
import inspect
import unittest

from huggingface_hub import hf_hub_download

from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available

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 transformers import UperNetForSemanticSegmentation
			from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import AutoImageProcessor




class     _SCREAMING_SNAKE_CASE     :



	def __init__(    self						,  lowercase						,  lowercase=13						,  lowercase=32						,  lowercase=3						,  lowercase=4						,  lowercase=[10, 20, 30, 40]						,  lowercase=[2, 2, 3, 2]						,  lowercase=True						,  lowercase=True						,  lowercase=37						,  lowercase="gelu"						,  lowercase=10						,  lowercase=0.0_2						,  lowercase=["stage2", "stage3", "stage4"]						,  lowercase=3						,  lowercase=None						,  )				->       str:
					lowerCamelCase_						=			parent
					lowerCamelCase_						=			batch_size
					lowerCamelCase_						=			image_size
					lowerCamelCase_						=			num_channels
					lowerCamelCase_						=			num_stages
					lowerCamelCase_						=			hidden_sizes
					lowerCamelCase_						=			depths
					lowerCamelCase_						=			is_training
					lowerCamelCase_						=			use_labels
					lowerCamelCase_						=			intermediate_size
					lowerCamelCase_						=			hidden_act
					lowerCamelCase_						=			type_sequence_label_size
					lowerCamelCase_						=			initializer_range
					lowerCamelCase_						=			out_features
					lowerCamelCase_						=			num_labels
					lowerCamelCase_						=			scope
					lowerCamelCase_						=			num_stages



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

					lowerCamelCase_						=			None
					if self.use_labels:
									lowerCamelCase_						=			ids_tensor([self.batch_size]						,  self.type_sequence_label_size )

					lowerCamelCase_						=			self.get_config()

					return config, pixel_values, labels



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					return ConvNextConfig(
					    num_channels=self.num_channels						,  num_stages=self.num_stages						,  hidden_sizes=self.hidden_sizes						,  depths=self.depths						,  is_training=self.is_training						,  intermediate_size=self.intermediate_size						,  hidden_act=self.hidden_act						,  out_features=self.out_features						,  )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return UperNetConfig(
					    backbone_config=self.get_backbone_config()						,  hidden_size=512						,  pool_scales=[1, 2, 3, 6]						,  use_auxiliary_head=lowercase						,  auxiliary_loss_weight=0.4						,  auxiliary_in_channels=40						,  auxiliary_channels=256						,  auxiliary_num_convs=1						,  auxiliary_concat_input=lowercase						,  loss_ignore_index=255						,  num_labels=self.num_labels						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase )				->       Optional[int]:
					lowerCamelCase_						=			UperNetForSemanticSegmentation(config=lowercase )
					model.to(lowercase )
					model.eval()
					lowerCamelCase_						=			model(lowercase )
					self.parent.assertEqual(
					    result.logits.shape						,  (self.batch_size, self.num_labels, self.image_size, self.image_size) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_						=			self.prepare_config_and_inputs()
					(
					    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    (
					    lowerCamelCase_
					)	,    
					)						=			config_and_inputs
					lowerCamelCase_						=			{"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class     _SCREAMING_SNAKE_CASE     (      snake_case_			,       snake_case_			,       unittest.TestCase						):
	lowerCAmelCase__         = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
	lowerCAmelCase__         = {'image-segmentation': UperNetForSemanticSegmentation} if is_torch_available() else {}
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False
	lowerCAmelCase__         = False



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					lowerCamelCase_						=			UperNetModelTester(self )
					lowerCamelCase_						=			ConfigTester(self						,  config_class=lowercase						,  has_text_modality=lowercase						,  hidden_size=37 )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					self.create_and_test_config_common_properties()
					self.config_tester.create_and_test_config_to_json_string()
					self.config_tester.create_and_test_config_to_json_file()
					self.config_tester.create_and_test_config_from_and_save_pretrained()
					self.config_tester.create_and_test_config_with_num_labels()
					self.config_tester.check_config_can_be_init_without_params()
					self.config_tester.check_config_arguments_init()



	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					return



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

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

									lowerCamelCase_						=			["pixel_values"]
									self.assertListEqual(arg_names[:1]						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[Any]:
					lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*lowercase )



	@unittest.skip(reason="UperNet does not use inputs_embeds" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					pass



	@unittest.skip(reason="UperNet does not support input and output embeddings" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Union[str, Any]:
					pass



	@unittest.skip(reason="UperNet does not have a base model" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					pass



	@unittest.skip(reason="UperNet does not have a base model" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					pass



	@require_torch_multi_gpu
	@unittest.skip(reason="UperNet has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Dict:
					pass



	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					pass



	def     SCREAMING_SNAKE_CASE_(    self )				->       Any:
					def check_hidden_states_output(lowercase						,  lowercase						,  lowercase ):
									lowerCamelCase_						=			model_class(lowercase )
									model.to(lowercase )
									model.eval()

									with torch.no_grad():
													lowerCamelCase_						=			model(**self._prepare_for_class(lowercase						,  lowercase ) )

									lowerCamelCase_						=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

									lowerCamelCase_						=			self.model_tester.num_stages
									self.assertEqual(len(lowercase )						,  expected_num_stages + 1 )

									# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
									self.assertListEqual(
									    list(hidden_states[0].shape[-2:] )						,  [self.model_tester.image_size // 4, self.model_tester.image_size // 4]						,  )

					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
									lowerCamelCase_						=			True
									check_hidden_states_output(lowercase						,  lowercase						,  lowercase )

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									lowerCamelCase_						=			True

									check_hidden_states_output(lowercase						,  lowercase						,  lowercase )



	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					lowerCamelCase_	,    lowerCamelCase_						=			self.model_tester.prepare_config_and_inputs_for_common()

					lowerCamelCase_						=			_config_zero_init(lowercase )
					lowerCamelCase_						=			_config_zero_init(configs_no_init.backbone_config )
					for model_class in self.all_model_classes:
									lowerCamelCase_						=			model_class(config=lowercase )
									for name, param in model.named_parameters():
													if param.requires_grad:
																	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(reason="UperNet does not have tied weights" )
	def     SCREAMING_SNAKE_CASE_(    self )				->       Optional[int]:
					pass



	@slow
	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCamelCase_						=			UperNetForSemanticSegmentation.from_pretrained(lowercase )
									self.assertIsNotNone(lowercase )





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			hf_hub_download(
				    repo_id="hf-internal-testing/fixtures_ade20k"	, repo_type="dataset"	, filename="ADE_val_00000001.jpg"    )
				lowerCamelCase_						=			Image.open(lowerCamelCase__    ).convert("RGB"    )
				return image





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



	def     SCREAMING_SNAKE_CASE_(    self )				->       str:
					lowerCamelCase_						=			AutoImageProcessor.from_pretrained("openmmlab/upernet-swin-tiny" )
					lowerCamelCase_						=			UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-swin-tiny" ).to(lowercase )

					lowerCamelCase_						=			prepare_img()
					lowerCamelCase_						=			processor(images=lowercase						,  return_tensors="pt" ).to(lowercase )

					with torch.no_grad():
									lowerCamelCase_						=			model(**lowercase )

					lowerCamelCase_						=			torch.Size((1, model.config.num_labels, 512, 512) )
					self.assertEqual(outputs.logits.shape						,  lowercase )

					lowerCamelCase_						=			torch.tensor(
					    [[-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.5_9_5_8, -7.5_9_5_8, -7.4_3_0_2], [-7.4_7_9_7, -7.4_7_9_7, -7.3_0_6_8]] ).to(lowercase )
					self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,  lowercase						,  atol=1e-4 ) )



	def     SCREAMING_SNAKE_CASE_(    self )				->       Tuple:
					lowerCamelCase_						=			AutoImageProcessor.from_pretrained("openmmlab/upernet-convnext-tiny" )
					lowerCamelCase_						=			UperNetForSemanticSegmentation.from_pretrained("openmmlab/upernet-convnext-tiny" ).to(lowercase )

					lowerCamelCase_						=			prepare_img()
					lowerCamelCase_						=			processor(images=lowercase						,  return_tensors="pt" ).to(lowercase )

					with torch.no_grad():
									lowerCamelCase_						=			model(**lowercase )

					lowerCamelCase_						=			torch.Size((1, model.config.num_labels, 512, 512) )
					self.assertEqual(outputs.logits.shape						,  lowercase )

					lowerCamelCase_						=			torch.tensor(
					    [[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] ).to(lowercase )
					self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3]						,  lowercase						,  atol=1e-4 ) )




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

import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel

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


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

__A				='''
    Examples:
        ```py
        >>> from PIL import Image
        >>> import torch
        >>> from diffusers import DiffusionPipeline
        >>> from diffusers.utils import export_to_gif, load_image

        >>> device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

        >>> repo = "openai/shap-e-img2img"
        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)
        >>> pipe = pipe.to(device)

        >>> guidance_scale = 3.0
        >>> image_url = "https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png"
        >>> image = load_image(image_url).convert("RGB")

        >>> images = pipe(
        ...     image,
        ...     guidance_scale=guidance_scale,
        ...     num_inference_steps=64,
        ...     frame_size=256,
        ... ).images

        >>> gif_path = export_to_gif(images[0], "corgi_3d.gif")
        ```
'''




@dataclass
class     _SCREAMING_SNAKE_CASE     (      snake_case_						):
	lowerCAmelCase__         = 42




class     _SCREAMING_SNAKE_CASE     (      snake_case_						):



	def __init__(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					super().__init__()

					self.register_modules(
					    prior=lowercase						,  image_encoder=lowercase						,  image_processor=lowercase						,  scheduler=lowercase						,  renderer=lowercase						,  )



	def     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  lowercase )				->       int:
					if latents is None:
									lowerCamelCase_						=			randn_tensor(lowercase						,  generator=lowercase						,  device=lowercase						,  dtype=lowercase )
					else:
									if latents.shape != shape:
													raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}' )
									lowerCamelCase_						=			latents.to(lowercase )

					lowerCamelCase_						=			latents * scheduler.init_noise_sigma
					return latents



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

					lowerCamelCase_						=			torch.device(f'cuda:{gpu_id}' )

					lowerCamelCase_						=			[self.image_encoder, self.prior]
					for cpu_offloaded_model in models:
									if cpu_offloaded_model is not None:
													cpu_offload(lowercase						,  lowercase )



	@property
	def     SCREAMING_SNAKE_CASE_(    self )				->       List[str]:
					if self.device != torch.device("meta" ) or not hasattr(self.image_encoder						,  "_hf_hook" ):
									return self.device
					for module in self.image_encoder.modules():
									if (
									    hasattr(lowercase						,  "_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     SCREAMING_SNAKE_CASE_(    self						,  lowercase						,  lowercase						,  lowercase						,  lowercase						,  )				->       List[str]:
					if isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  torch.Tensor ):
									lowerCamelCase_						=			torch.cat(lowercase						,  axis=0 ) if image[0].ndim == 4 else torch.stack(lowercase						,  axis=0 )

					if not isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			self.image_processor(lowercase						,  return_tensors="pt" ).pixel_values[0].unsqueeze(0 )

					lowerCamelCase_						=			image.to(dtype=self.image_encoder.dtype						,  device=lowercase )

					lowerCamelCase_						=			self.image_encoder(lowercase )["last_hidden_state"]
					lowerCamelCase_						=			image_embeds[:, 1:, :].contiguous()  # batch_size, dim, 256

					lowerCamelCase_						=			image_embeds.repeat_interleave(lowercase						,  dim=0 )

					if do_classifier_free_guidance:
									lowerCamelCase_						=			torch.zeros_like(lowercase )

									# 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
									lowerCamelCase_						=			torch.cat([negative_image_embeds, image_embeds] )

					return image_embeds



	@torch.no_grad()
	@replace_example_docstring(lowercase )
	def __call__(    self						,  lowercase						,  lowercase = 1						,  lowercase = 25						,  lowercase = None						,  lowercase = None						,  lowercase = 4.0						,  lowercase = 64						,  lowercase = "pil"						,  lowercase = True						,  )				->       Union[str, Any]:

					if isinstance(lowercase						,  PIL.Image.Image ):
									lowerCamelCase_						=			1
					elif isinstance(lowercase						,  torch.Tensor ):
									lowerCamelCase_						=			image.shape[0]
					elif isinstance(lowercase						,  lowercase ) and isinstance(image[0]						,  (torch.Tensor, PIL.Image.Image) ):
									lowerCamelCase_						=			len(lowercase )
					else:
									raise ValueError(
									    f'`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(lowercase )}' )

					lowerCamelCase_						=			self._execution_device

					lowerCamelCase_						=			batch_size * num_images_per_prompt

					lowerCamelCase_						=			guidance_scale > 1.0
					lowerCamelCase_						=			self._encode_image(lowercase						,  lowercase						,  lowercase						,  lowercase )

					# prior

					self.scheduler.set_timesteps(lowercase						,  device=lowercase )
					lowerCamelCase_						=			self.scheduler.timesteps

					lowerCamelCase_						=			self.prior.config.num_embeddings
					lowerCamelCase_						=			self.prior.config.embedding_dim

					lowerCamelCase_						=			self.prepare_latents(
					    (batch_size, num_embeddings * embedding_dim)						,  image_embeds.dtype						,  lowercase						,  lowercase						,  lowercase						,  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
					lowerCamelCase_						=			latents.reshape(latents.shape[0]						,  lowercase						,  lowercase )

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

									lowerCamelCase_						=			self.prior(
									    lowercase						,  timestep=lowercase						,  proj_embedding=lowercase						,  ).predicted_image_embedding

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

									if do_classifier_free_guidance is not None:
													lowerCamelCase_	,    lowerCamelCase_						=			noise_pred.chunk(2 )
													lowerCamelCase_						=			noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

									lowerCamelCase_						=			self.scheduler.step(
									    lowercase						,  timestep=lowercase						,  sample=lowercase						,  ).prev_sample

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

					lowerCamelCase_						=			[]
					for i, latent in enumerate(lowercase ):
									print()
									lowerCamelCase_						=			self.renderer.decode(
									    latent[None, :]						,  lowercase						,  size=lowercase						,  ray_batch_size=4096						,  n_coarse_samples=64						,  n_fine_samples=128						,  )

									images.append(lowercase )

					lowerCamelCase_						=			torch.stack(lowercase )

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

					lowerCamelCase_						=			images.cpu().numpy()

					if output_type == "pil":
									lowerCamelCase_						=			[self.numpy_to_pil(lowercase ) 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=lowercase )


