


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"""






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

     @property
     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							Union[str, Any]:
          return 32

     @property
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							int:
          return 32

     @property
     def 			lowerCamelCase__			(    self					:		str      )						->							List[str]:
          return self.time_input_dim * 4

     @property
     def 			lowerCamelCase__			(    self					:		List[Any]      )						->							str:
          return 8

     @property
     def 			lowerCamelCase__			(    self					:		int      )						->							Dict:
          torch.manual_seed(0      )
          __UpperCAmelCase     :			int  							=      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							,			)

          __UpperCAmelCase     :			List[Any]  							=      CLIPVisionModel(UpperCamelCase_      )
          return model

     @property
     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      CLIPImageProcessor(
              crop_size=224							,			do_center_crop=UpperCamelCase_							,			do_normalize=UpperCamelCase_							,			do_resize=UpperCamelCase_							,			image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]							,			image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]							,			resample=3							,			size=224							,			)

          return image_processor

     @property
     def 			lowerCamelCase__			(    self					:		List[str]      )						->							str:
          torch.manual_seed(0      )

          __UpperCAmelCase     :			Dict  							=      {
              '''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,
          }

          __UpperCAmelCase     :			Tuple  							=      PriorTransformer(**UpperCamelCase_      )
          return model

     @property
     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[Any]:
          torch.manual_seed(0      )

          __UpperCAmelCase     :			int  							=      {
              '''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,
              ),
          }
          __UpperCAmelCase     :			Tuple  							=      ShapERenderer(**UpperCamelCase_      )
          return model

     def 			lowerCamelCase__			(    self					:		str      )						->							Any:
          __UpperCAmelCase     :			List[Any]  							=      self.dummy_prior
          __UpperCAmelCase     :			Any  							=      self.dummy_image_encoder
          __UpperCAmelCase     :			Optional[Any]  							=      self.dummy_image_processor
          __UpperCAmelCase     :			Any  							=      self.dummy_renderer

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

          return components

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		Optional[int]=0      )						->							Tuple:
          __UpperCAmelCase     :			Tuple  							=      floats_tensor((1, 3, 64, 64)							,			rng=random.Random(UpperCamelCase_      )      ).to(UpperCamelCase_      )

          if str(UpperCamelCase_      ).startswith('''mps'''      ):
               __UpperCAmelCase     :			Any  							=      torch.manual_seed(UpperCamelCase_      )
          else:
               __UpperCAmelCase     :			Optional[int]  							=      torch.Generator(device=UpperCamelCase_      ).manual_seed(UpperCamelCase_      )
          __UpperCAmelCase     :			str  							=      {
              '''image''': input_image,
              '''generator''': generator,
              '''num_inference_steps''': 1,
              '''frame_size''': 32,
              '''output_type''': '''np''',
          }
          return inputs

     def 			lowerCamelCase__			(    self					:		int      )						->							str:
          __UpperCAmelCase     :			Tuple  							=      '''cpu'''

          __UpperCAmelCase     :			Any  							=      self.get_dummy_components()

          __UpperCAmelCase     :			Union[str, Any]  							=      self.pipeline_class(**UpperCamelCase_      )
          __UpperCAmelCase     :			Any  							=      pipe.to(UpperCamelCase_      )

          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          __UpperCAmelCase     :			List[Any]  							=      pipe(**self.get_dummy_inputs(UpperCamelCase_      )      )
          __UpperCAmelCase     :			Optional[int]  							=      output.images[0]
          __UpperCAmelCase     :			Optional[Any]  							=      image[0, -3:, -3:, -1]

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

          __UpperCAmelCase     :			Optional[int]  							=      np.array(
              [
                  0.00_039_216,
                  0.00_039_216,
                  0.00_039_216,
                  0.00_039_216,
                  0.00_039_216,
                  0.00_039_216,
                  0.00_039_216,
                  0.00_039_216,
                  0.00_039_216,
              ]      )

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

     def 			lowerCamelCase__			(    self					:		int      )						->							int:
          self._test_inference_batch_consistent(batch_sizes=[1, 2]      )

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							List[Any]:
          __UpperCAmelCase     :			List[str]  							=      torch_device == '''cpu'''
          __UpperCAmelCase     :			Any  							=      True
          self._test_inference_batch_single_identical(
              batch_size=2							,			test_max_difference=UpperCamelCase_							,			relax_max_difference=UpperCamelCase_							,			)






     def 			lowerCamelCase__			(    self					:		List[str]      )						->							str:
          __UpperCAmelCase     :			int  							=      self.get_dummy_components()
          __UpperCAmelCase     :			Dict  							=      self.pipeline_class(**UpperCamelCase_      )
          __UpperCAmelCase     :			Any  							=      pipe.to(UpperCamelCase_      )
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          __UpperCAmelCase     :			int  							=      1
          __UpperCAmelCase     :			Tuple  							=      2

          __UpperCAmelCase     :			Optional[int]  							=      self.get_dummy_inputs(UpperCamelCase_      )

          for key in inputs.keys():
               if key in self.batch_params:
                    __UpperCAmelCase     :			Optional[int]  							=      batch_size * [inputs[key]]

          __UpperCAmelCase     :			int  							=      pipe(**UpperCamelCase_							,			num_images_per_prompt=UpperCamelCase_      )[0]

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



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


     """simple docstring"""






     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






     def 			lowerCamelCase__			(    self					:		str      )						->							str:
          __UpperCAmelCase     :			Union[str, Any]  							=      load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png'''      )
          __UpperCAmelCase     :			List[Any]  							=      load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/shap_e/test_shap_e_img2img_out.npy'''      )
          __UpperCAmelCase     :			str  							=      ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img'''      )
          __UpperCAmelCase     :			int  							=      pipe.to(UpperCamelCase_      )
          pipe.set_progress_bar_config(disable=UpperCamelCase_      )

          __UpperCAmelCase     :			Dict  							=      torch.Generator(device=UpperCamelCase_      ).manual_seed(0      )

          __UpperCAmelCase     :			Optional[int]  							=      pipe(
              UpperCamelCase_							,			generator=UpperCamelCase_							,			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(UpperCamelCase_							,			UpperCamelCase_      )



'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


__UpperCAmelCase      :Union[str, Any]			    =       {
    "configuration_rag": ["RagConfig"],
    "retrieval_rag": ["RagRetriever"],
    "tokenization_rag": ["RagTokenizer"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "RagModel",
          "RagPreTrainedModel",
          "RagSequenceForGeneration",
          "RagTokenForGeneration",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :Dict			    =       [
          "TFRagModel",
          "TFRagPreTrainedModel",
          "TFRagSequenceForGeneration",
          "TFRagTokenForGeneration",
      ]


if TYPE_CHECKING:
      from .configuration_rag import RagConfig
      from .retrieval_rag import RagRetriever
      from .tokenization_rag import RagTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rag import (
                TFRagModel,
                TFRagPreTrainedModel,
                TFRagSequenceForGeneration,
                TFRagTokenForGeneration,
            )

else:
      import sys

      __UpperCAmelCase      :Any			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)