





'''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_		:




 def __init__(	self			,     _lowerCAmelCase			,     _lowerCAmelCase=2			,     _lowerCAmelCase=32			,     _lowerCAmelCase=16			,     _lowerCAmelCase=3			,     _lowerCAmelCase=True			,     _lowerCAmelCase=True			,     _lowerCAmelCase=32			,     _lowerCAmelCase=4			,     _lowerCAmelCase=[0, 1, 2, 3]			,     _lowerCAmelCase=4			,     _lowerCAmelCase=37			,     _lowerCAmelCase="gelu"			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=3			,     _lowerCAmelCase=[1, 384, 24, 24]			,     _lowerCAmelCase=True			,     _lowerCAmelCase=None			,     )    ->							Dict:
     _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		)    ->							Tuple:
     _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		)    ->							Union[str, Any]:
     _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=_lowerCAmelCase			,     initializer_range=self.initializer_range			,     is_hybrid=self.is_hybrid			,     backbone_config=_lowerCAmelCase			,     backbone_featmap_shape=self.backbone_featmap_shape			,     )




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[Any]:
     _lowerCAmelCase				=      DPTModel(config=_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase				=      self.num_labels
     _lowerCAmelCase				=      DPTForDepthEstimation(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase		)
     self.parent.assertEqual(result.predicted_depth.shape			,     (self.batch_size, self.image_size, self.image_size)		)




 def 							_snake_case   (	self			,     _lowerCAmelCase			,     _lowerCAmelCase			,     _lowerCAmelCase		)    ->							Optional[Any]:
     _lowerCAmelCase				=      self.num_labels
     _lowerCAmelCase				=      DPTForSemanticSegmentation(_lowerCAmelCase		)
     model.to(_lowerCAmelCase		)
     model.eval()
     _lowerCAmelCase				=      model(_lowerCAmelCase			,     labels=_lowerCAmelCase		)
     self.parent.assertEqual(
         result.logits.shape			,     (self.batch_size, self.num_labels, self.image_size, self.image_size)		)





 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase				=      self.prepare_config_and_inputs()
     _lowerCAmelCase			,			_lowerCAmelCase			,			_lowerCAmelCase				=      config_and_inputs
     _lowerCAmelCase				=      {"pixel_values": pixel_values}
     return config, inputs_dict







@require_torch
class    lowerCAmelCase_		(		__magic_name__						,__magic_name__						,unittest.TestCase		):
 __lowerCamelCase						:							Any         =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
 __lowerCamelCase						:							Any         =					(
     {
         "depth-estimation": DPTForDepthEstimation,
         "feature-extraction": DPTModel,
         "image-segmentation": DPTForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __lowerCamelCase						:							Dict         =					False
 __lowerCamelCase						:							Union[str, Any]         =					False
 __lowerCamelCase						:							Tuple         =					False




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase				=      DPTModelTester(self		)
     _lowerCAmelCase				=      ConfigTester(self			,     config_class=_lowerCAmelCase			,     has_text_modality=_lowerCAmelCase			,     hidden_size=37		)




 def 							_snake_case   (	self		)    ->							str:
     self.config_tester.run_common_tests()




 @unittest.skip(reason="DPT does not use inputs_embeds"		)
 def 							_snake_case   (	self		)    ->							Dict:
     pass




 def 							_snake_case   (	self		)    ->							Tuple:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

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




 def 							_snake_case   (	self		)    ->							int:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         _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]			,     _lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							List[Any]:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_depth_estimation(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[int]:
     _lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_semantic_segmentation(*_lowerCAmelCase		)




 def 							_snake_case   (	self		)    ->							Optional[int]:
     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(_lowerCAmelCase		):
             continue

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




 def 							_snake_case   (	self		)    ->							Tuple:
     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(_lowerCAmelCase		) or not model_class.supports_gradient_checkpointing:
             continue
         _lowerCAmelCase				=      model_class(_lowerCAmelCase		)
         model.to(_lowerCAmelCase		)
         model.gradient_checkpointing_enable()
         model.train()
         _lowerCAmelCase				=      self._prepare_for_class(_lowerCAmelCase			,     _lowerCAmelCase			,     return_labels=_lowerCAmelCase		)
         _lowerCAmelCase				=      model(**_lowerCAmelCase		).loss
         loss.backward()




 def 							_snake_case   (	self		)    ->							List[str]:
     _lowerCAmelCase			,			_lowerCAmelCase				=      self.model_tester.prepare_config_and_inputs_for_common()

     _lowerCAmelCase				=      _config_zero_init(_lowerCAmelCase		)
     for model_class in self.all_model_classes:
         _lowerCAmelCase				=      model_class(config=_lowerCAmelCase		)
         # 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		)    ->							Tuple:
     pass




 @slow
 def 							_snake_case   (	self		)    ->							List[str]:
     for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
         _lowerCAmelCase				=      DPTModel.from_pretrained(_lowerCAmelCase		)
         self.assertIsNotNone(_lowerCAmelCase		)





 def 							_snake_case   (	self		)    ->							Any:
     # 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(_lowerCAmelCase		):
         _lowerCAmelCase				=      DPTForDepthEstimation(_lowerCAmelCase		)







def 	__a():



    '''simple docstring'''





    _lowerCAmelCase				=      Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
    return image





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




 def 							_snake_case   (	self		)    ->							Any:
     _lowerCAmelCase				=      DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"		)
     _lowerCAmelCase				=      DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"		).to(_lowerCAmelCase		)

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

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

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

     _lowerCAmelCase				=      torch.tensor(
         [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]		).to(_lowerCAmelCase		)

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









'''simple docstring'''


from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							{
    "facebook/s2t-wav2vec2-large-en-de": (
        "https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json"
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							str         =					"speech_to_text_2"
 __lowerCamelCase						:							Union[str, Any]         =					["past_key_values"]
 __lowerCamelCase						:							Tuple         =					{"num_attention_heads": "decoder_attention_heads", "hidden_size": "d_model"}




 def __init__(	self			,     _lowerCAmelCase=10000			,     _lowerCAmelCase=6			,     _lowerCAmelCase=2048			,     _lowerCAmelCase=4			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=True			,     _lowerCAmelCase="relu"			,     _lowerCAmelCase=256			,     _lowerCAmelCase=0.1			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.0			,     _lowerCAmelCase=0.02			,     _lowerCAmelCase=2			,     _lowerCAmelCase=True			,     _lowerCAmelCase=1			,     _lowerCAmelCase=0			,     _lowerCAmelCase=2			,     _lowerCAmelCase=1024			,     **_lowerCAmelCase			,     )    ->							Dict:
     _lowerCAmelCase				=      vocab_size
     _lowerCAmelCase				=      d_model
     _lowerCAmelCase				=      decoder_ffn_dim
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      decoder_attention_heads
     _lowerCAmelCase				=      dropout
     _lowerCAmelCase				=      attention_dropout
     _lowerCAmelCase				=      activation_dropout
     _lowerCAmelCase				=      activation_function
     _lowerCAmelCase				=      init_std
     _lowerCAmelCase				=      decoder_layerdrop
     _lowerCAmelCase				=      use_cache
     _lowerCAmelCase				=      decoder_layers
     _lowerCAmelCase				=      scale_embedding  # scale factor will be sqrt(d_model) if True
     _lowerCAmelCase				=      max_target_positions

     super().__init__(
         pad_token_id=_lowerCAmelCase			,     bos_token_id=_lowerCAmelCase			,     eos_token_id=_lowerCAmelCase			,     decoder_start_token_id=_lowerCAmelCase			,     **_lowerCAmelCase			,     )


