


"""simple docstring"""



import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image
else:
 class 					__magic_name__		:






        @staticmethod
        def  __snake_case						(   *snake_case__  :    List[str]  ,     **snake_case__  :    Dict				):

               '''simple docstring'''






               pass






def       lowerCamelCase					(a_						:Image)     ->							str:
       lowercase		:Any       							=				hashlib.mda(image.tobytes())
       return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 					__magic_name__		(			unittest.TestCase     ):
       __A			:      Optional[int]        =   MODEL_FOR_DEPTH_ESTIMATION_MAPPING






       def  __snake_case						(   self  :    int  ,     snake_case__  :    Optional[int]  ,     snake_case__  :    int  ,     snake_case__  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				DepthEstimationPipeline(model=snake_case__  ,     image_processor=snake_case__				)
              return depth_estimator, [
                  "./tests/fixtures/tests_samples/COCO/000000039769.png",
                  "./tests/fixtures/tests_samples/COCO/000000039769.png",
              ]






       def  __snake_case						(   self  :    str  ,     snake_case__  :    Tuple  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				depth_estimator('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
              self.assertEqual({'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)}  ,     snake_case__				)
              import datasets

              lowercase		:Optional[Any]       							=				datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''  ,     '''image'''  ,     split='''test'''				)
              lowercase		:str       							=				depth_estimator(
                  [
                      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				),
                      '''http://images.cocodataset.org/val2017/000000039769.jpg''',
                      # RGBA
                      dataset[0]['''file'''],
                      # LA
                      dataset[1]['''file'''],
                      # L
                      dataset[2]['''file'''],
                  ]				)
              self.assertEqual(
                  [
                      {'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)},
                      {'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)},
                      {'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)},
                      {'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)},
                      {'''predicted_depth''': ANY(torch.Tensor				), '''depth''': ANY(Image.Image				)},
                  ]  ,     snake_case__  ,     )






       @require_tf
       @unittest.skip('''Depth estimation is not implemented in TF'''				)
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              pass






       @slow
       @require_torch
       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Dict       							=				'''Intel/dpt-large'''
              lowercase		:int       							=				pipeline('''depth-estimation'''  ,     model=snake_case__				)
              lowercase		:int       							=				depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''				)
              lowercase		:List[str]       							=				hashimage(outputs['''depth''']				)

              # This seems flaky.
              # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
              self.assertEqual(nested_simplify(outputs['''predicted_depth'''].max().item()				)  ,     29.3_04				)
              self.assertEqual(nested_simplify(outputs['''predicted_depth'''].min().item()				)  ,     2.6_62				)







       @require_torch
       def  __snake_case						(   self  :    int				):

              '''simple docstring'''






              self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''				)





"""simple docstring"""



import gc
import inspect
import unittest

import torch
from parameterized import parameterized

from diffusers import PriorTransformer
from diffusers.utils import floats_tensor, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      Tuple        =   PriorTransformer
       __A			:      Any        =   "hidden_states"






       @property
       def  __snake_case						(   self  :    str				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				4
              lowercase		:List[Any]       							=				8
              lowercase		:Any       							=				7

              lowercase		:Optional[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[Any]       							=				floats_tensor((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Union[str, Any]       							=				floats_tensor((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Optional[Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Union[str, Any]       							=				4
              lowercase		:Optional[int]       							=				8
              lowercase		:str       							=				7

              lowercase		:Dict       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:Union[str, Any]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Tuple       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (4, 8)






       @property
       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              return (4, 8)






       def  __snake_case						(   self  :    Optional[Any]				):

              '''simple docstring'''






              lowercase		:Union[str, Any]       							=				{
                  '''num_attention_heads''': 2,
                  '''attention_head_dim''': 4,
                  '''num_layers''': 2,
                  '''embedding_dim''': 8,
                  '''num_embeddings''': 7,
                  '''additional_embeddings''': 4,
              }
              lowercase		:List[str]       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    List[str]				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained(
                  '''hf-internal-testing/prior-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[Any]       							=				model(**self.dummy_input				)[0]

              assert hidden_states is not None, "Make sure output is not None"






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase							,  lowercase		:List[Any]       							=				self.prepare_init_args_and_inputs_for_common()

              lowercase		:str       							=				self.model_class(**snake_case__				)
              lowercase		:Optional[Any]       							=				inspect.signature(model.forward				)
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              lowercase		:List[Any]       							=				[*signature.parameters.keys()]

              lowercase		:Optional[Any]       							=				['''hidden_states''', '''timestep''']
              self.assertListEqual(arg_names[:2]  ,     snake_case__				)







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				PriorTransformer.from_pretrained('''hf-internal-testing/prior-dummy'''				)
              lowercase		:List[Any]       							=				model.to(snake_case__				)

              if hasattr(snake_case__  ,     '''set_default_attn_processor'''				):
                     model.set_default_attn_processor()

              lowercase		:List[str]       							=				self.get_dummy_seed_input()

              with torch.no_grad():
                     lowercase		:List[Any]       							=				model(**snake_case__				)[0]

              lowercase		:Dict       							=				output[0, :5].flatten().cpu()
              print(snake_case__				)

              # Since the VAE Gaussian prior's generator is seeded on the appropriate device,
              # the expected output slices are not the same for CPU and GPU.
              lowercase		:str       							=				torch.tensor([-1.34_36, -0.28_70, 0.75_38, 0.43_68, -0.02_39]				)
              self.assertTrue(torch_all_close(snake_case__  ,     snake_case__  ,     rtol=1e-2				)				)







@slow
class 					__magic_name__		(			unittest.TestCase     ):






       def  __snake_case						(   self  :    Tuple  ,     snake_case__  :    int=1  ,     snake_case__  :    int=7_6_8  ,     snake_case__  :    Optional[Any]=7_7  ,     snake_case__  :    Union[str, Any]=0				):

              '''simple docstring'''






              torch.manual_seed(snake_case__				)
              lowercase		:Any       							=				batch_size
              lowercase		:Dict       							=				embedding_dim
              lowercase		:int       							=				num_embeddings

              lowercase		:int       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)

              lowercase		:List[str]       							=				torch.randn((batch_size, embedding_dim)				).to(snake_case__				)
              lowercase		:Optional[Any]       							=				torch.randn((batch_size, num_embeddings, embedding_dim)				).to(snake_case__				)

              return {
                  "hidden_states": hidden_states,
                  "timestep": 2,
                  "proj_embedding": proj_embedding,
                  "encoder_hidden_states": encoder_hidden_states,
              }






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              super().tearDown()
              gc.collect()
              torch.cuda.empty_cache()







       @parameterized.expand(
           [
               # fmt: off
               [1_3, [-0.58_61,  0.12_83, -0.09_31,  0.08_82,  0.44_76,  0.13_29, -0.04_98,  0.06_40]],
               [3_7, [-0.49_13,  0.01_10, -0.04_83,  0.05_41,  0.49_54, -0.01_70,  0.03_54,  0.16_51]],
               # fmt: on
           ]				)
       def  __snake_case						(   self  :    Dict  ,     snake_case__  :    Dict  ,     snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:str       							=				PriorTransformer.from_pretrained('''kandinsky-community/kandinsky-2-1-prior'''  ,     subfolder='''prior'''				)
              model.to(snake_case__				)
              lowercase		:Union[str, Any]       							=				self.get_dummy_seed_input(seed=snake_case__				)

              with torch.no_grad():
                     lowercase		:int       							=				model(**snake_case__				)[0]

              assert list(sample.shape				) == [1, 7_6_8]

              lowercase		:List[Any]       							=				sample[0, :8].flatten().cpu()
              print(snake_case__				)
              lowercase		:Union[str, Any]       							=				torch.tensor(snake_case__				)

              assert torch_all_close(snake_case__  ,     snake_case__  ,     atol=1e-3				)

