



'''simple docstring'''





import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
_UpperCamelCase											=						'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
_UpperCamelCase											=						direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
_UpperCamelCase											=						re.compile(R'TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
_UpperCamelCase											=						re.compile(R'Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
_UpperCamelCase											=						re.compile(R'(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)')


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
_UpperCamelCase											=						[
    ('pretraining', 'MODEL_FOR_PRETRAINING_MAPPING_NAMES', 'AutoModelForPreTraining'),
    ('feature-extraction', 'MODEL_MAPPING_NAMES', 'AutoModel'),
    ('audio-classification', 'MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForAudioClassification'),
    ('text-generation', 'MODEL_FOR_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForCausalLM'),
    ('automatic-speech-recognition', 'MODEL_FOR_CTC_MAPPING_NAMES', 'AutoModelForCTC'),
    ('image-classification', 'MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForImageClassification'),
    ('image-segmentation', 'MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES', 'AutoModelForImageSegmentation'),
    ('fill-mask', 'MODEL_FOR_MASKED_LM_MAPPING_NAMES', 'AutoModelForMaskedLM'),
    ('object-detection', 'MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES', 'AutoModelForObjectDetection'),
    (
        'zero-shot-object-detection',
        'MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES',
        'AutoModelForZeroShotObjectDetection',
    ),
    ('question-answering', 'MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES', 'AutoModelForQuestionAnswering'),
    ('text2text-generation', 'MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES', 'AutoModelForSeq2SeqLM'),
    ('text-classification', 'MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForSequenceClassification'),
    ('automatic-speech-recognition', 'MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES', 'AutoModelForSpeechSeq2Seq'),
    (
        'table-question-answering',
        'MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForTableQuestionAnswering',
    ),
    ('token-classification', 'MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForTokenClassification'),
    ('multiple-choice', 'MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES', 'AutoModelForMultipleChoice'),
    (
        'next-sentence-prediction',
        'MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES',
        'AutoModelForNextSentencePrediction',
    ),
    (
        'audio-frame-classification',
        'MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForAudioFrameClassification',
    ),
    ('audio-xvector', 'MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES', 'AutoModelForAudioXVector'),
    (
        'document-question-answering',
        'MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForDocumentQuestionAnswering',
    ),
    (
        'visual-question-answering',
        'MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES',
        'AutoModelForVisualQuestionAnswering',
    ),
    ('image-to-text', 'MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES', 'AutoModelForVision2Seq'),
    (
        'zero-shot-image-classification',
        'MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES',
        'AutoModelForZeroShotImageClassification',
    ),
    ('depth-estimation', 'MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES', 'AutoModelForDepthEstimation'),
    ('video-classification', 'MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES', 'AutoModelForVideoClassification'),
    ('mask-generation', 'MODEL_FOR_MASK_GENERATION_MAPPING_NAMES', 'AutoModelForMaskGeneration'),
]

def        a_     (							_lowerCAmelCase       )      ->      int:
  __lowerCamelCase       :       List[str]           = re.finditer('.+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)'       ,_lowerCAmelCase       )
  return [m.group(0       ) for m in matches]

def        a_     (							)      ->      Any:
  __lowerCamelCase       :       Tuple           = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
  __lowerCamelCase       :       List[str]           = {
      config.replace('Config'       ,''       ): model_type for model_type, config in config_maping_names.items()
  }

  # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
  __lowerCamelCase       :       int           = collections.defaultdict(_lowerCAmelCase       )
  __lowerCamelCase       :       Optional[Any]           = collections.defaultdict(_lowerCAmelCase       )
  __lowerCamelCase       :       Any           = collections.defaultdict(_lowerCAmelCase       )

  # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
  for attr_name in dir(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[Any]           = None
    if _re_tf_models.match(_lowerCAmelCase       ) is not None:
      __lowerCamelCase       :       Tuple           = tf_models
      __lowerCamelCase       :       List[str]           = _re_tf_models.match(_lowerCAmelCase       ).groups()[0]
    elif _re_flax_models.match(_lowerCAmelCase       ) is not None:
      __lowerCamelCase       :       Union[str, Any]           = flax_models
      __lowerCamelCase       :       Tuple           = _re_flax_models.match(_lowerCAmelCase       ).groups()[0]
    elif _re_pt_models.match(_lowerCAmelCase       ) is not None:
      __lowerCamelCase       :       Union[str, Any]           = pt_models
      __lowerCamelCase       :       Dict           = _re_pt_models.match(_lowerCAmelCase       ).groups()[0]

    if lookup_dict is not None:
      while len(_lowerCAmelCase       ) > 0:
        if attr_name in model_prefix_to_model_type:
          __lowerCamelCase       :       Optional[Any]           = True
          break
        # Try again after removing the last word in the name
        __lowerCamelCase       :       Tuple           = ''.join(camel_case_split(_lowerCAmelCase       )[:-1]       )

  __lowerCamelCase       :       Union[str, Any]           = set(list(pt_models.keys()       ) + list(tf_models.keys()       ) + list(flax_models.keys()       )       )
  __lowerCamelCase       :       List[Any]           = list(_lowerCAmelCase       )
  all_models.sort()

  __lowerCamelCase       :       Any           = {'model_type': all_models}
  __lowerCamelCase       :       str           = [pt_models[t] for t in all_models]
  __lowerCamelCase       :       Optional[Any]           = [tf_models[t] for t in all_models]
  __lowerCamelCase       :       Tuple           = [flax_models[t] for t in all_models]

  # Now let's use the auto-mapping names to make sure
  __lowerCamelCase       :       Dict           = {}
  for t in all_models:
    if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
      __lowerCamelCase       :       Optional[Any]           = 'AutoProcessor'
    elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
      __lowerCamelCase       :       int           = 'AutoTokenizer'
    elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
      __lowerCamelCase       :       str           = 'AutoFeatureExtractor'
    else:
      # Default to AutoTokenizer if a model has nothing, for backward compatibility.
      __lowerCamelCase       :       int           = 'AutoTokenizer'

  __lowerCamelCase       :       Optional[Any]           = [processors[t] for t in all_models]

  return pd.DataFrame(_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       )      ->      Tuple:
  __lowerCamelCase       :       List[str]           = [
      transformers_module.models.auto.modeling_auto,
      transformers_module.models.auto.modeling_tf_auto,
      transformers_module.models.auto.modeling_flax_auto,
  ]
  for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
    __lowerCamelCase       :       Any           = [model_mapping, F'TF_{model_mapping}', F'FLAX_{model_mapping}']
    __lowerCamelCase       :       Optional[int]           = [auto_class, F'TF_{auto_class}', F'Flax_{auto_class}']
    # Loop through all three frameworks
    for module, cls, mapping in zip(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ):
      # The type of pipeline may not exist in this framework
      if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
        continue
      # First extract all model_names
      __lowerCamelCase       :       Any           = []
      for name in getattr(_lowerCAmelCase       ,_lowerCAmelCase       ).values():
        if isinstance(_lowerCAmelCase       ,_lowerCAmelCase       ):
          model_names.append(_lowerCAmelCase       )
        else:
          model_names.extend(list(_lowerCAmelCase       )       )

            # Add pipeline tag and auto model class for those models
      table.update({model_name: (pipeline_tag, cls) for model_name in model_names}       )

  return table

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[Any]:
  __lowerCamelCase       :       Any           = get_frameworks_table()
  __lowerCamelCase       :       Any           = Dataset.from_pandas(_lowerCAmelCase       )

  __lowerCamelCase       :       List[Any]           = hf_hub_download(
      'huggingface/transformers-metadata'       ,'pipeline_tags.json'       ,repo_type='dataset'       ,token=_lowerCAmelCase       )
  __lowerCamelCase       :       Union[str, Any]           = Dataset.from_json(_lowerCAmelCase       )
  __lowerCamelCase       :       Tuple           = {
      tags_dataset[i]['model_class']: (tags_dataset[i]['pipeline_tag'], tags_dataset[i]['auto_class'])
      for i in range(len(_lowerCAmelCase       )       )
  }
  __lowerCamelCase       :       Any           = update_pipeline_and_auto_class_table(_lowerCAmelCase       )

  # Sort the model classes to avoid some nondeterministic updates to create false update commits.
  __lowerCamelCase       :       Optional[Any]           = sorted(table.keys()       )
  __lowerCamelCase       :       Tuple           = pd.DataFrame(
      {
          'model_class': model_classes,
          'pipeline_tag': [table[m][0] for m in model_classes],
          'auto_class': [table[m][1] for m in model_classes],
      }       )
  __lowerCamelCase       :       Any           = Dataset.from_pandas(_lowerCAmelCase       )

  with tempfile.TemporaryDirectory() as tmp_dir:
    frameworks_dataset.to_json(os.path.join(_lowerCAmelCase       ,'frameworks.json'       )       )
    tags_dataset.to_json(os.path.join(_lowerCAmelCase       ,'pipeline_tags.json'       )       )

    if commit_sha is not None:
      __lowerCamelCase       :       Dict           = (
          F'Update with commit {commit_sha}\n\nSee: '
          F'https://github.com/huggingface/transformers/commit/{commit_sha}'
      )
    else:
      __lowerCamelCase       :       Optional[Any]           = 'Update'

    upload_folder(
        repo_id='huggingface/transformers-metadata'       ,folder_path=_lowerCAmelCase       ,repo_type='dataset'       ,token=_lowerCAmelCase       ,commit_message=_lowerCAmelCase       ,)

def        a_     (							)      ->      int:
  __lowerCamelCase       :       str           = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
  __lowerCamelCase       :       str           = transformers_module.pipelines.SUPPORTED_TASKS
  __lowerCamelCase       :       Union[str, Any]           = []
  for key in pipeline_tasks:
    if key not in in_table:
      __lowerCamelCase       :       str           = pipeline_tasks[key]['pt']
      if isinstance(_lowerCAmelCase       ,(list, tuple)       ):
        __lowerCamelCase       :       Optional[int]           = model[0]
      __lowerCamelCase       :       str           = model.__name__
      if model not in in_table.values():
        missing.append(_lowerCAmelCase       )

  if len(_lowerCAmelCase       ) > 0:
    __lowerCamelCase       :       Tuple           = ', '.join(_lowerCAmelCase       )
    raise ValueError(
        'The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside '
        F'`utils/update_metadata.py`: {msg}. Please add them!'       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument('--token', type=str, help='The token to use to push to the transformers-metadata dataset.')
   parser.add_argument('--commit_sha', type=str, help='The sha of the commit going with this update.')
   parser.add_argument('--check-only', action='store_true', help='Activate to just check all pipelines are present.')
   _UpperCamelCase											=						parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)











'''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      :	Tuple			,	_a      :	List[Any]			,	_a      :	Dict=2			,	_a      :	Dict=32			,	_a      :	int=16			,	_a      :	str=3			,	_a      :	Optional[int]=True			,	_a      :	List[Any]=True			,	_a      :	int=32			,	_a      :	int=4			,	_a      :	Optional[Any]=[0, 1, 2, 3]			,	_a      :	int=4			,	_a      :	Union[str, Any]=37			,	_a      :	List[str]="gelu"			,	_a      :	List[str]=0.1			,	_a      :	List[str]=0.1			,	_a      :	Union[str, Any]=0.02			,	_a      :	str=3			,	_a      :	int=[1, 384, 24, 24]			,	_a      :	Optional[Any]=True			,	_a      :	Tuple=None			,	)					->					Tuple:
    __lowerCamelCase       :       Dict           = parent
    __lowerCamelCase       :       List[Any]           = batch_size
    __lowerCamelCase       :       int           = image_size
    __lowerCamelCase       :       Any           = patch_size
    __lowerCamelCase       :       Tuple           = num_channels
    __lowerCamelCase       :       Dict           = is_training
    __lowerCamelCase       :       List[str]           = use_labels
    __lowerCamelCase       :       Union[str, Any]           = hidden_size
    __lowerCamelCase       :       List[Any]           = num_hidden_layers
    __lowerCamelCase       :       List[Any]           = backbone_out_indices
    __lowerCamelCase       :       Tuple           = num_attention_heads
    __lowerCamelCase       :       Optional[Any]           = intermediate_size
    __lowerCamelCase       :       Any           = hidden_act
    __lowerCamelCase       :       List[str]           = hidden_dropout_prob
    __lowerCamelCase       :       Tuple           = attention_probs_dropout_prob
    __lowerCamelCase       :       List[str]           = initializer_range
    __lowerCamelCase       :       Dict           = num_labels
    __lowerCamelCase       :       List[Any]           = backbone_featmap_shape
    __lowerCamelCase       :       Optional[int]           = scope
    __lowerCamelCase       :       str           = is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    __lowerCamelCase       :       Union[str, Any]           = (image_size // patch_size) ** 2
    __lowerCamelCase       :       Optional[Any]           = num_patches + 1




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[int]           = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

    __lowerCamelCase       :       List[str]           = None
    if self.use_labels:
      __lowerCamelCase       :       Dict           = ids_tensor([self.batch_size, self.image_size, self.image_size]			,	self.num_labels   )

    __lowerCamelCase       :       Dict           = self.get_config()

    return config, pixel_values, labels




  def 						_lowercase     ( self      :	Optional[Any]   )					->					List[str]:
    __lowerCamelCase       :       Optional[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=_a			,	initializer_range=self.initializer_range			,	is_hybrid=self.is_hybrid			,	backbone_config=_a			,	backbone_featmap_shape=self.backbone_featmap_shape			,	)




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	int			,	_a      :	str			,	_a      :	Optional[int]   )					->					Optional[int]:
    __lowerCamelCase       :       Any           = DPTModel(config=_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)   )




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Union[str, Any]			,	_a      :	Optional[Any]			,	_a      :	List[Any]   )					->					List[Any]:
    __lowerCamelCase       :       Dict           = self.num_labels
    __lowerCamelCase       :       List[Any]           = DPTForDepthEstimation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.predicted_depth.shape			,	(self.batch_size, self.image_size, self.image_size)   )




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Optional[Any]			,	_a      :	Tuple			,	_a      :	Tuple   )					->					List[Any]:
    __lowerCamelCase       :       Union[str, Any]           = self.num_labels
    __lowerCamelCase       :       Optional[Any]           = DPTForSemanticSegmentation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       int           = model(_a			,	labels=_a   )
    self.parent.assertEqual(
        result.logits.shape			,	(self.batch_size, self.num_labels, self.image_size, self.image_size)   )




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






@require_torch
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




  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 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[Any]           = DPTModelTester(self   )
    __lowerCamelCase       :       Union[str, Any]           = ConfigTester(self			,	config_class=_a			,	has_text_modality=_a			,	hidden_size=37   )




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




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




  def 						_lowercase     ( self      :	Dict   )					->					str:
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()

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




  def 						_lowercase     ( self      :	List[str]   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Any           = self.model_tester.prepare_config_and_inputs_for_common()

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

      __lowerCamelCase       :       Optional[int]           = ['pixel_values']
      self.assertListEqual(arg_names[:1]			,	_a   )




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					int:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    __lowerCamelCase       :       Union[str, Any]           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*_a   )




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

      __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase       :       Optional[int]           = True

      if model_class in get_values(_a   ):
        continue

      __lowerCamelCase       :       Tuple           = model_class(_a   )
      model.to(_a   )
      model.train()
      __lowerCamelCase       :       str           = self._prepare_for_class(_a			,	_a			,	return_labels=_a   )
      __lowerCamelCase       :       Dict           = model(**_a   ).loss
      loss.backward()




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					str:
    for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
        continue

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

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




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()

    __lowerCamelCase       :       Union[str, Any]           = _config_zero_init(_a   )
    for model_class in self.all_model_classes:
      __lowerCamelCase       :       List[Any]           = model_class(config=_a   )
      # Skip the check for the backbone
      __lowerCamelCase       :       Any           = []
      for name, module in model.named_modules():
        if module.__class__.__name__ == "DPTViTHybridEmbeddings":
          __lowerCamelCase       :       Dict           = [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 						_lowercase     ( self      :	Dict   )					->					Optional[int]:
    pass




  @slow
  def 						_lowercase     ( self      :	Any   )					->					int:
    for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
      __lowerCamelCase       :       Union[str, Any]           = DPTModel.from_pretrained(_a   )
      self.assertIsNotNone(_a   )




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




def        a_     (							)      ->      str:
  __lowerCamelCase       :       List[str]           = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image






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




  def 						_lowercase     ( self      :	Dict   )					->					Tuple:
    __lowerCamelCase       :       Any           = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
    __lowerCamelCase       :       Any           = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(_a   )

    __lowerCamelCase       :       Any           = prepare_img()
    __lowerCamelCase       :       Dict           = image_processor(images=_a			,	return_tensors='pt'   ).to(_a   )

    # forward pass
    with torch.no_grad():
      __lowerCamelCase       :       Any           = model(**_a   )
      __lowerCamelCase       :       Any           = outputs.predicted_depth

    # verify the predicted depth
    __lowerCamelCase       :       int           = torch.Size((1, 384, 384)   )
    self.assertEqual(predicted_depth.shape			,	_a   )

    __lowerCamelCase       :       Tuple           = 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] / 100			,	_a			,	atol=1e-4   )   )






