



'''simple docstring'''







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

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


if is_sentencepiece_available():
      from .tokenization_reformer import ReformerTokenizer
else:
      lowercase__  :	Optional[Any]            =      None


lowercase__  :	Dict            =      logging.get_logger(__name__)


lowercase__  :	List[Any]            =      '▁'

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

lowercase__  :	Dict            =      {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    },
    'tokenizer_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'
        )
    },
}

lowercase__  :	Optional[Any]            =      {
    'google/reformer-crime-and-punishment': 52_42_88,
}

class    lowerCamelCase_				(						lowerCAmelCase_	):






   '''simple docstring'''

   A			:  Optional[Any]					=						VOCAB_FILES_NAMES
   A			:  Union[str, Any]					=						PRETRAINED_VOCAB_FILES_MAP
   A			:  Dict					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A			:  Optional[Any]					=						["input_ids", "attention_mask"]
   A			:  List[str]					=						ReformerTokenizer


   def __init__(	self  ,    __UpperCamelCase=None  ,    __UpperCamelCase=None  ,    __UpperCamelCase="</s>"  ,    __UpperCamelCase="<unk>"  ,    __UpperCamelCase=[]  ,    **__UpperCamelCase  ,    ):
       super().__init__(
           __UpperCamelCase  ,    tokenizer_file=__UpperCamelCase  ,    eos_token=__UpperCamelCase  ,    unk_token=__UpperCamelCase  ,    additional_special_tokens=__UpperCamelCase  ,    **__UpperCamelCase  ,    )

       UpperCamelCase__	     =      vocab_file
       UpperCamelCase__	     =      False if not self.vocab_file else True







   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase = None			):
       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(__UpperCamelCase			):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'			)
           return
       UpperCamelCase__	     =      os.path.join(
           __UpperCamelCase  ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]			)

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

       return (out_vocab_file,)









'''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_				:






   '''simple docstring'''

   def __init__(	self  ,    __UpperCamelCase  ,    __UpperCamelCase=2  ,    __UpperCamelCase=32  ,    __UpperCamelCase=16  ,    __UpperCamelCase=3  ,    __UpperCamelCase=True  ,    __UpperCamelCase=True  ,    __UpperCamelCase=32  ,    __UpperCamelCase=4  ,    __UpperCamelCase=[0, 1, 2, 3]  ,    __UpperCamelCase=4  ,    __UpperCamelCase=37  ,    __UpperCamelCase="gelu"  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.1  ,    __UpperCamelCase=0.02  ,    __UpperCamelCase=3  ,    __UpperCamelCase=False  ,    __UpperCamelCase=None  ,    ):
       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 						_UpperCamelCase							(	self			):
       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 						_UpperCamelCase							(	self			):
       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=__UpperCamelCase  ,    initializer_range=self.initializer_range  ,    is_hybrid=self.is_hybrid  ,    )


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      DPTModel(config=__UpperCamelCase			)
       model.to(__UpperCamelCase			)
       model.eval()
       UpperCamelCase__	     =      model(__UpperCamelCase			)
       self.parent.assertEqual(result.last_hidden_state.shape  ,    (self.batch_size, self.seq_length, self.hidden_size)			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.num_labels
       UpperCamelCase__	     =      DPTForDepthEstimation(__UpperCamelCase			)
       model.to(__UpperCamelCase			)
       model.eval()
       UpperCamelCase__	     =      model(__UpperCamelCase			)
       self.parent.assertEqual(result.predicted_depth.shape  ,    (self.batch_size, self.image_size, self.image_size)			)


   def 						_UpperCamelCase							(	self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):
       UpperCamelCase__	     =      self.num_labels
       UpperCamelCase__	     =      DPTForSemanticSegmentation(__UpperCamelCase			)
       model.to(__UpperCamelCase			)
       model.eval()
       UpperCamelCase__	     =      model(__UpperCamelCase  ,    labels=__UpperCamelCase			)
       self.parent.assertEqual(
           result.logits.shape  ,    (self.batch_size, self.num_labels, self.image_size, self.image_size)			)







   def 						_UpperCamelCase							(	self			):
       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_				(						lowerCAmelCase_       ,     lowerCAmelCase_       ,     unittest.TestCase	):






   '''simple docstring'''

   A			:  int					=						(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
   A			:  Any					=						(
       {
           "depth-estimation": DPTForDepthEstimation,
           "feature-extraction": DPTModel,
           "image-segmentation": DPTForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   A			:  int					=						False
   A			:  List[str]					=						False
   A			:  int					=						False


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTModelTester(self			)
       UpperCamelCase__	     =      ConfigTester(self  ,    config_class=__UpperCamelCase  ,    has_text_modality=__UpperCamelCase  ,    hidden_size=37			)


   def 						_UpperCamelCase							(	self			):
       self.config_tester.run_common_tests()


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


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()

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


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           UpperCamelCase__	     =      model_class(__UpperCamelCase			)
           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]  ,    __UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase			)


   def 						_UpperCamelCase							(	self			):
       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(__UpperCamelCase			):
               continue

           UpperCamelCase__	     =      model_class(__UpperCamelCase			)
           model.to(__UpperCamelCase			)
           model.train()
           UpperCamelCase__	     =      self._prepare_for_class(__UpperCamelCase  ,    __UpperCamelCase  ,    return_labels=__UpperCamelCase			)
           UpperCamelCase__	     =      model(**__UpperCamelCase			).loss
           loss.backward()


   def 						_UpperCamelCase							(	self			):
       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(__UpperCamelCase			) or not model_class.supports_gradient_checkpointing:
               continue
           UpperCamelCase__	     =      model_class(__UpperCamelCase			)
           model.to(__UpperCamelCase			)
           model.gradient_checkpointing_enable()
           model.train()
           UpperCamelCase__	     =      self._prepare_for_class(__UpperCamelCase  ,    __UpperCamelCase  ,    return_labels=__UpperCamelCase			)
           UpperCamelCase__	     =      model(**__UpperCamelCase			).loss
           loss.backward()


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__     ,	UpperCamelCase__	     =      self.model_tester.prepare_config_and_inputs_for_common()

       UpperCamelCase__	     =      _config_zero_init(__UpperCamelCase			)
       for model_class in self.all_model_classes:
           UpperCamelCase__	     =      model_class(config=__UpperCamelCase			)
           # 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 						_UpperCamelCase							(	self			):
       pass







   @slow
   def 						_UpperCamelCase							(	self			):
       for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           UpperCamelCase__	     =      DPTModel.from_pretrained(__UpperCamelCase			)
           self.assertIsNotNone(__UpperCamelCase			)





def     snake_case__			():
    UpperCamelCase__	     =      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
    return image





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






   '''simple docstring'''

   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTImageProcessor.from_pretrained("Intel/dpt-large"			)
       UpperCamelCase__	     =      DPTForDepthEstimation.from_pretrained("Intel/dpt-large"			).to(__UpperCamelCase			)

       UpperCamelCase__	     =      prepare_img()
       UpperCamelCase__	     =      image_processor(images=__UpperCamelCase  ,    return_tensors="pt"			).to(__UpperCamelCase			)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__	     =      model(**__UpperCamelCase			)
           UpperCamelCase__	     =      outputs.predicted_depth

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

       UpperCamelCase__	     =      torch.tensor(
           [[6.3_199, 6.3_629, 6.4_148], [6.3_850, 6.3_615, 6.4_166], [6.3_519, 6.3_176, 6.3_575]]			).to(__UpperCamelCase			)

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


   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"			)
       UpperCamelCase__	     =      DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"			).to(__UpperCamelCase			)

       UpperCamelCase__	     =      prepare_img()
       UpperCamelCase__	     =      image_processor(images=__UpperCamelCase  ,    return_tensors="pt"			).to(__UpperCamelCase			)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__	     =      model(**__UpperCamelCase			)

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

       UpperCamelCase__	     =      torch.tensor(
           [[4.0_480, 4.2_420, 4.4_360], [4.3_124, 4.5_693, 4.8_261], [4.5_768, 4.8_965, 5.2_163]]			).to(__UpperCamelCase			)

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







   def 						_UpperCamelCase							(	self			):
       UpperCamelCase__	     =      DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"			)
       UpperCamelCase__	     =      DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"			).to(__UpperCamelCase			)

       UpperCamelCase__	     =      prepare_img()
       UpperCamelCase__	     =      image_processor(images=__UpperCamelCase  ,    return_tensors="pt"			).to(__UpperCamelCase			)

       # forward pass
       with torch.no_grad():
           UpperCamelCase__	     =      model(**__UpperCamelCase			)

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

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

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




