



'''simple docstring'''




import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)



def 	a			( lowerCamelCase__    ):
			'''simple docstring'''





			if model.config.model_type == "gpt2":
						return model.transformer.h[0].mlp.c_fc
			return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
						import torch
						import torch.nn as nn







						class 	_lowerCAmelCase							(			nn.Module ):


							def __init__(self       ,      lowercase       ,      lowercase							):
										super().__init__()
										A_	:						Optional[Any]	=     module
										A_	:						Union[str, Any]	=     nn.Sequential(
										    nn.Linear(module.in_features       ,      _a       ,      bias=_a							)       ,      nn.Linear(_a       ,      module.out_features       ,      bias=_a							)       ,      )
										A_	:						Union[str, Any]	=     (2.0 / (5 * min(module.in_features       ,      module.out_features							))) ** 0.5
										nn.init.normal_(self.adapter[0].weight       ,      std=_a							)
										nn.init.zeros_(self.adapter[1].weight							)
										self.adapter.to(module.weight.device							)


							def     _a	(self       ,      lowercase       ,      *lowercase       ,      **lowercase							):
										return self.module(_a       ,      *_a       ,      **_a							) + self.adapter(_a							)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	_lowerCAmelCase							(			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					'bigscience/bloom-1b7'

	# Constant values
	__SCREAMING_SNAKE_CASE   :		int					=					2.109_659_552_692_574

	__SCREAMING_SNAKE_CASE   :		Optional[int]					=					'Hello my name is'
	__SCREAMING_SNAKE_CASE   :		int					=					set()
	EXPECTED_OUTPUTS.add('Hello my name is John and I am a professional photographer. I' )
	EXPECTED_OUTPUTS.add('Hello my name is John.\nI am a friend of your father.\n' )
	EXPECTED_OUTPUTS.add('Hello my name is John Doe, I am a student at the University' )
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					10


	def     _a	(self							):
				# Models and tokenizer
				A_	:						Optional[int]	=     AutoTokenizer.from_pretrained(self.model_name							)







class 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):


	def     _a	(self							):
				super().setUp()

				# Models and tokenizer
				A_	:						str	=     AutoModelForCausalLM.from_pretrained(
				    self.model_name       ,      torch_dtype=torch.floataa       ,      device_map="""auto"""							)
				A_	:						Dict	=     AutoModelForCausalLM.from_pretrained(self.model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)


	def     _a	(self							):
				del self.model_fpaa
				del self.model_abit

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


	def     _a	(self							):
				A_	:						Optional[int]	=     self.model_abit.config

				self.assertTrue(hasattr(_a       ,      """quantization_config"""							)							)

				A_	:						Any	=     config.to_dict()
				A_	:						Any	=     config.to_diff_dict()

				A_	:						Optional[Any]	=     config.to_json_string()


	def     _a	(self							):
				from bitsandbytes.nn import Paramsabit

				A_	:						Optional[int]	=     self.model_fpaa.get_memory_footprint()
				A_	:						str	=     self.model_abit.get_memory_footprint()

				self.assertAlmostEqual(mem_fpaa / mem_abit       ,      self.EXPECTED_RELATIVE_DIFFERENCE							)
				A_	:						Union[str, Any]	=     get_some_linear_layer(self.model_abit							)
				self.assertTrue(linear.weight.__class__ == Paramsabit							)


	def     _a	(self							):
				from transformers import TaPreTrainedModel

				self.model_fpaa.get_memory_footprint()
				self.model_abit.get_memory_footprint()

				for name, module in self.model_abit.named_modules():
							if isinstance(_a       ,      torch.nn.Linear							):
										if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
													# 4-bit parameters are packed in uint8 variables
													self.assertTrue(module.weight.dtype == torch.uinta							)


	def     _a	(self							):
				A_	:						Optional[Any]	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							)
				A_	:						Dict	=     self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,      max_new_tokens=10							)

				self.assertIn(self.tokenizer.decode(output_sequences[0]       ,      skip_special_tokens=_a							)       ,      self.EXPECTED_OUTPUTS							)


	def     _a	(self							):
				A_	:						Dict	=     BitsAndBytesConfig()
				A_	:						Optional[int]	=     True

				A_	:						Any	=     AutoModelForCausalLM.from_pretrained(
				    self.model_name       ,      quantization_config=_a       ,      device_map="""auto"""							)

				A_	:						Tuple	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							)
				A_	:						List[str]	=     model_abit_from_config.generate(
				    input_ids=encoded_input["""input_ids"""].to(0							)       ,      max_new_tokens=10							)

				self.assertIn(self.tokenizer.decode(output_sequences[0]       ,      skip_special_tokens=_a							)       ,      self.EXPECTED_OUTPUTS							)


	def     _a	(self							):
				with self.assertRaises(_a							), tempfile.TemporaryDirectory() as tmpdirname:
							self.model_abit.save_pretrained(_a							)


	def     _a	(self							):
				A_	:						Union[str, Any]	=     BitsAndBytesConfig()

				with self.assertRaises(_a							):
							A_	:						Tuple	=     AutoModelForCausalLM.from_pretrained(
							    self.model_name       ,      quantization_config=_a       ,      load_in_abit=_a       ,      device_map="""auto"""       ,      bnb_abit_quant_type="""nf4"""       ,      )


	def     _a	(self							):
				with self.assertRaises(_a							):
							# Tries with `str`
							self.model_abit.to("""cpu"""							)

				with self.assertRaises(_a							):
							# Tries with a `dtype``
							self.model_abit.to(torch.floataa							)

				with self.assertRaises(_a							):
							# Tries with a `device`
							self.model_abit.to(torch.device("""cuda:0"""							)							)

				with self.assertRaises(_a							):
							# Tries with a `device`
							self.model_abit.float()

				with self.assertRaises(_a							):
							# Tries with a `device`
							self.model_abit.half()

				# Test if we did not break anything
				A_	:						Optional[int]	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							)

				A_	:						Tuple	=     self.model_fpaa.to(torch.floataa							)
				A_	:						List[Any]	=     self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,      max_new_tokens=10							)

				# Check this does not throw an error
				A_	:						Tuple	=     self.model_fpaa.to("""cpu"""							)

				# Check this does not throw an error
				A_	:						int	=     self.model_fpaa.half()

				# Check this does not throw an error
				A_	:						Any	=     self.model_fpaa.float()


	def     _a	(self							):
				A_	:						Tuple	=     AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""       ,      load_in_abit=_a       ,      device_map="""auto"""							)
				self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)







@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	_lowerCAmelCase							(			unittest.TestCase ):


	@classmethod
	def     _a	(cls							):
				A_	:						Optional[int]	=     't5-small'
				A_	:						Tuple	=     'google/flan-t5-small'  # flan-t5 uses dense-act instead of dense-relu-dense
				A_	:						int	=     AutoTokenizer.from_pretrained(cls.model_name							)
				A_	:						List[Any]	=     'Translate in German: Hello, my dog is cute'


	def     _a	(self							):
				gc.collect()
				torch.cuda.empty_cache()


	def     _a	(self							):
				from transformers import TaForConditionalGeneration

				A_	:						Optional[int]	=     TaForConditionalGeneration._keep_in_fpaa_modules
				A_	:						Dict	=     None

				# test with `t5-small`
				A_	:						str	=     TaForConditionalGeneration.from_pretrained(self.model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)
				A_	:						Optional[int]	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							).to(0							)
				A_	:						Any	=     model.generate(**_a							)

				# test with `flan-t5-small`
				A_	:						Union[str, Any]	=     TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)
				A_	:						List[Any]	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							).to(0							)
				A_	:						Union[str, Any]	=     model.generate(**_a							)
				A_	:						List[str]	=     modules


	def     _a	(self							):
				import bitsandbytes as bnb

				from transformers import TaForConditionalGeneration

				# test with `t5-small`
				A_	:						List[Any]	=     TaForConditionalGeneration.from_pretrained(self.model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)

				# there was a bug with decoders - this test checks that it is fixed
				self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q       ,      bnb.nn.Linearabit							)							)

				A_	:						List[str]	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							).to(0							)
				A_	:						Union[str, Any]	=     model.generate(**_a							)

				# test with `flan-t5-small`
				A_	:						Optional[int]	=     TaForConditionalGeneration.from_pretrained(
				    self.dense_act_model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)
				A_	:						int	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							).to(0							)
				A_	:						Any	=     model.generate(**_a							)







class 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):


	def     _a	(self							):
				super().setUp()
				# model_name
				A_	:						Optional[Any]	=     'bigscience/bloom-560m'
				A_	:						int	=     't5-small'

				# Different types of model

				A_	:						Dict	=     AutoModel.from_pretrained(self.model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)
				# Sequence classification model
				A_	:						Optional[Any]	=     AutoModelForSequenceClassification.from_pretrained(
				    self.model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)
				# CausalLM model
				A_	:						str	=     AutoModelForCausalLM.from_pretrained(self.model_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)
				# Seq2seq model
				A_	:						Any	=     AutoModelForSeqaSeqLM.from_pretrained(
				    self.seq_to_seq_name       ,      load_in_abit=_a       ,      device_map="""auto"""							)


	def     _a	(self							):
				del self.base_model
				del self.sequence_model
				del self.model_abit
				del self.seq_to_seq_model

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


	def     _a	(self							):
				from bitsandbytes.nn import Paramsabit

				self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

				# Other heads should be nn.Parameter
				self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
				self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
				self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)







class 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):


	def     _a	(self							):
				super().setUp()


	def     _a	(self							):
				del self.pipe

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


	def     _a	(self							):
				A_	:						Union[str, Any]	=     pipeline(
				    """text-generation"""       ,      model=self.model_name       ,      model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}       ,      max_new_tokens=self.MAX_NEW_TOKENS       ,      )

				# Real second forward pass
				A_	:						Dict	=     self.pipe(self.input_text							)
				self.assertIn(pipeline_output[0]["""generated_text"""]       ,      self.EXPECTED_OUTPUTS							)







@require_torch_multi_gpu
class 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):


	def     _a	(self							):
				super().setUp()


	def     _a	(self							):
				A_	:						Tuple	=     AutoModelForCausalLM.from_pretrained(
				    self.model_name       ,      load_in_abit=_a       ,      device_map="""balanced"""							)

				# Check correct device map
				self.assertEqual(set(model_parallel.hf_device_map.values()							)       ,      {0, 1}							)

				# Check that inference pass works on the model
				A_	:						List[Any]	=     self.tokenizer(self.input_text       ,      return_tensors="""pt"""							)

				# Second real batch
				A_	:						Any	=     model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,      max_new_tokens=10							)
				self.assertIn(self.tokenizer.decode(output_parallel[0]       ,      skip_special_tokens=_a							)       ,      self.EXPECTED_OUTPUTS							)







class 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):


	def     _a	(self							):
				A_	:						Tuple	=     'facebook/opt-350m'
				super().setUp()


	def     _a	(self							):
				if version.parse(importlib.metadata.version("""bitsandbytes"""							)							) < version.parse("""0.37.0"""							):
							return

				# Step 1: freeze all parameters
				A_	:						Dict	=     AutoModelForCausalLM.from_pretrained(self.model_name       ,      load_in_abit=_a							)

				self.assertEqual(set(model.hf_device_map.values()							)       ,      {torch.cuda.current_device()}							)

				for param in model.parameters():
							A_	:						str	=     False  # freeze the model - train adapters later
							if param.ndim == 1:
										# cast the small parameters (e.g. layernorm) to fp32 for stability
										A_	:						Optional[int]	=     param.data.to(torch.floataa							)

										# Step 2: add adapters
				for _, module in model.named_modules():
							if "OPTAttention" in repr(type(_a							)							):
										A_	:						Optional[int]	=     LoRALayer(module.q_proj       ,      rank=16							)
										A_	:						Optional[Any]	=     LoRALayer(module.k_proj       ,      rank=16							)
										A_	:						Tuple	=     LoRALayer(module.v_proj       ,      rank=16							)

										# Step 3: dummy batch
				A_	:						Optional[int]	=     self.tokenizer("""Test batch """       ,      return_tensors="""pt"""							).to(0							)

				# Step 4: Check if the gradient is not None
				with torch.cuda.amp.autocast():
							A_	:						Optional[Any]	=     model.forward(**_a							)
							out.logits.norm().backward()

				for module in model.modules():
							if isinstance(_a       ,      _a							):
										self.assertTrue(module.adapter[1].weight.grad is not None							)
										self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
							elif isinstance(_a       ,      nn.Embedding							):
										self.assertTrue(module.weight.grad is None							)







class 	_lowerCAmelCase							(			SCREAMING_SNAKE_CASE_ ):
	__SCREAMING_SNAKE_CASE   :		str					=					'gpt2-xl'
	__SCREAMING_SNAKE_CASE   :		Tuple					=					3.3_191_854_854_152_187




'''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      :	Tuple			,	_a      :	List[Any]			,	_a      :	Dict=2			,	_a      :	Dict=32			,	_a      :	int=16			,	_a      :	str=3			,	_a      :	Optional[int]=True			,	_a      :	List[Any]=True			,	_a      :	int=32			,	_a      :	int=4			,	_a      :	Optional[Any]=[0, 1, 2, 3]			,	_a      :	int=4			,	_a      :	Union[str, Any]=37			,	_a      :	List[str]="gelu"			,	_a      :	List[str]=0.1			,	_a      :	List[str]=0.1			,	_a      :	Union[str, Any]=0.02			,	_a      :	str=3			,	_a      :	int=[1, 384, 24, 24]			,	_a      :	Optional[Any]=True			,	_a      :	Tuple=None			,	)					->					Tuple:
    __lowerCamelCase       :       Dict           = parent
    __lowerCamelCase       :       List[Any]           = batch_size
    __lowerCamelCase       :       int           = image_size
    __lowerCamelCase       :       Any           = patch_size
    __lowerCamelCase       :       Tuple           = num_channels
    __lowerCamelCase       :       Dict           = is_training
    __lowerCamelCase       :       List[str]           = use_labels
    __lowerCamelCase       :       Union[str, Any]           = hidden_size
    __lowerCamelCase       :       List[Any]           = num_hidden_layers
    __lowerCamelCase       :       List[Any]           = backbone_out_indices
    __lowerCamelCase       :       Tuple           = num_attention_heads
    __lowerCamelCase       :       Optional[Any]           = intermediate_size
    __lowerCamelCase       :       Any           = hidden_act
    __lowerCamelCase       :       List[str]           = hidden_dropout_prob
    __lowerCamelCase       :       Tuple           = attention_probs_dropout_prob
    __lowerCamelCase       :       List[str]           = initializer_range
    __lowerCamelCase       :       Dict           = num_labels
    __lowerCamelCase       :       List[Any]           = backbone_featmap_shape
    __lowerCamelCase       :       Optional[int]           = scope
    __lowerCamelCase       :       str           = is_hybrid
    # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
    __lowerCamelCase       :       Union[str, Any]           = (image_size // patch_size) ** 2
    __lowerCamelCase       :       Optional[Any]           = num_patches + 1




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[int]           = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]   )

    __lowerCamelCase       :       List[str]           = None
    if self.use_labels:
      __lowerCamelCase       :       Dict           = ids_tensor([self.batch_size, self.image_size, self.image_size]			,	self.num_labels   )

    __lowerCamelCase       :       Dict           = self.get_config()

    return config, pixel_values, labels




  def 						_lowercase     ( self      :	Optional[Any]   )					->					List[str]:
    __lowerCamelCase       :       Optional[Any]           = {
        '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=_a			,	initializer_range=self.initializer_range			,	is_hybrid=self.is_hybrid			,	backbone_config=_a			,	backbone_featmap_shape=self.backbone_featmap_shape			,	)




  def 						_lowercase     ( self      :	Optional[int]			,	_a      :	int			,	_a      :	str			,	_a      :	Optional[int]   )					->					Optional[int]:
    __lowerCamelCase       :       Any           = DPTModel(config=_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.last_hidden_state.shape			,	(self.batch_size, self.seq_length, self.hidden_size)   )




  def 						_lowercase     ( self      :	Optional[Any]			,	_a      :	Union[str, Any]			,	_a      :	Optional[Any]			,	_a      :	List[Any]   )					->					List[Any]:
    __lowerCamelCase       :       Dict           = self.num_labels
    __lowerCamelCase       :       List[Any]           = DPTForDepthEstimation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       List[str]           = model(_a   )
    self.parent.assertEqual(result.predicted_depth.shape			,	(self.batch_size, self.image_size, self.image_size)   )




  def 						_lowercase     ( self      :	List[str]			,	_a      :	Optional[Any]			,	_a      :	Tuple			,	_a      :	Tuple   )					->					List[Any]:
    __lowerCamelCase       :       Union[str, Any]           = self.num_labels
    __lowerCamelCase       :       Optional[Any]           = DPTForSemanticSegmentation(_a   )
    model.to(_a   )
    model.eval()
    __lowerCamelCase       :       int           = model(_a			,	labels=_a   )
    self.parent.assertEqual(
        result.logits.shape			,	(self.batch_size, self.num_labels, self.image_size, self.image_size)   )




  def 						_lowercase     ( self      :	Optional[Any]   )					->					Union[str, Any]:
    __lowerCamelCase       :       Tuple           = self.prepare_config_and_inputs()
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       str           = config_and_inputs
    __lowerCamelCase       :       Optional[Any]           = {'pixel_values': pixel_values}
    return config, inputs_dict






@require_torch
class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_   ,   SCREAMING_SNAKE_CASE_   ,   unittest.TestCase     ):
  """simple docstring"""




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

  a_												=False
  a_												=False
  a_												=False




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Optional[Any]           = DPTModelTester(self   )
    __lowerCamelCase       :       Union[str, Any]           = ConfigTester(self			,	config_class=_a			,	has_text_modality=_a			,	hidden_size=37   )




  def 						_lowercase     ( self      :	Tuple   )					->					Union[str, Any]:
    self.config_tester.run_common_tests()




  @unittest.skip(reason='DPT does not use inputs_embeds'   )
  def 						_lowercase     ( self      :	Dict   )					->					Union[str, Any]:
    pass




  def 						_lowercase     ( self      :	Dict   )					->					str:
    __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
      __lowerCamelCase       :       Union[str, Any]           = model_class(_a   )
      self.assertIsInstance(model.get_input_embeddings()			,	(nn.Module)   )
      __lowerCamelCase       :       List[Any]           = model.get_output_embeddings()
      self.assertTrue(x is None or isinstance(_a			,	nn.Linear   )   )




  def 						_lowercase     ( self      :	List[str]   )					->					Any:
    __lowerCamelCase       ,__lowerCamelCase       :       Any           = self.model_tester.prepare_config_and_inputs_for_common()

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

      __lowerCamelCase       :       Optional[int]           = ['pixel_values']
      self.assertListEqual(arg_names[:1]			,	_a   )




  def 						_lowercase     ( self      :	Dict   )					->					Any:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_a   )




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					int:
    __lowerCamelCase       :       Dict           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					Any:
    __lowerCamelCase       :       Union[str, Any]           = self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_semantic_segmentation(*_a   )




  def 						_lowercase     ( self      :	Optional[int]   )					->					Dict:
    for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
        continue

      __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase       :       Optional[int]           = True

      if model_class in get_values(_a   ):
        continue

      __lowerCamelCase       :       Tuple           = model_class(_a   )
      model.to(_a   )
      model.train()
      __lowerCamelCase       :       str           = self._prepare_for_class(_a			,	_a			,	return_labels=_a   )
      __lowerCamelCase       :       Dict           = model(**_a   ).loss
      loss.backward()




  def 						_lowercase     ( self      :	Union[str, Any]   )					->					str:
    for model_class in self.all_model_classes:
      if model_class.__name__ == "DPTForDepthEstimation":
        continue

      __lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase       :       Tuple           = False
      __lowerCamelCase       :       List[str]           = True

      if model_class in get_values(_a   ) or not model_class.supports_gradient_checkpointing:
        continue
      __lowerCamelCase       :       Optional[int]           = model_class(_a   )
      model.to(_a   )
      model.gradient_checkpointing_enable()
      model.train()
      __lowerCamelCase       :       List[Any]           = self._prepare_for_class(_a			,	_a			,	return_labels=_a   )
      __lowerCamelCase       :       str           = model(**_a   ).loss
      loss.backward()




  def 						_lowercase     ( self      :	Dict   )					->					Optional[Any]:
    __lowerCamelCase       ,__lowerCamelCase       :       int           = self.model_tester.prepare_config_and_inputs_for_common()

    __lowerCamelCase       :       Union[str, Any]           = _config_zero_init(_a   )
    for model_class in self.all_model_classes:
      __lowerCamelCase       :       List[Any]           = model_class(config=_a   )
      # Skip the check for the backbone
      __lowerCamelCase       :       Any           = []
      for name, module in model.named_modules():
        if module.__class__.__name__ == "DPTViTHybridEmbeddings":
          __lowerCamelCase       :       Dict           = [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 						_lowercase     ( self      :	Dict   )					->					Optional[int]:
    pass




  @slow
  def 						_lowercase     ( self      :	Any   )					->					int:
    for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
      __lowerCamelCase       :       Union[str, Any]           = DPTModel.from_pretrained(_a   )
      self.assertIsNotNone(_a   )




  def 						_lowercase     ( self      :	List[Any]   )					->					List[Any]:
    # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
    __lowerCamelCase       ,__lowerCamelCase       :       str           = self.model_tester.prepare_config_and_inputs_for_common()
    __lowerCamelCase       :       List[Any]           = 'add'
    with self.assertRaises(_a   ):
      __lowerCamelCase       :       int           = DPTForDepthEstimation(_a   )




def        a_     (							)      ->      str:
  __lowerCamelCase       :       List[str]           = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image






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




  def 						_lowercase     ( self      :	Dict   )					->					Tuple:
    __lowerCamelCase       :       Any           = DPTImageProcessor.from_pretrained('Intel/dpt-hybrid-midas'   )
    __lowerCamelCase       :       Any           = DPTForDepthEstimation.from_pretrained('Intel/dpt-hybrid-midas'   ).to(_a   )

    __lowerCamelCase       :       Any           = prepare_img()
    __lowerCamelCase       :       Dict           = image_processor(images=_a			,	return_tensors='pt'   ).to(_a   )

    # forward pass
    with torch.no_grad():
      __lowerCamelCase       :       Any           = model(**_a   )
      __lowerCamelCase       :       Any           = outputs.predicted_depth

    # verify the predicted depth
    __lowerCamelCase       :       int           = torch.Size((1, 384, 384)   )
    self.assertEqual(predicted_depth.shape			,	_a   )

    __lowerCamelCase       :       Tuple           = torch.tensor(
        [[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]]   ).to(_a   )

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






