.post_process_semantic_segmentation(outputs=lowercase__    ,					target_sizes=[(500, 300)]      )
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	torch.Size((500, 300)      )
          self.assertEqual(segmentation[0].shape    ,					lowercase__      )

          SCREAMING_SNAKE_CASE_				:      Dict					=	image_processor.post_process_semantic_segmentation(outputs=lowercase__      )
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	torch.Size((480, 480)      )
          self.assertEqual(segmentation[0].shape    ,					lowercase__      )




'''simple docstring'''






import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import AddedToken, PreTrainedTokenizerFast
from ...utils import logging


snake_case_					  =	logging.get_logger(__name__)

snake_case_					  =	{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case_					  =	{
    'vocab_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        'TsinghuaAI/CPM-Generate': 'https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/tokenizer.json',
    },
}
class      SCREAMING_SNAKE_CASE__      (    _UpperCAmelCase				):




    def __init__( self    ,					lowercase__=None    ,					lowercase__=None    ,					lowercase__=False    ,					lowercase__=True    ,					lowercase__=False    ,					lowercase__="<s>"    ,					lowercase__="</s>"    ,					lowercase__="<unk>"    ,					lowercase__="<sep>"    ,					lowercase__="<pad>"    ,					lowercase__="<cls>"    ,					lowercase__="<mask>"    ,					lowercase__=["<eop>", "<eod>"]    ,					**lowercase__    ,					):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Tuple					=	AddedToken(lowercase__    ,					lstrip=lowercase__    ,					rstrip=lowercase__      ) if isinstance(lowercase__    ,					lowercase__      ) else mask_token

          super().__init__(
              vocab_file=lowercase__    ,					tokenizer_file=lowercase__    ,					do_lower_case=lowercase__    ,					remove_space=lowercase__    ,					keep_accents=lowercase__    ,					bos_token=lowercase__    ,					eos_token=lowercase__    ,					unk_token=lowercase__    ,					sep_token=lowercase__    ,					pad_token=lowercase__    ,					cls_token=lowercase__    ,					mask_token=lowercase__    ,					additional_special_tokens=lowercase__    ,					**lowercase__    ,					)

          SCREAMING_SNAKE_CASE_				:      List[str]					=	3
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	do_lower_case
          SCREAMING_SNAKE_CASE_				:      Optional[int]					=	remove_space
          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	keep_accents
          SCREAMING_SNAKE_CASE_				:      List[str]					=	vocab_file
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	False if not self.vocab_file else True

          try:
                import jieba
          except ModuleNotFoundError as error:
                raise error.__class__(
                    "You need to install jieba to use CpmTokenizer or CpmTokenizerFast. "
                    "See https://pypi.org/project/jieba/ for installation."      )
          SCREAMING_SNAKE_CASE_				:      Dict					=	jieba
          SCREAMING_SNAKE_CASE_				:      Optional[Any]					=	str.maketrans(" \n"    ,					"\u2582\u2583"      )




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Union[str, Any]					=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_				:      List[Any]					=	[self.cls_token_id]
          if token_ids_a is None:
                return token_ids_a + sep + cls
          return token_ids_a + sep + token_ids_a + sep + cls




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      List[Any]					=	[self.sep_token_id]
          SCREAMING_SNAKE_CASE_				:      Dict					=	[2]

          if token_ids_a is None:
                return len(token_ids_a + sep      ) * [0] + cls_segment_id
          return len(token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1] + cls_segment_id




    def   __lowerCamelCase				( self    ,					lowercase__    ,					lowercase__ = None      ):






          """simple docstring"""






          if not self.can_save_slow_tokenizer:
                raise ValueError(
                    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                    "tokenizer."      )

          if not os.path.isdir(lowercase__      ):
                logger.error(F"Vocabulary path ({save_directory}) should be a directory"      )
                return
          SCREAMING_SNAKE_CASE_				:      str					=	os.path.join(
              lowercase__    ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]      )

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(lowercase__      ):
                copyfile(self.vocab_file    ,					lowercase__      )

          return (out_vocab_file,)




    def   __lowerCamelCase				( self    ,					lowercase__    ,					*lowercase__    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      int					=	[
              " ".join([x.translate(self.translator      ) for x in self.jieba.cut(lowercase__    ,					cut_all=lowercase__      )]      )
              for text in batch_text_or_text_pairs
          ]
          return super()._batch_encode_plus(lowercase__    ,					*lowercase__    ,					**lowercase__      )







    def   __lowerCamelCase				( self    ,					*lowercase__    ,					**lowercase__      ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_				:      Dict					=	super()._decode(*lowercase__    ,					**lowercase__      )
          SCREAMING_SNAKE_CASE_				:      str					=	text.replace(" "    ,					""      ).replace("\u2582"    ,					" "      ).replace("\u2583"    ,					"\n"      )
          return text

