

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 					a__						:
			"""simple docstring"""







			def __init__(    self      ,      lowercase      ,      lowercase=2      ,      lowercase=32      ,      lowercase=16      ,      lowercase=3      ,      lowercase=True      ,      lowercase=True      ,      lowercase=32      ,      lowercase=4      ,      lowercase=[0, 1, 2, 3]      ,      lowercase=4      ,      lowercase=37      ,      lowercase="gelu"      ,      lowercase=0.1      ,      lowercase=0.1      ,      lowercase=0.02      ,      lowercase=3      ,      lowercase=[1, 384, 24, 24]      ,      lowercase=True      ,      lowercase=None      ,      )  ->						List[Any]:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

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

							A__        =			self.get_config()

							return config, pixel_values, labels


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			{
							    "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=lowercase      ,      initializer_range=self.initializer_range      ,      is_hybrid=self.is_hybrid      ,      backbone_config=lowercase      ,      backbone_featmap_shape=self.backbone_featmap_shape      ,      )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						str:
							'''simple docstring'''






							A__        =			DPTModel(config=lowercase       )
							model.to(lowercase       )
							model.eval()
							A__        =			model(lowercase       )
							self.parent.assertEqual(result.last_hidden_state.shape      ,      (self.batch_size, self.seq_length, self.hidden_size)       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						Tuple:
							'''simple docstring'''






							A__        =			self.num_labels
							A__        =			DPTForDepthEstimation(lowercase       )
							model.to(lowercase       )
							model.eval()
							A__        =			model(lowercase       )
							self.parent.assertEqual(result.predicted_depth.shape      ,      (self.batch_size, self.image_size, self.image_size)       )


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase      ,      lowercase       )  ->						List[str]:
							'''simple docstring'''






							A__        =			self.num_labels
							A__        =			DPTForSemanticSegmentation(lowercase       )
							model.to(lowercase       )
							model.eval()
							A__        =			model(lowercase      ,      labels=lowercase       )
							self.parent.assertEqual(
							    result.logits.shape      ,      (self.batch_size, self.num_labels, self.image_size, self.image_size)       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






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

@require_torch
class 					a__						(					snake_case							,	snake_case							,	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      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__        =			DPTModelTester(self       )
							A__        =			ConfigTester(self      ,      config_class=lowercase      ,      has_text_modality=lowercase      ,      hidden_size=37       )


			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							self.config_tester.run_common_tests()


			@unittest.skip(reason="DPT does not use inputs_embeds"       )
			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							pass


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

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


			def      UpperCamelCase		(    self       )  ->						List[str]:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

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

											A__        =			["pixel_values"]
											self.assertListEqual(arg_names[:1]      ,      lowercase       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*lowercase       )


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






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

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

											if model_class in get_values(lowercase       ):
															continue

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


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






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

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

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


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()

							A__        =			_config_zero_init(lowercase       )
							for model_class in self.all_model_classes:
											A__        =			model_class(config=lowercase       )
											# Skip the check for the backbone
											A__        =			[]
											for name, module in model.named_modules():
															if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																			A__        =			[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      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							pass


			@slow
			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											A__        =			DPTModel.from_pretrained(lowercase       )
											self.assertIsNotNone(lowercase       )


			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__    ,				A__        =			self.model_tester.prepare_config_and_inputs_for_common()
							A__        =			"add"
							with self.assertRaises(lowercase       ):
											A__        =			DPTForDepthEstimation(lowercase       )



def 					lowerCAmelCase__							(	)			->      List[Any]:




				'''simple docstring'''





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





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







			def      UpperCamelCase		(    self       )  ->						Tuple:
							'''simple docstring'''






							A__        =			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"       )
							A__        =			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"       ).to(lowercase       )

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

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

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

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

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




import gc
import unittest

import numpy as np
import torch

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()

class 					a__						(					snake_case							,	unittest.TestCase			):
			"""simple docstring"""







			__lowerCamelCase        =       DanceDiffusionPipeline
			__lowerCamelCase        =       UNCONDITIONAL_AUDIO_GENERATION_PARAMS
			__lowerCamelCase        =       PipelineTesterMixin.required_optional_params - {
			    'callback',
			    'latents',
			    'callback_steps',
			    'output_type',
			    'num_images_per_prompt',
			}
			__lowerCamelCase        =       UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
			__lowerCamelCase        =       False
			__lowerCamelCase        =       False


			def      UpperCamelCase		(    self       )  ->						Optional[int]:
							'''simple docstring'''






							torch.manual_seed(0       )
							A__        =			UNetaDModel(
							    block_out_channels=(32, 32, 64)      ,      extra_in_channels=16      ,      sample_size=512      ,      sample_rate=16000      ,      in_channels=2      ,      out_channels=2      ,      flip_sin_to_cos=lowercase      ,      use_timestep_embedding=lowercase      ,      time_embedding_type="fourier"      ,      mid_block_type="UNetMidBlock1D"      ,      down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D")      ,      up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip")      ,      )
							A__        =			IPNDMScheduler()

							A__        =			{
							    "unet": unet,
							    "scheduler": scheduler,
							}
							return components


			def      UpperCamelCase		(    self      ,      lowercase      ,      lowercase=0       )  ->						Union[str, Any]:
							'''simple docstring'''






							if str(lowercase       ).startswith("mps"       ):
											A__        =			torch.manual_seed(lowercase       )
							else:
											A__        =			torch.Generator(device=lowercase       ).manual_seed(lowercase       )
							A__        =			{
							    "batch_size": 1,
							    "generator": generator,
							    "num_inference_steps": 4,
							}
							return inputs


			def      UpperCamelCase		(    self       )  ->						Any:
							'''simple docstring'''






							A__        =			"cpu"  # ensure determinism for the device-dependent torch.Generator
							A__        =			self.get_dummy_components()
							A__        =			DanceDiffusionPipeline(**lowercase       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			self.get_dummy_inputs(lowercase       )
							A__        =			pipe(**lowercase       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, components["unet"].sample_size)
							A__        =			np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000]       )
							assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Dict:
							'''simple docstring'''






							return super().test_save_load_local()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						Optional[Any]:
							'''simple docstring'''






							return super().test_save_load_optional_components()


			@skip_mps
			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							return super().test_attention_slicing_forward_pass()


			def      UpperCamelCase		(    self       )  ->						str:
							'''simple docstring'''






							super().test_inference_batch_single_identical(expected_max_diff=3e-3       )

@slow
@require_torch_gpu
class 					a__						(					unittest.TestCase			):
			"""simple docstring"""







			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






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


			def      UpperCamelCase		(    self       )  ->						int:
							'''simple docstring'''






							A__        =			torch_device

							A__        =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			torch.manual_seed(0       )
							A__        =			pipe(generator=lowercase      ,      num_inference_steps=100      ,      audio_length_in_s=4.096       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							A__        =			np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020]       )

							assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2


			def      UpperCamelCase		(    self       )  ->						List[Any]:
							'''simple docstring'''






							A__        =			torch_device

							A__        =			DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k"      ,      torch_dtype=torch.floataa       )
							A__        =			pipe.to(lowercase       )
							pipe.set_progress_bar_config(disable=lowercase       )

							A__        =			torch.manual_seed(0       )
							A__        =			pipe(generator=lowercase      ,      num_inference_steps=100      ,      audio_length_in_s=4.096       )
							A__        =			output.audios

							A__        =			audio[0, -3:, -3:]

							assert audio.shape == (1, 2, pipe.unet.sample_size)
							A__        =			np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341]       )

							assert np.abs(audio_slice.flatten() - expected_slice       ).max() < 1e-2

