
import gc
import unittest

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

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import 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 		UpperCamelCase__  (	__lowercase   ,unittest.TestCase   ):
   _SCREAMING_SNAKE_CASE				:							Any     	=				ShapEPipeline
   _SCREAMING_SNAKE_CASE				:							Any     	=				["prompt"]
   _SCREAMING_SNAKE_CASE				:							List[Any]     	=				["prompt"]
   _SCREAMING_SNAKE_CASE				:							List[str]     	=				[
       "num_images_per_prompt",
       "num_inference_steps",
       "generator",
       "latents",
       "guidance_scale",
       "frame_size",
       "output_type",
       "return_dict",
   ]
   _SCREAMING_SNAKE_CASE				:							Dict     	=				False
   @property
   def 				lowerCAmelCase      (self			: Optional[Any]       ):
    return 3_2
   @property
   def 				lowerCAmelCase      (self			: Dict       ):
    return 3_2
   @property
   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    return self.time_input_dim * 4
   @property
   def 				lowerCAmelCase      (self			: Dict       ):
    return 8
   @property
   def 				lowerCAmelCase      (self			: List[str]       ):
    __a			:  Tuple		    =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )
    return tokenizer
   @property
   def 				lowerCAmelCase      (self			: Dict       ):
    torch.manual_seed(0       )
    __a			:  Tuple		    =  CLIPTextConfig(
        bos_token_id=0 ,    eos_token_id=2 ,    hidden_size=self.text_embedder_hidden_size ,    projection_dim=self.text_embedder_hidden_size ,    intermediate_size=3_7 ,    layer_norm_eps=1E-05 ,    num_attention_heads=4 ,    num_hidden_layers=5 ,    pad_token_id=1 ,    vocab_size=1_0_0_0 ,    )
    return CLIPTextModelWithProjection(snake_case_       )
   @property
   def 				lowerCAmelCase      (self			: List[Any]       ):
    torch.manual_seed(0       )

    __a			:  Union[str, Any]		    =  {
        '''num_attention_heads''': 2,
        '''attention_head_dim''': 1_6,
        '''embedding_dim''': self.time_input_dim,
        '''num_embeddings''': 3_2,
        '''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''',
        '''encoder_hid_proj_type''': None,
        '''added_emb_type''': None,
    }

    __a			:  Union[str, Any]		    =  PriorTransformer(**snake_case_       )
    return model
   @property
   def 				lowerCAmelCase      (self			: Tuple       ):
    torch.manual_seed(0       )

    __a			:  Optional[int]		    =  {
        '''param_shapes''': (
            (self.renderer_dim, 9_3),
            (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''': 1_2,
        '''background''': (
            0.1,
            0.1,
            0.1,
        ),
    }
    __a			:  int		    =  ShapERenderer(**snake_case_       )
    return model
   def 				lowerCAmelCase      (self			: Any       ):
    __a			:  Optional[Any]		    =  self.dummy_prior
    __a			:  Dict		    =  self.dummy_text_encoder
    __a			:  List[Any]		    =  self.dummy_tokenizer
    __a			:  Any		    =  self.dummy_renderer

    __a			:  Dict		    =  HeunDiscreteScheduler(
        beta_schedule='''exp''' ,    num_train_timesteps=1_0_2_4 ,    prediction_type='''sample''' ,    use_karras_sigmas=snake_case_ ,    clip_sample=snake_case_ ,    clip_sample_range=1.0 ,    )
    __a			:  Union[str, Any]		    =  {
        '''prior''': prior,
        '''text_encoder''': text_encoder,
        '''tokenizer''': tokenizer,
        '''renderer''': renderer,
        '''scheduler''': scheduler,
    }

    return components
   def 				lowerCAmelCase      (self			: str ,    snake_case_			: Dict ,    snake_case_			: Optional[int]=0       ):
    if str(snake_case_       ).startswith('''mps'''       ):
     __a			:  List[str]		    =  torch.manual_seed(snake_case_       )
    else:
     __a			:  str		    =  torch.Generator(device=snake_case_       ).manual_seed(snake_case_       )
    __a			:  Dict		    =  {
        '''prompt''': '''horse''',
        '''generator''': generator,
        '''num_inference_steps''': 1,
        '''frame_size''': 3_2,
        '''output_type''': '''np''',
    }
    return inputs
   def 				lowerCAmelCase      (self			: int       ):
    __a			:  List[Any]		    =  '''cpu'''

    __a			:  Dict		    =  self.get_dummy_components()

    __a			:  str		    =  self.pipeline_class(**snake_case_       )
    __a			:  Optional[int]		    =  pipe.to(snake_case_       )

    pipe.set_progress_bar_config(disable=snake_case_       )

    __a			:  Any		    =  pipe(**self.get_dummy_inputs(snake_case_       )       )
    __a			:  List[str]		    =  output.images[0]
    __a			:  int		    =  image[0, -3:, -3:, -1]

    assert image.shape == (2_0, 3_2, 3_2, 3)

    __a			:  Union[str, Any]		    =  np.array(
        [
            0.0003_9216,
            0.0003_9216,
            0.0003_9216,
            0.0003_9216,
            0.0003_9216,
            0.0003_9216,
            0.0003_9216,
            0.0003_9216,
            0.0003_9216,
        ]       )

    assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-2
   def 				lowerCAmelCase      (self			: Any       ):
    # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
    self._test_inference_batch_consistent(batch_sizes=[1, 2]       )
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  str		    =  torch_device == '''cpu'''
    __a			:  int		    =  True

    self._test_inference_batch_single_identical(
        batch_size=2 ,    test_max_difference=snake_case_ ,    relax_max_difference=snake_case_ ,    )







   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  self.get_dummy_components()
    __a			:  Any		    =  self.pipeline_class(**snake_case_       )
    __a			:  Optional[int]		    =  pipe.to(snake_case_       )
    pipe.set_progress_bar_config(disable=snake_case_       )

    __a			:  int		    =  1
    __a			:  List[Any]		    =  2

    __a			:  Any		    =  self.get_dummy_inputs(snake_case_       )

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

    __a			:  List[Any]		    =  pipe(**snake_case_ ,    num_images_per_prompt=snake_case_       )[0]

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





@slow
@require_torch_gpu
class 		UpperCamelCase__  (	unittest.TestCase   ):
   def 				lowerCAmelCase      (self			: Dict       ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()







   def 				lowerCAmelCase      (self			: str       ):
    __a			:  int		    =  load_numpy(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
        '''/shap_e/test_shap_e_np_out.npy'''       )
    __a			:  Tuple		    =  ShapEPipeline.from_pretrained('''openai/shap-e'''       )
    __a			:  Union[str, Any]		    =  pipe.to(snake_case_       )
    pipe.set_progress_bar_config(disable=snake_case_       )

    __a			:  str		    =  torch.Generator(device=snake_case_       ).manual_seed(0       )

    __a			:  List[str]		    =  pipe(
        '''a shark''' ,    generator=snake_case_ ,    guidance_scale=15.0 ,    num_inference_steps=6_4 ,    frame_size=6_4 ,    output_type='''np''' ,    ).images[0]

    assert images.shape == (2_0, 6_4, 6_4, 3)

    assert_mean_pixel_difference(snake_case_ ,    snake_case_       )


def        __UpperCamelCase					(    lowerCAmelCase__							:    list[list[int | float]]  ):
 __a			:  int		    =  len(lowerCAmelCase__  )
 __a			:  Dict		    =  len(matrix[0]  )
 __a			:  Union[str, Any]		    =  min(lowerCAmelCase__  ,       lowerCAmelCase__  )

 for row in range(lowerCAmelCase__  ):
  # Check if diagonal element is not zero
  if matrix[row][row] != 0:
   # Eliminate all the elements below the diagonal
   for col in range(row + 1  ,       lowerCAmelCase__  ):
    __a			:  Dict		    =  matrix[col][row] / matrix[row][row]
    for i in range(lowerCAmelCase__  ,       lowerCAmelCase__  ):
     matrix[col][i] -= multiplier * matrix[row][i]
  else:
   # Find a non-zero diagonal element to swap rows
   __a			:  Optional[int]		    =  True
   for i in range(row + 1  ,       lowerCAmelCase__  ):
    if matrix[i][row] != 0:
     __a    ,	__a			:  Any		    =  matrix[i], matrix[row]
     __a			:  Union[str, Any]		    =  False
     break
   if reduce:
    rank -= 1
    for i in range(lowerCAmelCase__  ):
     __a			:  Optional[Any]		    =  matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
   row -= 1

 return rank


if __name__ == "__main__":
      import doctest

      doctest.testmod()
