


'''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 	snake_case       :





  """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=[1, 384, 24, 24]     ,    UpperCamelCase=True     ,    UpperCamelCase=None     ,    ):







   """simple docstring"""
   lowerCamelCase_						=    parent
   lowerCamelCase_						=    batch_size
   lowerCamelCase_						=    image_size
   lowerCamelCase_						=    patch_size
   lowerCamelCase_						=    num_channels
   lowerCamelCase_						=    is_training
   lowerCamelCase_						=    use_labels
   lowerCamelCase_						=    hidden_size
   lowerCamelCase_						=    num_hidden_layers
   lowerCamelCase_						=    backbone_out_indices
   lowerCamelCase_						=    num_attention_heads
   lowerCamelCase_						=    intermediate_size
   lowerCamelCase_						=    hidden_act
   lowerCamelCase_						=    hidden_dropout_prob
   lowerCamelCase_						=    attention_probs_dropout_prob
   lowerCamelCase_						=    initializer_range
   lowerCamelCase_						=    num_labels
   lowerCamelCase_						=    backbone_featmap_shape
   lowerCamelCase_						=    scope
   lowerCamelCase_						=    is_hybrid
   # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
   lowerCamelCase_						=    (image_size // patch_size) ** 2
   lowerCamelCase_						=    num_patches + 1






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

   lowerCamelCase_						=    None
   if self.use_labels:
    lowerCamelCase_						=    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,    self.num_labels	)

   lowerCamelCase_						=    self.get_config()

   return config, pixel_values, labels






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    {
       "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     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    DPTModel(config=A_	)
   model.to(A_	)
   model.eval()
   lowerCamelCase_						=    model(A_	)
   self.parent.assertEqual(result.last_hidden_state.shape     ,    (self.batch_size, self.seq_length, self.hidden_size)	)






  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    self.num_labels
   lowerCamelCase_						=    DPTForDepthEstimation(A_	)
   model.to(A_	)
   model.eval()
   lowerCamelCase_						=    model(A_	)
   self.parent.assertEqual(result.predicted_depth.shape     ,    (self.batch_size, self.image_size, self.image_size)	)






  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    self.num_labels
   lowerCamelCase_						=    DPTForSemanticSegmentation(A_	)
   model.to(A_	)
   model.eval()
   lowerCamelCase_						=    model(A_     ,    labels=A_	)
   self.parent.assertEqual(
       result.logits.shape     ,    (self.batch_size, self.num_labels, self.image_size, self.image_size)	)







  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    self.prepare_config_and_inputs()
   lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_						=    config_and_inputs
   lowerCamelCase_						=    {"pixel_values": pixel_values}
   return config, inputs_dict





@require_torch
class 	snake_case       (       lowercase     ,		lowercase     ,		unittest.TestCase     ):





  """simple docstring"""




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

  _lowerCamelCase						       =    False
  _lowerCamelCase						       =    False
  _lowerCamelCase						       =    False






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    DPTModelTester(self	)
   lowerCamelCase_						=    ConfigTester(self     ,    config_class=A_     ,    has_text_modality=A_     ,    hidden_size=37	)






  def     snake_case						(       self	):







   """simple docstring"""
   self.config_tester.run_common_tests()






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







   """simple docstring"""
   pass






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_					,lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs_for_common()

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






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_					,lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs_for_common()

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

    lowerCamelCase_						=    ["pixel_values"]
    self.assertListEqual(arg_names[:1]     ,    A_	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*A_	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_depth_estimation(*A_	)






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_semantic_segmentation(*A_	)






  def     snake_case						(       self	):







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

    lowerCamelCase_					,lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs_for_common()
    lowerCamelCase_						=    True

    if model_class in get_values(A_	):
     continue

    lowerCamelCase_						=    model_class(A_	)
    model.to(A_	)
    model.train()
    lowerCamelCase_						=    self._prepare_for_class(A_     ,    A_     ,    return_labels=A_	)
    lowerCamelCase_						=    model(**A_	).loss
    loss.backward()






  def     snake_case						(       self	):







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

    lowerCamelCase_					,lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs_for_common()
    lowerCamelCase_						=    False
    lowerCamelCase_						=    True

    if model_class in get_values(A_	) or not model_class.supports_gradient_checkpointing:
     continue
    lowerCamelCase_						=    model_class(A_	)
    model.to(A_	)
    model.gradient_checkpointing_enable()
    model.train()
    lowerCamelCase_						=    self._prepare_for_class(A_     ,    A_     ,    return_labels=A_	)
    lowerCamelCase_						=    model(**A_	).loss
    loss.backward()






  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_					,lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs_for_common()

   lowerCamelCase_						=    _config_zero_init(A_	)
   for model_class in self.all_model_classes:
    lowerCamelCase_						=    model_class(config=A_	)
    # Skip the check for the backbone
    lowerCamelCase_						=    []
    for name, module in model.named_modules():
     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
      lowerCamelCase_						=    [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     snake_case						(       self	):







   """simple docstring"""
   pass






  @slow
  def     snake_case						(       self	):







   """simple docstring"""
   for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
    lowerCamelCase_						=    DPTModel.from_pretrained(A_	)
    self.assertIsNotNone(A_	)







  def     snake_case						(       self	):







   """simple docstring"""
   # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
   lowerCamelCase_					,lowerCamelCase_						=    self.model_tester.prepare_config_and_inputs_for_common()
   lowerCamelCase_						=    "add"
   with self.assertRaises(A_	):
    lowerCamelCase_						=    DPTForDepthEstimation(A_	)





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







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





  """simple docstring"""




  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"	)
   lowerCamelCase_						=    DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"	).to(A_	)

   lowerCamelCase_						=    prepare_img()
   lowerCamelCase_						=    image_processor(images=A_     ,    return_tensors="pt"	).to(A_	)

   # forward pass
   with torch.no_grad():
    lowerCamelCase_						=    model(**A_	)
    lowerCamelCase_						=    outputs.predicted_depth

   # verify the predicted depth
   lowerCamelCase_						=    torch.Size((1, 384, 384)	)
   self.assertEqual(predicted_depth.shape     ,    A_	)

   lowerCamelCase_						=    torch.tensor(
       [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]	).to(A_	)

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










from __future__ import annotations

import requests

UpperCamelCase_												=	set(
    'approved_at_utc approved_by author_flair_background_color\nauthor_flair_css_class author_flair_richtext author_flair_template_id author_fullname\nauthor_premium can_mod_post category clicked content_categories created_utc downs\nedited gilded gildings hidden hide_score is_created_from_ads_ui is_meta\nis_original_content is_reddit_media_domain is_video link_flair_css_class\nlink_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title\nname permalink pwls quarantine saved score secure_media secure_media_embed selftext\nsubreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type\ntotal_awards_received ups upvote_ratio url user_reports'.split()
)






def 				_UpperCAmelCase			(    A				,							A = 1				,							A = "new"				,							A = None     ):




   '''simple docstring'''

   UpperCAmelCase__						=wanted_data or []
   if invalid_search_terms := ", ".join(sorted(set(A     ) - valid_terms     )     ):
      UpperCAmelCase__						=F"""Invalid search term: {invalid_search_terms}"""
      raise ValueError(A     )
   UpperCAmelCase__						=requests.get(
       F"""https://reddit.com/r/{subreddit}/{age}.json?limit={limit}"""				,							headers={"User-agent": "A random string"}				,							)
   if response.status_code == 429:
      raise requests.HTTPError

   UpperCAmelCase__						=response.json()
   if not wanted_data:
      return {id_: data["data"]["children"][id_] for id_ in range(A     )}

   UpperCAmelCase__						={}
   for id_ in range(A     ):
      UpperCAmelCase__						={
          item: data["data"]["children"][id_]["data"][item] for item in wanted_data
      }
   return data_dict


if __name__ == "__main__":
    # If you get Error 429, that means you are rate limited.Try after some time
    print(get_subreddit_data('learnpython', wanted_data=['title', 'url', 'selftext']))



