



'''simple docstring'''





import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


UpperCamelCase__    :     Union[str, Any]			  =   logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCamelCase__    :     str			  =   '\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")\n\n        >>> repo = "openai/shap-e"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = "a shark"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], "shark_3d.gif")\n        ```\n'


@dataclass
class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 lowerCamelCase									=     42


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    Tuple:
    super().__init__()

    self.register_modules(
        prior=_lowerCamelCase					,    text_encoder=_lowerCamelCase					,    tokenizer=_lowerCamelCase					,    scheduler=_lowerCamelCase					,    renderer=_lowerCamelCase					,    )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Optional[Any]:
    if latents is None:
       A_  :		List[Any]						=					randn_tensor(_lowerCamelCase					,    generator=_lowerCamelCase					,    device=_lowerCamelCase					,    dtype=_lowerCamelCase  )
    else:
       if latents.shape != shape:
          raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}"  )
       A_  :		Any						=					latents.to(_lowerCamelCase  )

    A_  :		Optional[int]						=					latents * scheduler.init_noise_sigma
    return latents







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase=0  )    ->    Tuple:
    if is_accelerate_available():
       from accelerate import cpu_offload
    else:
       raise ImportError("""Please install accelerate via `pip install accelerate`"""  )

    A_  :		List[Any]						=					torch.device(F"cuda:{gpu_id}"  )

    A_  :		Dict						=					[self.text_encoder, self.prior]
    for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
          cpu_offload(_lowerCamelCase					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase=0  )    ->    Tuple:
    if is_accelerate_available() and is_accelerate_version(""">="""					,    """0.17.0.dev0"""  ):
       from accelerate import cpu_offload_with_hook
    else:
       raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""  )

    A_  :		Dict						=					torch.device(F"cuda:{gpu_id}"  )

    if self.device.type != "cpu":
       self.to("""cpu"""					,    silence_dtype_warnings=_lowerCamelCase  )
       torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

    A_  :		Any						=					None
    for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
       A_						,  A_  :		Any						=					cpu_offload_with_hook(_lowerCamelCase					,    _lowerCamelCase					,    prev_module_hook=_lowerCamelCase  )

    if self.safety_checker is not None:
       A_						,  A_  :		Tuple						=					cpu_offload_with_hook(self.safety_checker					,    _lowerCamelCase					,    prev_module_hook=_lowerCamelCase  )

    # We'll offload the last model manually.
    A_  :		Any						=					hook







 @property
 def       UpperCAmelCase_       (      self  )    ->    Any:
    if self.device != torch.device("""meta"""  ) or not hasattr(self.text_encoder					,    """_hf_hook"""  ):
       return self.device
    for module in self.text_encoder.modules():
       if (
           hasattr(_lowerCamelCase					,    """_hf_hook"""  )
           and hasattr(module._hf_hook					,    """execution_device"""  )
           and module._hf_hook.execution_device is not None
       ):
          return torch.device(module._hf_hook.execution_device  )
    return self.device







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    )    ->    Any:
    len(_lowerCamelCase  ) if isinstance(_lowerCamelCase					,    _lowerCamelCase  ) else 1

    # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
    A_  :		Optional[Any]						=					0
    # get prompt text embeddings
    A_  :		Optional[int]						=					self.tokenizer(
        _lowerCamelCase					,    padding="""max_length"""					,    max_length=self.tokenizer.model_max_length					,    truncation=_lowerCamelCase					,    return_tensors="""pt"""					,    )
    A_  :		Any						=					text_inputs.input_ids
    A_  :		int						=					self.tokenizer(_lowerCamelCase					,    padding="""longest"""					,    return_tensors="""pt"""  ).input_ids

    if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(_lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		str						=					self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]  )
       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}"  )

    A_  :		int						=					self.text_encoder(text_input_ids.to(_lowerCamelCase  )  )
    A_  :		Any						=					text_encoder_output.text_embeds

    A_  :		str						=					prompt_embeds.repeat_interleave(_lowerCamelCase					,    dim=0  )
    # in Shap-E it normalize the prompt_embeds and then later rescale it
    A_  :		Any						=					prompt_embeds / torch.linalg.norm(_lowerCamelCase					,    dim=-1					,    keepdim=_lowerCamelCase  )

    if do_classifier_free_guidance:
       A_  :		Union[str, Any]						=					torch.zeros_like(_lowerCamelCase  )

       # 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
       A_  :		Union[str, Any]						=					torch.cat([negative_prompt_embeds, prompt_embeds]  )

    # Rescale the features to have unit variance
    A_  :		Optional[int]						=					math.sqrt(prompt_embeds.shape[1]  ) * prompt_embeds

    return prompt_embeds







 @torch.no_grad()
 @replace_example_docstring(_lowerCamelCase  )
 def __call__(      self					,    _lowerCamelCase					,    _lowerCamelCase = 1					,    _lowerCamelCase = 25					,    _lowerCamelCase = None					,    _lowerCamelCase = None					,    _lowerCamelCase = 4.0					,    _lowerCamelCase = 64					,    _lowerCamelCase = "pil"					,    _lowerCamelCase = True					,    )    ->    Dict:

    if isinstance(_lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		str						=					1
    elif isinstance(_lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		Optional[Any]						=					len(_lowerCamelCase  )
    else:
       raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(_lowerCamelCase  )}"  )

    A_  :		int						=					self._execution_device

    A_  :		List[Any]						=					batch_size * num_images_per_prompt

    A_  :		int						=					guidance_scale > 1.0
    A_  :		int						=					self._encode_prompt(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )

    # prior

    self.scheduler.set_timesteps(_lowerCamelCase					,    device=_lowerCamelCase  )
    A_  :		int						=					self.scheduler.timesteps

    A_  :		Optional[Any]						=					self.prior.config.num_embeddings
    A_  :		int						=					self.prior.config.embedding_dim

    A_  :		Dict						=					self.prepare_latents(
        (batch_size, num_embeddings * embedding_dim)					,    prompt_embeds.dtype					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase					,    self.scheduler					,    )

    # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
    A_  :		Optional[int]						=					latents.reshape(latents.shape[0]					,    _lowerCamelCase					,    _lowerCamelCase  )

    for i, t in enumerate(self.progress_bar(_lowerCamelCase  )  ):
       # expand the latents if we are doing classifier free guidance
       A_  :		Dict						=					torch.cat([latents] * 2  ) if do_classifier_free_guidance else latents
       A_  :		Any						=					self.scheduler.scale_model_input(_lowerCamelCase					,    _lowerCamelCase  )

       A_  :		List[Any]						=					self.prior(
           _lowerCamelCase					,    timestep=_lowerCamelCase					,    proj_embedding=_lowerCamelCase					,    ).predicted_image_embedding

       # remove the variance
       A_						,  A_  :		List[Any]						=					noise_pred.split(
           scaled_model_input.shape[2]					,    dim=2  )  # batch_size, num_embeddings, embedding_dim

       if do_classifier_free_guidance is not None:
          A_						,  A_  :		List[str]						=					noise_pred.chunk(2  )
          A_  :		Any						=					noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

       A_  :		int						=					self.scheduler.step(
           _lowerCamelCase					,    timestep=_lowerCamelCase					,    sample=_lowerCamelCase					,    ).prev_sample

    if output_type == "latent":
       return ShapEPipelineOutput(images=_lowerCamelCase  )

    A_  :		Dict						=					[]
    for i, latent in enumerate(_lowerCamelCase  ):
       A_  :		Union[str, Any]						=					self.renderer.decode(
           latent[None, :]					,    _lowerCamelCase					,    size=_lowerCamelCase					,    ray_batch_size=4096					,    n_coarse_samples=64					,    n_fine_samples=128					,    )
       images.append(_lowerCamelCase  )

    A_  :		List[Any]						=					torch.stack(_lowerCamelCase  )

    if output_type not in ["np", "pil"]:
       raise ValueError(F"Only the output types `pil` and `np` are supported not output_type={output_type}"  )

    A_  :		Union[str, Any]						=					images.cpu().numpy()

    if output_type == "pil":
       A_  :		List[Any]						=					[self.numpy_to_pil(_lowerCamelCase  ) for image in images]

    # Offload last model to CPU
    if hasattr(self					,    """final_offload_hook"""  ) and self.final_offload_hook is not None:
       self.final_offload_hook.offload()

    if not return_dict:
       return (images,)

    return ShapEPipelineOutput(images=_lowerCamelCase  )





'''simple docstring'''





import inspect
import unittest

from transformers 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import MODEL_MAPPING, GLPNConfig, GLPNForDepthEstimation, GLPNModel
  from transformers.models.glpn.modeling_glpn import GLPN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
  from PIL import Image

  from transformers import GLPNImageProcessor


class 							_lowerCAmelCase	(  __A							):


 """simple docstring"""

 def       UpperCAmelCase_       (      self  )    ->    int:
    A_  :		Tuple						=					self.config_class(**self.inputs_dict  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """hidden_sizes"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_attention_heads"""  )  )
    self.parent.assertTrue(hasattr(_lowerCamelCase					,    """num_encoder_blocks"""  )  )


class 							_lowerCAmelCase	:


 """simple docstring"""

 def __init__(      self					,    _lowerCamelCase					,    _lowerCamelCase=13					,    _lowerCamelCase=64					,    _lowerCamelCase=3					,    _lowerCamelCase=4					,    _lowerCamelCase=[2, 2, 2, 2]					,    _lowerCamelCase=[8, 4, 2, 1]					,    _lowerCamelCase=[16, 32, 64, 128]					,    _lowerCamelCase=[1, 4, 8, 16]					,    _lowerCamelCase=[1, 2, 4, 8]					,    _lowerCamelCase=True					,    _lowerCamelCase=True					,    _lowerCamelCase="gelu"					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.1					,    _lowerCamelCase=0.02					,    _lowerCamelCase=16					,    _lowerCamelCase=3					,    _lowerCamelCase=None					,    )    ->    Optional[int]:
    A_  :		int						=					parent
    A_  :		int						=					batch_size
    A_  :		Optional[int]						=					image_size
    A_  :		str						=					num_channels
    A_  :		Optional[Any]						=					num_encoder_blocks
    A_  :		List[str]						=					sr_ratios
    A_  :		int						=					depths
    A_  :		str						=					hidden_sizes
    A_  :		Optional[int]						=					downsampling_rates
    A_  :		Any						=					num_attention_heads
    A_  :		List[str]						=					is_training
    A_  :		Optional[Any]						=					use_labels
    A_  :		List[str]						=					hidden_act
    A_  :		Tuple						=					hidden_dropout_prob
    A_  :		Optional[int]						=					attention_probs_dropout_prob
    A_  :		Optional[int]						=					initializer_range
    A_  :		str						=					decoder_hidden_size
    A_  :		Optional[Any]						=					num_labels
    A_  :		List[Any]						=					scope







 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    A_  :		List[Any]						=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

    A_  :		Dict						=					None
    if self.use_labels:
       A_  :		Optional[Any]						=					ids_tensor([self.batch_size, self.image_size, self.image_size]					,    self.num_labels  )

    A_  :		Any						=					self.get_config()
    return config, pixel_values, labels







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    return GLPNConfig(
        image_size=self.image_size					,    num_channels=self.num_channels					,    num_encoder_blocks=self.num_encoder_blocks					,    depths=self.depths					,    hidden_sizes=self.hidden_sizes					,    num_attention_heads=self.num_attention_heads					,    hidden_act=self.hidden_act					,    hidden_dropout_prob=self.hidden_dropout_prob					,    attention_probs_dropout_prob=self.attention_probs_dropout_prob					,    initializer_range=self.initializer_range					,    decoder_hidden_size=self.decoder_hidden_size					,    )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    str:
    A_  :		Tuple						=					GLPNModel(config=_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Optional[Any]						=					model(_lowerCamelCase  )
    A_  :		Optional[int]						=					self.image_size // (self.downsampling_rates[-1] * 2)
    self.parent.assertEqual(
        result.last_hidden_state.shape					,    (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)  )







 def       UpperCAmelCase_       (      self					,    _lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )    ->    Optional[int]:
    A_  :		Tuple						=					self.num_labels
    A_  :		Any						=					GLPNForDepthEstimation(_lowerCamelCase  )
    model.to(_lowerCamelCase  )
    model.eval()
    A_  :		Dict						=					model(_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )
    A_  :		Optional[int]						=					model(_lowerCamelCase					,    labels=_lowerCamelCase  )
    self.parent.assertEqual(result.predicted_depth.shape					,    (self.batch_size, self.image_size, self.image_size)  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_  :		Optional[Any]						=					self.prepare_config_and_inputs()
    A_						,  A_						,  A_  :		Tuple						=					config_and_inputs
    A_  :		Dict						=					{"""pixel_values""": pixel_values}
    return config, inputs_dict


@require_torch
class 							_lowerCAmelCase	(  __A,							__A,							unittest.TestCase							):


 """simple docstring"""

 lowerCamelCase									=     (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
 lowerCamelCase									=     (
     {'''depth-estimation''': GLPNForDepthEstimation, '''feature-extraction''': GLPNModel} if is_torch_available() else {}
 )

 lowerCamelCase									=     False
 lowerCamelCase									=     False
 lowerCamelCase									=     False







 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Optional[Any]						=					GLPNModelTester(self  )
    A_  :		Dict						=					GLPNConfigTester(self					,    config_class=_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[int]:
    self.config_tester.run_common_tests()







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		int						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_model(*_lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    List[str]:
    A_  :		Dict						=					self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_for_depth_estimation(*_lowerCamelCase  )







 @unittest.skip("""GLPN does not use inputs_embeds"""  )
 def       UpperCAmelCase_       (      self  )    ->    List[Any]:
    pass







 @unittest.skip("""GLPN does not have get_input_embeddings method and get_output_embeddings methods"""  )
 def       UpperCAmelCase_       (      self  )    ->    Any:
    pass







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		Union[str, Any]						=					self.model_tester.prepare_config_and_inputs_for_common()

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

       A_  :		Dict						=					["""pixel_values"""]
       self.assertListEqual(arg_names[:1]					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    A_						,  A_  :		List[str]						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		Optional[Any]						=					True

    for model_class in self.all_model_classes:
       A_  :		int						=					True
       A_  :		str						=					False
       A_  :		Any						=					True
       A_  :		Dict						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		List[str]						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		Union[str, Any]						=					outputs.attentions

       A_  :		List[str]						=					sum(self.model_tester.depths  )
       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       A_  :		Dict						=					True
       A_  :		List[str]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Any						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )
       A_  :		str						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		str						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )

       # verify the last attentions (last block, last layer)
       A_  :		Optional[Any]						=					(self.model_tester.image_size // 32) ** 2
       A_  :		Optional[Any]						=					(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
       self.assertListEqual(
           list(attentions[-1].shape[-3:]  )					,    [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]					,    )
       A_  :		str						=					len(_lowerCamelCase  )

       # Check attention is always last and order is fine
       A_  :		Union[str, Any]						=					True
       A_  :		str						=					True
       A_  :		Tuple						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()
       with torch.no_grad():
          A_  :		Dict						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       self.assertEqual(out_len + 1					,    len(_lowerCamelCase  )  )

       A_  :		Any						=					outputs.attentions

       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )
       # verify the first attentions (first block, first layer)
       A_  :		List[Any]						=					(self.model_tester.image_size // 4) ** 2
       A_  :		Optional[int]						=					(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
       self.assertListEqual(
           list(self_attentions[0].shape[-3:]  )					,    [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]					,    )







 def       UpperCAmelCase_       (      self  )    ->    Dict:
    def check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  ):
       A_  :		List[Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.eval()

       with torch.no_grad():
          A_  :		Tuple						=					model(**self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase  )  )

       A_  :		List[str]						=					outputs.hidden_states

       A_  :		Dict						=					self.model_tester.num_encoder_blocks
       self.assertEqual(len(_lowerCamelCase  )					,    _lowerCamelCase  )

       # verify the first hidden states (first block)
       self.assertListEqual(
           list(hidden_states[0].shape[-3:]  )					,    [
               self.model_tester.hidden_sizes[0],
               self.model_tester.image_size // 4,
               self.model_tester.image_size // 4,
           ]					,    )

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

    for model_class in self.all_model_classes:
       A_  :		Any						=					True
       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )

       # check that output_hidden_states also work using config
       del inputs_dict["output_hidden_states"]
       A_  :		Optional[Any]						=					True

       check_hidden_states_output(_lowerCamelCase					,    _lowerCamelCase					,    _lowerCamelCase  )







 def       UpperCAmelCase_       (      self  )    ->    Optional[Any]:
    if not self.model_tester.is_training:
       return

    A_						,  A_  :		Any						=					self.model_tester.prepare_config_and_inputs_for_common()
    A_  :		Union[str, Any]						=					True

    for model_class in self.all_model_classes:
       if model_class in get_values(_lowerCamelCase  ):
          continue
       # TODO: remove the following 3 lines once we have a MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       # this can then be incorporated into _prepare_for_class in test_modeling_common.py
       if model_class.__name__ == "GLPNForDepthEstimation":
          A_						,  A_						,  A_						,  A_  :		Any						=					inputs_dict["""pixel_values"""].shape
          A_  :		List[Any]						=					torch.zeros(
              [self.model_tester.batch_size, height, width]					,    device=_lowerCamelCase  ).long()
       A_  :		Union[str, Any]						=					model_class(_lowerCamelCase  )
       model.to(_lowerCamelCase  )
       model.train()
       A_  :		List[str]						=					self._prepare_for_class(_lowerCamelCase					,    _lowerCamelCase					,    return_labels=_lowerCamelCase  )
       A_  :		Dict						=					model(**_lowerCamelCase  ).loss
       loss.backward()







 @slow
 def       UpperCAmelCase_       (      self  )    ->    Dict:
    for model_name in GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A_  :		Union[str, Any]						=					GLPNModel.from_pretrained(_lowerCamelCase  )
       self.assertIsNotNone(_lowerCamelCase  )



def     UpperCAmelCase     (    )   ->       int:



   """simple docstring"""






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







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


 """simple docstring"""

 @slow
 def       UpperCAmelCase_       (      self  )    ->    Tuple:
    A_  :		Tuple						=					GLPNImageProcessor.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  )
    A_  :		List[Any]						=					GLPNForDepthEstimation.from_pretrained(GLPN_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(_lowerCamelCase  )

    A_  :		str						=					prepare_img()
    A_  :		int						=					image_processor(images=_lowerCamelCase					,    return_tensors="""pt"""  ).to(_lowerCamelCase  )

    # forward pass
    with torch.no_grad():
       A_  :		Optional[int]						=					model(**_lowerCamelCase  )

    # verify the predicted depth
    A_  :		List[Any]						=					torch.Size([1, 480, 640]  )
    self.assertEqual(outputs.predicted_depth.shape					,    _lowerCamelCase  )

    A_  :		Union[str, Any]						=					torch.tensor(
        [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]]  ).to(_lowerCamelCase  )

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