





import gc
import random
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference





class  A__    (							__UpperCAmelCase ,  unittest.TestCase	):




   """simple docstring"""



   __A   :				Optional[int]         =  ShapEImgaImgPipeline
   __A   :				Tuple         =  ['''image''']
   __A   :				Any         =  ['''image''']
   __A   :				Optional[Any]         =  [
       '''num_images_per_prompt''',
       '''num_inference_steps''',
       '''generator''',
       '''latents''',
       '''guidance_scale''',
       '''frame_size''',
       '''output_type''',
       '''return_dict''',
   ]
   __A   :				Dict         =  False

   @property
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          return 32

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return 32

   @property
   def 							__lowercase		(					self)			->						Optional[Any]:







          '''simple docstring'''

          return self.time_input_dim * 4

   @property
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          return 8

   @property
   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          torch.manual_seed(0)
          a__						:	Any =				CLIPVisionConfig(
              hidden_size=self.text_embedder_hidden_size					,  image_size=64					,  projection_dim=self.text_embedder_hidden_size					,  intermediate_size=37					,  num_attention_heads=4					,  num_channels=3					,  num_hidden_layers=5					,  patch_size=1					,  )

          a__						:	Dict =				CLIPVisionModel(lowercase)
          return model

   @property
   def 							__lowercase		(					self)			->						List[Any]:







          '''simple docstring'''

          a__						:	str =				CLIPImageProcessor(
              crop_size=224					,  do_center_crop=lowercase					,  do_normalize=lowercase					,  do_resize=lowercase					,  image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,  image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,  resample=3					,  size=224					,  )

          return image_processor

   @property
   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          torch.manual_seed(0)

          a__						:	str =				{
              'num_attention_heads': 2,
              'attention_head_dim': 16,
              'embedding_dim': self.time_input_dim,
              'num_embeddings': 32,
              'embedding_proj_dim': self.text_embedder_hidden_size,
              'time_embed_dim': self.time_embed_dim,
              'num_layers': 1,
              'clip_embed_dim': self.time_input_dim * 2,
              'additional_embeddings': 0,
              'time_embed_act_fn': 'gelu',
              'norm_in_type': 'layer',
              'embedding_proj_norm_type': 'layer',
              'encoder_hid_proj_type': None,
              'added_emb_type': None,
          }

          a__						:	Any =				PriorTransformer(**lowercase)
          return model

   @property
   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          torch.manual_seed(0)

          a__						:	List[Any] =				{
              'param_shapes': (
                  (self.renderer_dim, 93),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
                  (self.renderer_dim, 8),
              ),
              'd_latent': self.time_input_dim,
              'd_hidden': self.renderer_dim,
              'n_output': 12,
              'background': (
                  0.1,
                  0.1,
                  0.1,
              ),
          }
          a__						:	List[str] =				ShapERenderer(**lowercase)
          return model

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          a__						:	Dict =				self.dummy_prior
          a__						:	List[str] =				self.dummy_image_encoder
          a__						:	int =				self.dummy_image_processor
          a__						:	str =				self.dummy_renderer

          a__						:	Optional[int] =				HeunDiscreteScheduler(
              beta_schedule='exp'					,  num_train_timesteps=1024					,  prediction_type='sample'					,  use_karras_sigmas=lowercase					,  clip_sample=lowercase					,  clip_sample_range=1.0					,  )
          a__						:	List[Any] =				{
              'prior': prior,
              'image_encoder': image_encoder,
              'image_processor': image_processor,
              'renderer': renderer,
              'scheduler': scheduler,
          }

          return components

   def 							__lowercase		(					self					,  lowercase					,  lowercase=0)			->						List[str]:







          '''simple docstring'''

          a__						:	List[str] =				floats_tensor((1, 3, 64, 64)					,  rng=random.Random(lowercase)).to(lowercase)

          if str(lowercase).startswith('mps'):
                 a__						:	List[str] =				torch.manual_seed(lowercase)
          else:
                 a__						:	str =				torch.Generator(device=lowercase).manual_seed(lowercase)
          a__						:	Tuple =				{
              'image': input_image,
              'generator': generator,
              'num_inference_steps': 1,
              'frame_size': 32,
              'output_type': 'np',
          }
          return inputs

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	int =				'cpu'

          a__						:	List[str] =				self.get_dummy_components()

          a__						:	Dict =				self.pipeline_class(**lowercase)
          a__						:	Optional[int] =				pipe.to(lowercase)

          pipe.set_progress_bar_config(disable=lowercase)

          a__						:	Tuple =				pipe(**self.get_dummy_inputs(lowercase))
          a__						:	Any =				output.images[0]
          a__						:	Any =				image[0, -3:, -3:, -1]

          assert image.shape == (20, 32, 32, 3)

          a__						:	List[str] =				np.array(
              [
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
                  0.00_03_92_16,
              ])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2

   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          self._test_inference_batch_consistent(batch_sizes=[1, 2])

   def 							__lowercase		(					self)			->						Union[str, Any]:







          '''simple docstring'''

          a__						:	str =				torch_device == 'cpu'
          a__						:	Tuple =				True
          self._test_inference_batch_single_identical(
              batch_size=2					,  test_max_difference=lowercase					,  relax_max_difference=lowercase					,  )

   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	List[str] =				self.get_dummy_components()
          a__						:	str =				self.pipeline_class(**lowercase)
          a__						:	List[str] =				pipe.to(lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__						:	Optional[Any] =				1
          a__						:	List[str] =				2

          a__						:	Optional[Any] =				self.get_dummy_inputs(lowercase)

          for key in inputs.keys():
                 if key in self.batch_params:
                        a__						:	Any =				batch_size * [inputs[key]]

          a__						:	int =				pipe(**lowercase					,  num_images_per_prompt=lowercase)[0]

          assert images.shape[0] == batch_size * num_images_per_prompt





@slow
@require_torch_gpu
class  A__    (							unittest.TestCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

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

   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          a__						:	str =				load_image(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png')
          a__						:	str =				load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/shap_e/test_shap_e_img2img_out.npy')
          a__						:	List[str] =				ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img')
          a__						:	Tuple =				pipe.to(lowercase)
          pipe.set_progress_bar_config(disable=lowercase)

          a__						:	List[Any] =				torch.Generator(device=lowercase).manual_seed(0)

          a__						:	Optional[int] =				pipe(
              lowercase					,  generator=lowercase					,  guidance_scale=3.0					,  num_inference_steps=64					,  frame_size=64					,  output_type='np'					,  ).images[0]

          assert images.shape == (20, 64, 64, 3)

          assert_mean_pixel_difference(lowercase					,  lowercase)






"""simple docstring"""


import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A     :   Dict			    =			logging.getLogger(__name__)
@dataclass
class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=0.0       ,metadata={"""help""": """The label smoothing epsilon to apply (if not zero)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Whether to SortishSamler or not."""}		)
							__UpperCAmelCase						:    bool				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Whether to use generate to calculate generative metrics (ROUGE, BLEU)."""}		)
							__UpperCAmelCase						:    bool				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """whether to use adafactor"""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Encoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Decoder layer dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(default=lowerCAmelCase__       ,metadata={"""help""": """Dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[float]				      =field(
							    default=lowerCAmelCase__       ,metadata={"""help""": """Attention dropout probability. Goes into model.config."""}		)
							__UpperCAmelCase						:    Optional[str]				      =field(
							    default="""linear"""       ,metadata={"""help""": F'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'''}       ,)




