




'''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_				:




					'''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=False,    __UpperCamelCase=None,    ):
							_snake_case      			=			parent
							_snake_case      			=			batch_size
							_snake_case      			=			image_size
							_snake_case      			=			patch_size
							_snake_case      			=			num_channels
							_snake_case      			=			is_training
							_snake_case      			=			use_labels
							_snake_case      			=			hidden_size
							_snake_case      			=			num_hidden_layers
							_snake_case      			=			backbone_out_indices
							_snake_case      			=			num_attention_heads
							_snake_case      			=			intermediate_size
							_snake_case      			=			hidden_act
							_snake_case      			=			hidden_dropout_prob
							_snake_case      			=			attention_probs_dropout_prob
							_snake_case      			=			initializer_range
							_snake_case      			=			num_labels
							_snake_case      			=			scope
							_snake_case      			=			is_hybrid
							# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
							_snake_case      			=			(image_size // patch_size) ** 2
							_snake_case      			=			num_patches + 1






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

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

							_snake_case      			=			self.get_config()

							return config, pixel_values, labels






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							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=__UpperCamelCase,    initializer_range=self.initializer_range,    is_hybrid=self.is_hybrid,    )






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					):
							_snake_case      			=			DPTModel(config=__UpperCamelCase					)
							model.to(__UpperCamelCase					)
							model.eval()
							_snake_case      			=			model(__UpperCamelCase					)
							self.parent.assertEqual(result.last_hidden_state.shape,    (self.batch_size, self.seq_length, self.hidden_size)					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					):
							_snake_case      			=			self.num_labels
							_snake_case      			=			DPTForDepthEstimation(__UpperCamelCase					)
							model.to(__UpperCamelCase					)
							model.eval()
							_snake_case      			=			model(__UpperCamelCase					)
							self.parent.assertEqual(result.predicted_depth.shape,    (self.batch_size, self.image_size, self.image_size)					)






					def 							__SCREAMING_SNAKE_CASE      (       self,    __UpperCamelCase,    __UpperCamelCase,    __UpperCamelCase					):
							_snake_case      			=			self.num_labels
							_snake_case      			=			DPTForSemanticSegmentation(__UpperCamelCase					)
							model.to(__UpperCamelCase					)
							model.eval()
							_snake_case      			=			model(__UpperCamelCase,    labels=__UpperCamelCase					)
							self.parent.assertEqual(
							    result.logits.shape,    (self.batch_size, self.num_labels, self.image_size, self.image_size)					)






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





@require_torch
class 	lowerCamelCase_				(			UpperCAmelCase_,			UpperCAmelCase_,			unittest.TestCase				):




					'''simple docstring'''






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

					lowercase__       =					False
					lowercase__       =					False
					lowercase__       =					False






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			DPTModelTester(self					)
							_snake_case      			=			ConfigTester(self,    config_class=__UpperCamelCase,    has_text_modality=__UpperCamelCase,    hidden_size=37					)






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






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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case			,       _snake_case      			=			self.model_tester.prepare_config_and_inputs_for_common()

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case			,       _snake_case      			=			self.model_tester.prepare_config_and_inputs_for_common()

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

									_snake_case      			=			["pixel_values"]
									self.assertListEqual(arg_names[:1],    __UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase					)






					def 							__SCREAMING_SNAKE_CASE      (       self					):

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

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

									if model_class in get_values(__UpperCamelCase					):
											continue

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):

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

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

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case			,       _snake_case      			=			self.model_tester.prepare_config_and_inputs_for_common()

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






					@slow
					def 							__SCREAMING_SNAKE_CASE      (       self					):

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






def 						_lowercase						(      ):



		'''simple docstring'''







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





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




					'''simple docstring'''






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			DPTImageProcessor.from_pretrained("Intel/dpt-large"					)
							_snake_case      			=			DPTForDepthEstimation.from_pretrained("Intel/dpt-large"					).to(__UpperCamelCase					)

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

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

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

							_snake_case      			=			torch.tensor(
							    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]					).to(__UpperCamelCase					)

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"					)
							_snake_case      			=			DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"					).to(__UpperCamelCase					)

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

							# forward pass
							with torch.no_grad():
									_snake_case      			=			model(**__UpperCamelCase					)

							# verify the logits
							_snake_case      			=			torch.Size((1, 150, 480, 480)					)
							self.assertEqual(outputs.logits.shape,    __UpperCamelCase					)

							_snake_case      			=			torch.tensor(
							    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]					).to(__UpperCamelCase					)

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






					def 							__SCREAMING_SNAKE_CASE      (       self					):
							_snake_case      			=			DPTImageProcessor.from_pretrained("Intel/dpt-large-ade"					)
							_snake_case      			=			DPTForSemanticSegmentation.from_pretrained("Intel/dpt-large-ade"					).to(__UpperCamelCase					)

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

							# forward pass
							with torch.no_grad():
									_snake_case      			=			model(**__UpperCamelCase					)

							_snake_case      			=			outputs.logits.detach().cpu()

							_snake_case      			=			image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase,    target_sizes=[(500, 300)]					)
							_snake_case      			=			torch.Size((500, 300)					)
							self.assertEqual(segmentation[0].shape,    __UpperCamelCase					)

							_snake_case      			=			image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase					)
							_snake_case      			=			torch.Size((480, 480)					)
							self.assertEqual(segmentation[0].shape,    __UpperCamelCase					)






"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__       =	{
    "configuration_open_llama": ["OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP", "OpenLlamaConfig"],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizer"]

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	["LlamaTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__       =	[
					    "OpenLlamaForCausalLM",
					    "OpenLlamaModel",
					    "OpenLlamaPreTrainedModel",
					    "OpenLlamaForSequenceClassification",
					]


if TYPE_CHECKING:
					from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from transformers import LlamaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_open_llama import (
										    OpenLlamaForCausalLM,
										    OpenLlamaForSequenceClassification,
										    OpenLlamaModel,
										    OpenLlamaPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE__       =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


