

import inspect
import unittest

from transformers 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
      from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import GLPNImageProcessor


class     lowerCamelCase_   ( _lowerCAmelCase):




 def 						a					( self       ):
   __UpperCAmelCase  :     str		=    self.config_class(**self.inputs_dict       )
   self.parent.assertTrue(hasattr(_UpperCAmelCase     ,     'hidden_sizes'       )       )
   self.parent.assertTrue(hasattr(_UpperCAmelCase     ,     'num_attention_heads'       )       )
   self.parent.assertTrue(hasattr(_UpperCAmelCase     ,     'num_encoder_blocks'       )       )



class     lowerCamelCase_   :




 def __init__( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase=13     ,     _UpperCAmelCase=64     ,     _UpperCAmelCase=3     ,     _UpperCAmelCase=4     ,     _UpperCAmelCase=[2, 2, 2, 2]     ,     _UpperCAmelCase=[8, 4, 2, 1]     ,     _UpperCAmelCase=[16, 32, 64, 128]     ,     _UpperCAmelCase=[1, 4, 8, 16]     ,     _UpperCAmelCase=[1, 2, 4, 8]     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase=True     ,     _UpperCAmelCase="gelu"     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.1     ,     _UpperCAmelCase=0.0_2     ,     _UpperCAmelCase=16     ,     _UpperCAmelCase=3     ,     _UpperCAmelCase=None     ,     ):
   __UpperCAmelCase  :     Optional[Any]		=    parent
   __UpperCAmelCase  :     Any		=    batch_size
   __UpperCAmelCase  :     str		=    image_size
   __UpperCAmelCase  :     Union[str, Any]		=    num_channels
   __UpperCAmelCase  :     Tuple		=    num_encoder_blocks
   __UpperCAmelCase  :     Tuple		=    sr_ratios
   __UpperCAmelCase  :     List[Any]		=    depths
   __UpperCAmelCase  :     List[Any]		=    hidden_sizes
   __UpperCAmelCase  :     Any		=    downsampling_rates
   __UpperCAmelCase  :     Union[str, Any]		=    num_attention_heads
   __UpperCAmelCase  :     Optional[Any]		=    is_training
   __UpperCAmelCase  :     int		=    use_labels
   __UpperCAmelCase  :     Tuple		=    hidden_act
   __UpperCAmelCase  :     Any		=    hidden_dropout_prob
   __UpperCAmelCase  :     List[str]		=    attention_probs_dropout_prob
   __UpperCAmelCase  :     Optional[Any]		=    initializer_range
   __UpperCAmelCase  :     int		=    decoder_hidden_size
   __UpperCAmelCase  :     Optional[int]		=    num_labels
   __UpperCAmelCase  :     Union[str, Any]		=    scope




 def 						a					( self       ):
   __UpperCAmelCase  :     int		=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

   __UpperCAmelCase  :     Optional[int]		=    None
   if self.use_labels:
     __UpperCAmelCase  :     Union[str, Any]		=    ids_tensor([self.batch_size, self.image_size, self.image_size]     ,     self.num_labels       )

   __UpperCAmelCase  :     int		=    self.get_config()
   return config, pixel_values, labels




 def 						a					( self       ):
   return GLPNConfig(
       image_size=self.image_size     ,     num_channels=self.num_channels     ,     num_encoder_blocks=self.num_encoder_blocks     ,     depths=self.depths     ,     hidden_sizes=self.hidden_sizes     ,     num_attention_heads=self.num_attention_heads     ,     hidden_act=self.hidden_act     ,     hidden_dropout_prob=self.hidden_dropout_prob     ,     attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,     initializer_range=self.initializer_range     ,     decoder_hidden_size=self.decoder_hidden_size     ,     )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Any		=    GLPNModel(config=_UpperCAmelCase       )
   model.to(_UpperCAmelCase       )
   model.eval()
   __UpperCAmelCase  :     List[Any]		=    model(_UpperCAmelCase       )
   __UpperCAmelCase  :     Any		=    self.image_size // (self.downsampling_rates[-1] * 2)
   self.parent.assertEqual(
       result.last_hidden_state.shape     ,     (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)       )




 def 						a					( self     ,     _UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
   __UpperCAmelCase  :     Union[str, Any]		=    self.num_labels
   __UpperCAmelCase  :     Any		=    GLPNForDepthEstimation(_UpperCAmelCase       )
   model.to(_UpperCAmelCase       )
   model.eval()
   __UpperCAmelCase  :     List[Any]		=    model(_UpperCAmelCase       )
   self.parent.assertEqual(result.predicted_depth.shape     ,     (self.batch_size, self.image_size, self.image_size)       )
   __UpperCAmelCase  :     List[Any]		=    model(_UpperCAmelCase     ,     labels=_UpperCAmelCase       )
   self.parent.assertEqual(result.predicted_depth.shape     ,     (self.batch_size, self.image_size, self.image_size)       )




 def 						a					( self       ):
   __UpperCAmelCase  :     Tuple		=    self.prepare_config_and_inputs()
   __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     Dict		=    config_and_inputs
   __UpperCAmelCase  :     Tuple		=    {'pixel_values': pixel_values}
   return config, inputs_dict



@require_torch
class     lowerCamelCase_   ( _lowerCAmelCase  ,							_lowerCAmelCase  ,							unittest.TestCase):
 _lowerCAmelCase       		=  (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 _lowerCAmelCase       		=  (
     {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
 )

 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  False
 _lowerCAmelCase       		=  False




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    GLPNModelTester(self       )
   __UpperCAmelCase  :     int		=    GLPNConfigTester(self     ,     config_class=_UpperCAmelCase       )




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




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase  :     str		=    self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_depth_estimation(*_UpperCAmelCase       )




 @unittest.skip('GLPN does not use inputs_embeds'       )
 def 						a					( self       ):
   pass




 @unittest.skip('GLPN does not have get_input_embeddings method and get_output_embeddings methods'       )
 def 						a					( self       ):
   pass




 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     Any		=    self.model_tester.prepare_config_and_inputs_for_common()

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

     __UpperCAmelCase  :     Optional[Any]		=    ['pixel_values']
     self.assertListEqual(arg_names[:1]     ,     _UpperCAmelCase       )




 def 						a					( self       ):
   __UpperCAmelCase, __UpperCAmelCase  :     List[Any]		=    self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase  :     Tuple		=    True

   for model_class in self.all_model_classes:
     __UpperCAmelCase  :     Optional[Any]		=    True
     __UpperCAmelCase  :     Dict		=    False
     __UpperCAmelCase  :     Union[str, Any]		=    True
     __UpperCAmelCase  :     Optional[int]		=    model_class(_UpperCAmelCase       )
     model.to(_UpperCAmelCase       )
     model.eval()
     with torch.no_grad():
       __UpperCAmelCase  :     Any		=    model(**self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )       )
     __UpperCAmelCase  :     Tuple		=    outputs.attentions

     __UpperCAmelCase  :     Union[str, Any]		=    sum(self.model_tester.depths       )
     self.assertEqual(len(_UpperCAmelCase       )     ,     _UpperCAmelCase       )

     # check that output_attentions also work using config
     del inputs_dict["output_attentions"]
     __UpperCAmelCase  :     List[str]		=    True
     __UpperCAmelCase  :     List[str]		=    model_class(_UpperCAmelCase       )
     model.to(_UpperCAmelCase       )
     model.eval()
     with torch.no_grad():
       __UpperCAmelCase  :     Union[str, Any]		=    model(**self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )       )
     __UpperCAmelCase  :     Optional[int]		=    outputs.attentions

     self.assertEqual(len(_UpperCAmelCase       )     ,     _UpperCAmelCase       )

     # verify the first attentions (first block, first layer)
     __UpperCAmelCase  :     int		=    (self.model_tester.image_size // 4) ** 2
     __UpperCAmelCase  :     List[str]		=    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
     self.assertListEqual(
         list(attentions[0].shape[-3:]       )     ,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,     )

     # verify the last attentions (last block, last layer)
     __UpperCAmelCase  :     List[Any]		=    (self.model_tester.image_size // 32) ** 2
     __UpperCAmelCase  :     List[str]		=    (self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
     self.assertListEqual(
         list(attentions[-1].shape[-3:]       )     ,     [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]     ,     )
     __UpperCAmelCase  :     Dict		=    len(_UpperCAmelCase       )

     # Check attention is always last and order is fine
     __UpperCAmelCase  :     Union[str, Any]		=    True
     __UpperCAmelCase  :     str		=    True
     __UpperCAmelCase  :     Optional[Any]		=    model_class(_UpperCAmelCase       )
     model.to(_UpperCAmelCase       )
     model.eval()
     with torch.no_grad():
       __UpperCAmelCase  :     Any		=    model(**self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )       )

     self.assertEqual(out_len + 1     ,     len(_UpperCAmelCase       )       )

     __UpperCAmelCase  :     int		=    outputs.attentions

     self.assertEqual(len(_UpperCAmelCase       )     ,     _UpperCAmelCase       )
     # verify the first attentions (first block, first layer)
     __UpperCAmelCase  :     Optional[Any]		=    (self.model_tester.image_size // 4) ** 2
     __UpperCAmelCase  :     Any		=    (self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
     self.assertListEqual(
         list(self_attentions[0].shape[-3:]       )     ,     [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,     )




 def 						a					( self       ):
   def check_hidden_states_output(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       ):
     __UpperCAmelCase  :     List[str]		=    model_class(_UpperCAmelCase       )
     model.to(_UpperCAmelCase       )
     model.eval()

     with torch.no_grad():
       __UpperCAmelCase  :     List[Any]		=    model(**self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase       )       )

     __UpperCAmelCase  :     int		=    outputs.hidden_states

     __UpperCAmelCase  :     Tuple		=    self.model_tester.num_encoder_blocks
     self.assertEqual(len(_UpperCAmelCase       )     ,     _UpperCAmelCase       )

     # verify the first hidden states (first block)
     self.assertListEqual(
         list(hidden_states[0].shape[-3:]       )     ,     [
             self.model_tester.hidden_sizes[0],
             self.model_tester.image_size // 4,
             self.model_tester.image_size // 4,
         ]     ,     )

   __UpperCAmelCase, __UpperCAmelCase  :     Union[str, Any]		=    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase  :     Optional[int]		=    True
     check_hidden_states_output(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     __UpperCAmelCase  :     Optional[Any]		=    True

     check_hidden_states_output(_UpperCAmelCase     ,     _UpperCAmelCase     ,     _UpperCAmelCase       )




 def 						a					( self       ):
   if not self.model_tester.is_training:
     return

   __UpperCAmelCase, __UpperCAmelCase  :     int		=    self.model_tester.prepare_config_and_inputs_for_common()
   __UpperCAmelCase  :     int		=    True

   for model_class in self.all_model_classes:
     if model_class in get_values(_UpperCAmelCase       ):
       continue
     # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
     # this can then be incorporated into _prepare_for_class in test_modeling_common.py
     if model_class.__name__ == "GLPNForDepthEstimation":
       __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase  :     Optional[int]		=    inputs_dict['pixel_values'].shape
       __UpperCAmelCase  :     int		=    torch.zeros(
           [self.model_tester.batch_size, height, width]     ,     device=_UpperCAmelCase       ).long()
     __UpperCAmelCase  :     int		=    model_class(_UpperCAmelCase       )
     model.to(_UpperCAmelCase       )
     model.train()
     __UpperCAmelCase  :     Union[str, Any]		=    self._prepare_for_class(_UpperCAmelCase     ,     _UpperCAmelCase     ,     return_labels=_UpperCAmelCase       )
     __UpperCAmelCase  :     Optional[int]		=    model(**_UpperCAmelCase       ).loss
     loss.backward()




 @slow
 def 						a					( self       ):
   for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase  :     str		=    GLPNModel.from_pretrained(_UpperCAmelCase       )
     self.assertIsNotNone(_UpperCAmelCase       )





def    _snake_case		()       ->		Optional[int]:
  __UpperCAmelCase  :     Union[str, Any]		=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
  return image







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




 @slow
 def 						a					( self       ):
   __UpperCAmelCase  :     Any		=    GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
   __UpperCAmelCase  :     str		=    GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(_UpperCAmelCase       )

   __UpperCAmelCase  :     Dict		=    prepare_img()
   __UpperCAmelCase  :     Tuple		=    image_processor(images=_UpperCAmelCase     ,     return_tensors='pt'       ).to(_UpperCAmelCase       )

   # forward pass
   with torch.no_grad():
     __UpperCAmelCase  :     str		=    model(**_UpperCAmelCase       )

   # verify the predicted depth
   __UpperCAmelCase  :     int		=    torch.Size([1, 480, 640]       )
   self.assertEqual(outputs.predicted_depth.shape     ,     _UpperCAmelCase       )

   __UpperCAmelCase  :     int		=    torch.tensor(
       [[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]]       ).to(_UpperCAmelCase       )

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








import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionDualGuidedPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_SCREAMING_SNAKE_CASE          = False


@nightly
@require_torch_gpu
class     lowerCamelCase_   ( unittest.TestCase):




 def 						a					( self       ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()




 def 						a					( self       ):
   __UpperCAmelCase  :     Dict		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   # remove text_unet
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Any		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     Union[str, Any]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(_UpperCAmelCase       )
     __UpperCAmelCase  :     List[str]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained(_UpperCAmelCase       )

   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     List[str]		=    generator.manual_seed(0       )
   __UpperCAmelCase  :     Optional[int]		=    pipe(
       prompt='first prompt'     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=2     ,     output_type='numpy'     ,     ).images

   assert np.abs(image - new_image       ).sum() < 1E-5, "Models don't have the same forward pass"




 def 						a					( self       ):
   __UpperCAmelCase  :     Optional[Any]		=    VersatileDiffusionDualGuidedPipeline.from_pretrained('shi-labs/versatile-diffusion'       )
   pipe.remove_unused_weights()
   pipe.to(_UpperCAmelCase       )
   pipe.set_progress_bar_config(disable=_UpperCAmelCase       )

   __UpperCAmelCase  :     Tuple		=    'cyberpunk 2077'
   __UpperCAmelCase  :     Dict		=    load_image(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
   __UpperCAmelCase  :     Optional[Any]		=    torch.manual_seed(0       )
   __UpperCAmelCase  :     List[Any]		=    pipe(
       prompt=_UpperCAmelCase     ,     image=_UpperCAmelCase     ,     text_to_image_strength=0.7_5     ,     generator=_UpperCAmelCase     ,     guidance_scale=7.5     ,     num_inference_steps=50     ,     output_type='numpy'     ,     ).images

   __UpperCAmelCase  :     Tuple		=    image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   __UpperCAmelCase  :     Tuple		=    np.array([0.0_7_8_7, 0.0_8_4_9, 0.0_8_2_6, 0.0_8_1_2, 0.0_8_0_7, 0.0_7_9_5, 0.0_8_1_8, 0.0_7_9_8, 0.0_7_7_9]       )

   assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2





