
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 							UpperCAmelCase				:




	'''simple docstring'''





	def __init__(	self :			List[str]							,A :			Union[str, Any]							,A :			List[Any]=2							,A :			Optional[Any]=32							,A :			Optional[Any]=16							,A :			Optional[int]=3							,A :			int=True							,A :			Optional[Any]=True							,A :			Optional[int]=32							,A :			Optional[int]=4							,A :			Optional[Any]=[0, 1, 2, 3]							,A :			Union[str, Any]=4							,A :			Optional[Any]=37							,A :			List[str]="gelu"							,A :			List[str]=0.1							,A :			Optional[Any]=0.1							,A :			Tuple=0.02							,A :			Optional[int]=3							,A :			List[str]=[1, 3_84, 24, 24]							,A :			int=True							,A :			Tuple=None							,):
					__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 :			Union[str, Any]			):
					__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 :			Optional[Any]			):
					__A             =			{
					    "global_padding": "same",
					    "layer_type": "bottleneck",
					    "depths": [3, 4, 9],
					    "out_features": ["stage1", "stage2", "stage3"],
					    "embedding_dynamic_padding": True,
					    "hidden_sizes": [96, 1_92, 3_84, 7_68],
					    "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=A							,initializer_range=self.initializer_range							,is_hybrid=self.is_hybrid							,backbone_config=A							,backbone_featmap_shape=self.backbone_featmap_shape							,)






	def     UpperCamelCase_  (	self :			Tuple							,A :			Tuple							,A :			Optional[Any]							,A :			Dict			):
					__A             =			DPTModel(config=A			)
					model.to(A			)
					model.eval()
					__A             =			model(A			)
					self.parent.assertEqual(result.last_hidden_state.shape							,(self.batch_size, self.seq_length, self.hidden_size)			)






	def     UpperCamelCase_  (	self :			Optional[Any]							,A :			Tuple							,A :			int							,A :			Dict			):
					__A             =			self.num_labels
					__A             =			DPTForDepthEstimation(A			)
					model.to(A			)
					model.eval()
					__A             =			model(A			)
					self.parent.assertEqual(result.predicted_depth.shape							,(self.batch_size, self.image_size, self.image_size)			)






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			List[Any]							,A :			int							,A :			Tuple			):
					__A             =			self.num_labels
					__A             =			DPTForSemanticSegmentation(A			)
					model.to(A			)
					model.eval()
					__A             =			model(A							,labels=A			)
					self.parent.assertEqual(
					    result.logits.shape							,(self.batch_size, self.num_labels, self.image_size, self.image_size)			)







	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			self.prepare_config_and_inputs()
					__A      ,							__A      ,							__A             =			config_and_inputs
					__A             =			{"pixel_values": pixel_values}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE      ,      unittest.TestCase  ):




	'''simple docstring'''





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

	snake_case_    =					False
	snake_case_    =					False
	snake_case_    =					False






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			DPTModelTester(self			)
					__A             =			ConfigTester(self							,config_class=A							,has_text_modality=A							,hidden_size=37			)






	def     UpperCamelCase_  (	self :			int			):
					self.config_tester.run_common_tests()






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






	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

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






	def     UpperCamelCase_  (	self :			List[Any]			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									__A             =			model_class(A			)
									__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]							,A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A			)






	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_depth_estimation(*A			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*A			)






	def     UpperCamelCase_  (	self :			Dict			):
					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(A			):
													continue

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






	def     UpperCamelCase_  (	self :			Any			):
					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(A			) or not model_class.supports_gradient_checkpointing:
													continue
									__A             =			model_class(A			)
									model.to(A			)
									model.gradient_checkpointing_enable()
									model.train()
									__A             =			self._prepare_for_class(A							,A							,return_labels=A			)
									__A             =			model(**A			).loss
									loss.backward()






	def     UpperCamelCase_  (	self :			int			):
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()

					__A             =			_config_zero_init(A			)
					for model_class in self.all_model_classes:
									__A             =			model_class(config=A			)
									# 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			):
					pass






	@slow
	def     UpperCamelCase_  (	self :			Tuple			):
					for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
									__A             =			DPTModel.from_pretrained(A			)
									self.assertIsNotNone(A			)







	def     UpperCamelCase_  (	self :			Dict			):
					# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
					__A      ,							__A             =			self.model_tester.prepare_config_and_inputs_for_common()
					__A             =			"add"
					with self.assertRaises(A			):
									__A             =			DPTForDepthEstimation(A			)





def 						UpperCAmelCase      (     )		->     Any:







				"""simple docstring"""



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





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




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			int			):
					__A             =			DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"			)
					__A             =			DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"			).to(A			)

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

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

					# verify the predicted depth
					__A             =			torch.Size((1, 3_84, 3_84)			)
					self.assertEqual(predicted_depth.shape							,A			)

					__A             =			torch.tensor(
					    [[[5.64_37, 5.61_46, 5.65_11], [5.43_71, 5.56_49, 5.59_58], [5.52_15, 5.51_84, 5.52_93]]]			).to(A			)

					self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00							,A							,atol=1E-4			)			)


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional

from packaging import version


if TYPE_CHECKING:
					from ... import PreTrainedTokenizer, TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging


SCREAMING_SNAKE_CASE							:Optional[Any]										=    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Union[str, Any]										=    {
    'bigscience/bloom': 'https://huggingface.co/bigscience/bloom/resolve/main/config.json',
    'bigscience/bloom-560m': 'https://huggingface.co/bigscience/bloom-560m/blob/main/config.json',
    'bigscience/bloom-1b1': 'https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json',
    'bigscience/bloom-1b7': 'https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json',
    'bigscience/bloom-3b': 'https://huggingface.co/bigscience/bloom-3b/blob/main/config.json',
    'bigscience/bloom-7b1': 'https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json',
}





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					"bloom"
	snake_case_    =					["past_key_values"]
	snake_case_    =					{
	    "num_hidden_layers": "n_layer",
	    "num_attention_heads": "n_head",
	}






	def __init__(	self :			Optional[Any]							,A :			List[Any]=25_08_80							,A :			Optional[int]=64							,A :			List[Any]=2							,A :			Optional[int]=8							,A :			str=1E-5							,A :			str=0.02							,A :			int=True							,A :			Optional[Any]=1							,A :			int=2							,A :			str=False							,A :			Dict=0.0							,A :			List[Any]=0.0							,A :			str=1							,A :			List[Any]=False							,**A :			List[Any]							,):
					__A             =			vocab_size
					# Backward compatibility with n_embed kwarg
					__A             =			kwargs.pop("n_embed"							,A			)
					__A             =			hidden_size if n_embed is None else n_embed
					__A             =			n_layer
					__A             =			n_head
					__A             =			layer_norm_epsilon
					__A             =			initializer_range
					__A             =			use_cache
					__A             =			pretraining_tp
					__A             =			apply_residual_connection_post_layernorm
					__A             =			hidden_dropout
					__A             =			attention_dropout

					__A             =			bos_token_id
					__A             =			eos_token_id
					__A             =			slow_but_exact

					super().__init__(bos_token_id=A							,eos_token_id=A							,**A			)





class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					version.parse("1.12"  )






	def __init__(	self :			str							,A :			PretrainedConfig							,A :			str = "default"							,A :			List[PatchingSpec] = None							,A :			bool = False							,):
					super().__init__(A							,task=A							,patching_specs=A							,use_past=A			)
					if not getattr(self._config							,"pad_token_id"							,A			):
									# TODO: how to do that better?
									__A             =			0






	@property
	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					__A             =			OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}			)
					if self.use_past:
									# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
									self.fill_with_past_key_values_(A							,direction="inputs"							,inverted_values_shape=A			)
									__A             =			{0: "batch", 1: "past_sequence + sequence"}
					else:
									__A             =			{0: "batch", 1: "sequence"}

					return common_inputs






	@property
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					return self._config.n_layer






	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					return self._config.n_head






	@property
	def     UpperCamelCase_  (	self :			Optional[int]			):
					return 1E-3






	def     UpperCamelCase_  (	self :			Any							,A :			"PreTrainedTokenizer"							,A :			int = -1							,A :			int = -1							,A :			bool = False							,A :			Optional["TensorType"] = None							,):
					__A             =			super(A							,self			).generate_dummy_inputs(
					    A							,batch_size=A							,seq_length=A							,is_pair=A							,framework=A			)

					# We need to order the input in the way they appears in the forward()
					__A             =			OrderedDict({"input_ids": common_inputs["input_ids"]}			)

					# Need to add the past_keys
					if self.use_past:
									if not is_torch_available():
													raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed."			)
									else:
													import torch

													__A      ,							__A             =			common_inputs["input_ids"].shape
													# Not using the same length for past_key_values
													__A             =			seqlen + 2
													__A             =			self._config.hidden_size // self.num_attention_heads
													__A             =			(
													    batch * self.num_attention_heads,
													    head_dim,
													    past_key_values_length,
													)
													__A             =			(
													    batch * self.num_attention_heads,
													    past_key_values_length,
													    head_dim,
													)
													__A             =			[
													    (torch.zeros(A			), torch.zeros(A			)) for _ in range(self.num_layers			)
													]

					__A             =			common_inputs["attention_mask"]
					if self.use_past:
									__A             =			ordered_inputs["attention_mask"].dtype
									__A             =			torch.cat(
									    [ordered_inputs["attention_mask"], torch.ones(A							,A							,dtype=A			)]							,dim=1			)

					return ordered_inputs







	@property
	def     UpperCamelCase_  (	self :			int			):
					return 13
