



"""simple docstring"""




from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowerCAmelCase    : str							=							logging.get_logger(__name__)  # pylint: disable=invalid-name






class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase						):



      @register_to_config
      def __init__(					self					:     Any					,     A					:     bool					,     A					:     Optional[int] = None					,     A					:     Optional[int] = None						):
           super().__init__()

           _UpperCAmelCase      :     List[str]        =   learnable

           if self.learnable:
                assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
                assert length is not None, "learnable=True requires `length` to be set"

                _UpperCAmelCase      :     int        =   torch.zeros(A					,     A						)
           else:
                _UpperCAmelCase      :     Optional[Any]        =   None

           _UpperCAmelCase      :     Optional[Any]        =   torch.nn.Parameter(A						)







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       VQModel
      __SCREAMING_SNAKE_CASE						:       CLIPTextModel
      __SCREAMING_SNAKE_CASE						:       CLIPTokenizer
      __SCREAMING_SNAKE_CASE						:       TransformeraDModel
      __SCREAMING_SNAKE_CASE						:       LearnedClassifierFreeSamplingEmbeddings
      __SCREAMING_SNAKE_CASE						:       VQDiffusionScheduler



      def __init__(					self					:     Any					,     A					:     VQModel					,     A					:     CLIPTextModel					,     A					:     CLIPTokenizer					,     A					:     TransformeraDModel					,     A					:     VQDiffusionScheduler					,     A					:     LearnedClassifierFreeSamplingEmbeddings					,     ):
           super().__init__()

           self.register_modules(
               vqvae=A					,     transformer=A					,     text_encoder=A					,     tokenizer=A					,     scheduler=A					,     learned_classifier_free_sampling_embeddings=A					,     )



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Any					,     A					:     Optional[int]					,     A					:     List[Any]						):
           _UpperCAmelCase      :     int        =   len(A						) if isinstance(A					,     A						) else 1

           # get prompt text embeddings
           _UpperCAmelCase      :     str        =   self.tokenizer(
               A					,     padding="max_length"					,     max_length=self.tokenizer.model_max_length					,     return_tensors="pt"					,     )
           _UpperCAmelCase      :     int        =   text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
                _UpperCAmelCase      :     int        =   self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]						)
                logger.warning(
                    "The following part of your input was truncated because CLIP can only handle sequences up to"
                    f' {self.tokenizer.model_max_length} tokens: {removed_text}'						)
                _UpperCAmelCase      :     Union[str, Any]        =   text_input_ids[:, : self.tokenizer.model_max_length]
           _UpperCAmelCase      :     int        =   self.text_encoder(text_input_ids.to(self.device						)						)[0]

           # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
           # While CLIP does normalize the pooled output of the text transformer when combining
           # the image and text embeddings, CLIP does not directly normalize the last hidden state.
           #
           # CLIP normalizing the pooled output.
           # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
           _UpperCAmelCase      :     Tuple        =   prompt_embeds / prompt_embeds.norm(dim=-1					,     keepdim=A						)

           # duplicate text embeddings for each generation per prompt
           _UpperCAmelCase      :     int        =   prompt_embeds.repeat_interleave(A					,     dim=0						)

           if do_classifier_free_guidance:
                if self.learned_classifier_free_sampling_embeddings.learnable:
                     _UpperCAmelCase      :     Optional[int]        =   self.learned_classifier_free_sampling_embeddings.embeddings
                     _UpperCAmelCase      :     int        =   negative_prompt_embeds.unsqueeze(0						).repeat(A					,     1					,     1						)
                else:
                     _UpperCAmelCase      :     Optional[Any]        =   [""] * batch_size

                     _UpperCAmelCase      :     Optional[Any]        =   text_input_ids.shape[-1]
                     _UpperCAmelCase      :     Union[str, Any]        =   self.tokenizer(
                         A					,     padding="max_length"					,     max_length=A					,     truncation=A					,     return_tensors="pt"					,     )
                     _UpperCAmelCase      :     Tuple        =   self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
                     # See comment for normalizing text embeddings
                     _UpperCAmelCase      :     Optional[Any]        =   negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1					,     keepdim=A						)

                # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
                _UpperCAmelCase      :     int        =   negative_prompt_embeds.shape[1]
                _UpperCAmelCase      :     Dict        =   negative_prompt_embeds.repeat(1					,     A					,     1						)
                _UpperCAmelCase      :     Union[str, Any]        =   negative_prompt_embeds.view(batch_size * num_images_per_prompt					,     A					,     -1						)

                # For classifier free guidance, we need to do two forward passes.
                # Here we concatenate the unconditional and text embeddings into a single batch
                # to avoid doing two forward passes
                _UpperCAmelCase      :     Optional[Any]        =   torch.cat([negative_prompt_embeds, prompt_embeds]						)

           return prompt_embeds



      @torch.no_grad()
      def __call__(					self					:     Tuple					,     A					:     Union[str, List[str]]					,     A					:     int = 1_0_0					,     A					:     float = 5.0					,     A					:     float = 1.0					,     A					:     int = 1					,     A					:     Optional[Union[torch.Generator, List[torch.Generator]]] = None					,     A					:     Optional[torch.FloatTensor] = None					,     A					:     Optional[str] = "pil"					,     A					:     bool = True					,     A					:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,     A					:     int = 1					,     ):
           if isinstance(A					,     A						):
                _UpperCAmelCase      :     List[Any]        =   1
           elif isinstance(A					,     A						):
                _UpperCAmelCase      :     Union[str, Any]        =   len(A						)
           else:
                raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(A						)}'						)

           _UpperCAmelCase      :     Tuple        =   batch_size * num_images_per_prompt

           _UpperCAmelCase      :     Optional[Any]        =   guidance_scale > 1.0

           _UpperCAmelCase      :     Optional[int]        =   self._encode_prompt(A					,     A					,     A						)

           if (callback_steps is None) or (
               callback_steps is not None and (not isinstance(A					,     A						) or callback_steps <= 0)
           ):
                raise ValueError(
                    f'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                    f' {type(A						)}.'						)

           # get the initial completely masked latents unless the user supplied it

           _UpperCAmelCase      :     Optional[Any]        =   (batch_size, self.transformer.num_latent_pixels)
           if latents is None:
                _UpperCAmelCase      :     Union[str, Any]        =   self.transformer.num_vector_embeds - 1
                _UpperCAmelCase      :     Union[str, Any]        =   torch.full(A					,     A						).to(self.device						)
           else:
                if latents.shape != latents_shape:
                     raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'						)
                if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                     raise ValueError(
                         "Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,"
                         f' {self.transformer.num_vector_embeds - 1} (inclusive).'						)
                _UpperCAmelCase      :     Any        =   latents.to(self.device						)

           # set timesteps
           self.scheduler.set_timesteps(A					,     device=self.device						)

           _UpperCAmelCase      :     Optional[Any]        =   self.scheduler.timesteps.to(self.device						)

           _UpperCAmelCase      :     Dict        =   latents

           for i, t in enumerate(self.progress_bar(A						)						):
                # expand the sample if we are doing classifier free guidance
                _UpperCAmelCase      :     int        =   torch.cat([sample] * 2						) if do_classifier_free_guidance else sample

                # predict the un-noised image
                # model_output == `log_p_x_0`
                _UpperCAmelCase      :     Optional[int]        =   self.transformer(A					,     encoder_hidden_states=A					,     timestep=A						).sample

                if do_classifier_free_guidance:
                     _UpperCAmelCase      :     Union[str, Any]        =   model_output.chunk(2						)
                     _UpperCAmelCase      :     List[Any]        =   model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                     model_output -= torch.logsumexp(A					,     dim=1					,     keepdim=A						)

                _UpperCAmelCase      :     Dict        =   self.truncate(A					,     A						)

                # remove `log(0)`'s (`-inf`s)
                _UpperCAmelCase      :     int        =   model_output.clamp(-7_0						)

                # compute the previous noisy sample x_t -> x_t-1
                _UpperCAmelCase      :     Dict        =   self.scheduler.step(A					,     timestep=A					,     sample=A					,     generator=A						).prev_sample

                # call the callback, if provided
                if callback is not None and i % callback_steps == 0:
                     callback(A					,     A					,     A						)

           _UpperCAmelCase      :     int        =   self.vqvae.config.vq_embed_dim
           _UpperCAmelCase      :     Union[str, Any]        =   (batch_size, self.transformer.height, self.transformer.width, embedding_channels)
           _UpperCAmelCase      :     List[Any]        =   self.vqvae.quantize.get_codebook_entry(A					,     shape=A						)
           _UpperCAmelCase      :     Dict        =   self.vqvae.decode(A					,     force_not_quantize=A						).sample

           _UpperCAmelCase      :     Optional[Any]        =   (image / 2 + 0.5).clamp(0					,     1						)
           _UpperCAmelCase      :     Optional[int]        =   image.cpu().permute(0					,     2					,     3					,     1						).numpy()

           if output_type == "pil":
                _UpperCAmelCase      :     Optional[Any]        =   self.numpy_to_pil(A						)

           if not return_dict:
                return (image,)

           return ImagePipelineOutput(images=A						)



      def 						snake_case_ (					self					:     Dict					,     A					:     torch.FloatTensor					,     A					:     float						):
           _UpperCAmelCase      :     Optional[int]        =   torch.sort(A					,     1					,     descending=A						)
           _UpperCAmelCase      :     Union[str, Any]        =   torch.exp(A						)
           _UpperCAmelCase      :     Any        =   sorted_p_x_0.cumsum(dim=1						) < truncation_rate

           # Ensure that at least the largest probability is not zeroed out
           _UpperCAmelCase      :     Optional[Any]        =   torch.full_like(keep_mask[:, 0:1, :]					,     A						)
           _UpperCAmelCase      :     Optional[int]        =   torch.cat((all_true, keep_mask)					,     dim=1						)
           _UpperCAmelCase      :     Optional[int]        =   keep_mask[:, :-1, :]

           _UpperCAmelCase      :     List[Any]        =   keep_mask.gather(1					,     indices.argsort(1						)						)

           _UpperCAmelCase      :     Dict        =   log_p_x_0.clone()

           _UpperCAmelCase      :     Optional[Any]        =   -torch.inf  # -inf = log(0)

           return rv





"""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      UpperCAmelCase_  :



      def __init__(					self					:     Tuple					,     A					:     str					,     A					:     Dict=2					,     A					:     List[Any]=3_2					,     A					:     Optional[Any]=1_6					,     A					:     Tuple=3					,     A					:     Optional[Any]=True					,     A					:     List[Any]=True					,     A					:     Optional[int]=3_2					,     A					:     Optional[int]=4					,     A					:     Tuple=[0, 1, 2, 3]					,     A					:     Optional[int]=4					,     A					:     Tuple=3_7					,     A					:     List[Any]="gelu"					,     A					:     List[Any]=0.1					,     A					:     List[str]=0.1					,     A					:     Union[str, Any]=0.02					,     A					:     Optional[int]=3					,     A					:     Optional[Any]=[1, 3_8_4, 2_4, 2_4]					,     A					:     Union[str, Any]=True					,     A					:     Any=None					,     ):
           _UpperCAmelCase      :     str        =   parent
           _UpperCAmelCase      :     int        =   batch_size
           _UpperCAmelCase      :     List[str]        =   image_size
           _UpperCAmelCase      :     Tuple        =   patch_size
           _UpperCAmelCase      :     Any        =   num_channels
           _UpperCAmelCase      :     List[str]        =   is_training
           _UpperCAmelCase      :     Optional[Any]        =   use_labels
           _UpperCAmelCase      :     str        =   hidden_size
           _UpperCAmelCase      :     Dict        =   num_hidden_layers
           _UpperCAmelCase      :     List[Any]        =   backbone_out_indices
           _UpperCAmelCase      :     Optional[Any]        =   num_attention_heads
           _UpperCAmelCase      :     Optional[int]        =   intermediate_size
           _UpperCAmelCase      :     str        =   hidden_act
           _UpperCAmelCase      :     int        =   hidden_dropout_prob
           _UpperCAmelCase      :     Any        =   attention_probs_dropout_prob
           _UpperCAmelCase      :     Optional[Any]        =   initializer_range
           _UpperCAmelCase      :     Optional[Any]        =   num_labels
           _UpperCAmelCase      :     Tuple        =   backbone_featmap_shape
           _UpperCAmelCase      :     Optional[Any]        =   scope
           _UpperCAmelCase      :     Union[str, Any]        =   is_hybrid
           # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
           _UpperCAmelCase      :     Optional[int]        =   (image_size // patch_size) ** 2
           _UpperCAmelCase      :     int        =   num_patches + 1



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Tuple        =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

           _UpperCAmelCase      :     str        =   self.get_config()

           return config, pixel_values, labels



      def 						snake_case_ (					self					:     str						):
           _UpperCAmelCase      :     Tuple        =   {
               "global_padding": "same",
               "layer_type": "bottleneck",
               "depths": [3, 4, 9],
               "out_features": ["stage1", "stage2", "stage3"],
               "embedding_dynamic_padding": True,
               "hidden_sizes": [9_6, 1_9_2, 3_8_4, 7_6_8],
               "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 						snake_case_ (					self					:     Any					,     A					:     Optional[Any]					,     A					:     str					,     A					:     Dict						):
           _UpperCAmelCase      :     List[str]        =   DPTModel(config=A						)
           model.to(A						)
           model.eval()
           _UpperCAmelCase      :     List[str]        =   model(A						)
           self.parent.assertEqual(result.last_hidden_state.shape					,     (self.batch_size, self.seq_length, self.hidden_size)						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     str					,     A					:     Any					,     A					:     List[Any]						):
           _UpperCAmelCase      :     str        =   self.num_labels
           _UpperCAmelCase      :     Any        =   DPTForDepthEstimation(A						)
           model.to(A						)
           model.eval()
           _UpperCAmelCase      :     Optional[int]        =   model(A						)
           self.parent.assertEqual(result.predicted_depth.shape					,     (self.batch_size, self.image_size, self.image_size)						)



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



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.prepare_config_and_inputs()
           _UpperCAmelCase    ,	_UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   config_and_inputs
           _UpperCAmelCase      :     int        =   {"pixel_values": pixel_values}
           return config, inputs_dict







@require_torch
class      UpperCAmelCase_  (    _UpperCamelCase	,      _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       List[str]								=			(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
      __SCREAMING_SNAKE_CASE						:       Any								=			(
          {
              'depth-estimation': DPTForDepthEstimation,
              'feature-extraction': DPTModel,
              'image-segmentation': DPTForSemanticSegmentation,
          }
          if is_torch_available()
          else {}
      )

      __SCREAMING_SNAKE_CASE						:       Dict								=			False
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			False



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     List[str]        =   DPTModelTester(self						)
           _UpperCAmelCase      :     Union[str, Any]        =   ConfigTester(self					,     config_class=A					,     has_text_modality=A					,     hidden_size=3_7						)



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



      @unittest.skip(reason="DPT does not use inputs_embeds"						)
      def 						snake_case_ (					self					:     Union[str, Any]						):
           pass



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[Any]        =   self.model_tester.prepare_config_and_inputs_for_common()

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



      def 						snake_case_ (					self					:     Optional[Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs_for_common()

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

                _UpperCAmelCase      :     Any        =   ["pixel_values"]
                self.assertListEqual(arg_names[:1]					,     A						)



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Dict        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*A						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Optional[Any]        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_depth_estimation(*A						)



      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Tuple        =   self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_semantic_segmentation(*A						)



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

                _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs_for_common()
                _UpperCAmelCase      :     Optional[int]        =   True

                if model_class in get_values(A						):
                     continue

                _UpperCAmelCase      :     int        =   model_class(A						)
                model.to(A						)
                model.train()
                _UpperCAmelCase      :     Optional[Any]        =   self._prepare_for_class(A					,     A					,     return_labels=A						)
                _UpperCAmelCase      :     Optional[Any]        =   model(**A						).loss
                loss.backward()



      def 						snake_case_ (					self					:     Dict						):
           for model_class in self.all_model_classes:
                if model_class.__name__ == "DPTForDepthEstimation":
                     continue

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

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



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   self.model_tester.prepare_config_and_inputs_for_common()

           _UpperCAmelCase      :     int        =   _config_zero_init(A						)
           for model_class in self.all_model_classes:
                _UpperCAmelCase      :     List[Any]        =   model_class(config=A						)
                # Skip the check for the backbone
                _UpperCAmelCase      :     Dict        =   []
                for name, module in model.named_modules():
                     if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                          _UpperCAmelCase      :     List[str]        =   [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 						snake_case_ (					self					:     int						):
           pass



      @slow
      def 						snake_case_ (					self					:     Dict						):
           for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
                _UpperCAmelCase      :     Any        =   DPTModel.from_pretrained(A						)
                self.assertIsNotNone(A						)



      def 						snake_case_ (					self					:     Tuple						):
           # We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
           _UpperCAmelCase    ,	_UpperCAmelCase      :     List[str]        =   self.model_tester.prepare_config_and_inputs_for_common()
           _UpperCAmelCase      :     Optional[Any]        =   "add"
           with self.assertRaises(A						):
                _UpperCAmelCase      :     List[Any]        =   DPTForDepthEstimation(A						)







def 						__snake_case						(				)  ->       Optional[int]:





     '''simple docstring'''

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






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



      def 						snake_case_ (					self					:     List[str]						):
           _UpperCAmelCase      :     Optional[int]        =   DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas"						)
           _UpperCAmelCase      :     Any        =   DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas"						).to(A						)

           _UpperCAmelCase      :     str        =   prepare_img()
           _UpperCAmelCase      :     Tuple        =   image_processor(images=A					,     return_tensors="pt"						).to(A						)

           # forward pass
           with torch.no_grad():
                _UpperCAmelCase      :     int        =   model(**A						)
                _UpperCAmelCase      :     List[str]        =   outputs.predicted_depth

           # verify the predicted depth
           _UpperCAmelCase      :     int        =   torch.Size((1, 3_8_4, 3_8_4)						)
           self.assertEqual(predicted_depth.shape					,     A						)

           _UpperCAmelCase      :     int        =   torch.tensor(
               [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]						).to(A						)

           self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_0_0					,     A					,     atol=1e-4						)						)
