



"""simple docstring"""




import unittest

import numpy as np

from transformers import is_flax_available
from transformers.testing_utils import require_flax

from ..test_modeling_flax_common import ids_tensor


if is_flax_available():
    import jax
    import jax.numpy as jnp

    from transformers.generation import (
        FlaxForcedBOSTokenLogitsProcessor,
        FlaxForcedEOSTokenLogitsProcessor,
        FlaxLogitsProcessorList,
        FlaxMinLengthLogitsProcessor,
        FlaxTemperatureLogitsWarper,
        FlaxTopKLogitsWarper,
        FlaxTopPLogitsWarper,
    )



@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  List[str]						     =     jnp.ones((batch_size, length)	) / length
          return scores
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Optional[Any]						     =     None
          UpperCamelCase   :  Optional[int]						     =     20

          UpperCamelCase   :  Optional[Any]						     =     self._get_uniform_logits(batch_size=2,      length=SCREAMING_SNAKE_CASE_	)

          # tweak scores to not be uniform anymore
          UpperCamelCase   :  Dict						     =     scores.at[1, 5].set((1 / length) + 0.1	)  # peak, 1st batch
          UpperCamelCase   :  Any						     =     scores.at[1, 10].set((1 / length) - 0.4	)  # valley, 1st batch

          # compute softmax
          UpperCamelCase   :  List[str]						     =     jax.nn.softmax(SCREAMING_SNAKE_CASE_,      axis=-1	)

          UpperCamelCase   :  List[Any]						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  int						     =     FlaxTemperatureLogitsWarper(temperature=1.3	)

          UpperCamelCase   :  Tuple						     =     jax.nn.softmax(temp_dist_warper_sharper(SCREAMING_SNAKE_CASE_,      scores.copy(),      cur_len=SCREAMING_SNAKE_CASE_	),      axis=-1	)
          UpperCamelCase   :  Any						     =     jax.nn.softmax(temp_dist_warper_smoother(SCREAMING_SNAKE_CASE_,      scores.copy(),      cur_len=SCREAMING_SNAKE_CASE_	),      axis=-1	)

          # uniform distribution stays uniform
          self.assertTrue(jnp.allclose(probs[0, :],      warped_prob_sharp[0, :],      atol=1e-3	)	)
          self.assertTrue(jnp.allclose(probs[0, :],      warped_prob_smooth[0, :],      atol=1e-3	)	)

          # sharp peaks get higher, valleys get lower
          self.assertLess(probs[1, :].max(),      warped_prob_sharp[1, :].max()	)
          self.assertGreater(probs[1, :].min(),      warped_prob_sharp[1, :].min()	)

          # smooth peaks get lower, valleys get higher
          self.assertGreater(probs[1, :].max(),      warped_prob_smooth[1, :].max()	)
          self.assertLess(probs[1, :].min(),      warped_prob_smooth[1, :].min()	)
       def  snake_case_	(      self	)					->		Optional[Any]:
          UpperCamelCase   :  Dict						     =     None
          UpperCamelCase   :  Any						     =     10
          UpperCamelCase   :  Any						     =     2

          # create ramp distribution
          UpperCamelCase   :  List[Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, vocab_size)	).copy()
          UpperCamelCase   :  Tuple						     =     ramp_logits[1:, : vocab_size // 2] + vocab_size

          UpperCamelCase   :  Dict						     =     FlaxTopKLogitsWarper(3	)

          UpperCamelCase   :  Tuple						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # check that correct tokens are filtered
          self.assertListEqual(jnp.isinf(scores[0]	).tolist(),      7 * [True] + 3 * [False]	)
          self.assertListEqual(jnp.isinf(scores[1]	).tolist(),      2 * [True] + 3 * [False] + 5 * [True]	)

          # check special case
          UpperCamelCase   :  Optional[int]						     =     5
          UpperCamelCase   :  Optional[int]						     =     FlaxTopKLogitsWarper(top_k=1,      filter_value=0.0,      min_tokens_to_keep=3	)

          UpperCamelCase   :  Union[str, Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, length)	).copy()
          UpperCamelCase   :  List[str]						     =     top_k_warp_safety_check(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
          self.assertListEqual((scores == 0.0).sum(axis=-1	).tolist(),      [2, 2]	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  int						     =     None
          UpperCamelCase   :  List[str]						     =     10
          UpperCamelCase   :  Optional[Any]						     =     2

          # create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
          UpperCamelCase   :  Optional[int]						     =     np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]]	)	)

          UpperCamelCase   :  Optional[Any]						     =     FlaxTopPLogitsWarper(0.8	)
          UpperCamelCase   :  int						     =     np.exp(top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)	)

          # dist should be filtered to keep min num values so that sum is >= top_p
          # exp (-inf) => 0
          UpperCamelCase   :  Any						     =     np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]]	)
          self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # check edge cases with negative and extreme logits
          UpperCamelCase   :  Optional[Any]						     =     np.broadcast_to(np.arange(SCREAMING_SNAKE_CASE_	)[None, :],      (batch_size, vocab_size)	).copy() - (
              vocab_size // 2
          )

          # make ramp_logits more extreme
          UpperCamelCase   :  Tuple						     =     ramp_logits[1] * 100.0

          # make sure at least 2 tokens are kept
          UpperCamelCase   :  int						     =     FlaxTopPLogitsWarper(0.9,      min_tokens_to_keep=2,      filter_value=0.0	)
          UpperCamelCase   :  List[str]						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
          self.assertListEqual((filtered_dist != 0.0).sum(axis=-1	).tolist(),      [3, 2]	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Union[str, Any]						     =     20
          UpperCamelCase   :  Union[str, Any]						     =     4
          UpperCamelCase   :  Optional[int]						     =     0

          UpperCamelCase   :  Dict						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that min length is applied at length 5
          UpperCamelCase   :  List[str]						     =     ids_tensor((batch_size, 20),      vocab_size=20	)
          UpperCamelCase   :  Any						     =     5
          UpperCamelCase   :  Tuple						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Tuple						     =     min_dist_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist(),      4 * [-float('inf'	)]	)

          # check that min length is not applied anymore at length 15
          UpperCamelCase   :  Any						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     15
          UpperCamelCase   :  str						     =     min_dist_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  str						     =     20
          UpperCamelCase   :  List[Any]						     =     4
          UpperCamelCase   :  List[str]						     =     0

          UpperCamelCase   :  int						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that all scores are -inf except the bos_token_id score
          UpperCamelCase   :  Any						     =     ids_tensor((batch_size, 1),      vocab_size=20	)
          UpperCamelCase   :  List[Any]						     =     1
          UpperCamelCase   :  Tuple						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :]	).all()	)
          self.assertListEqual(scores[:, bos_token_id].tolist(),      4 * [0]	)  # score for bos_token_id shold be zero

          # check that bos_token_id is not forced if current length is greater than 1
          UpperCamelCase   :  Dict						     =     3
          UpperCamelCase   :  str						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Union[str, Any]						     =     20
          UpperCamelCase   :  Optional[Any]						     =     4
          UpperCamelCase   :  List[Any]						     =     0
          UpperCamelCase   :  int						     =     5

          UpperCamelCase   :  Tuple						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          # check that all scores are -inf except the eos_token_id when max_length is reached
          UpperCamelCase   :  str						     =     ids_tensor((batch_size, 4),      vocab_size=20	)
          UpperCamelCase   :  Tuple						     =     4
          UpperCamelCase   :  Union[str, Any]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :]	).all()	)
          self.assertListEqual(scores[:, eos_token_id].tolist(),      4 * [0]	)  # score for eos_token_id should be zero

          # check that eos_token_id is not forced if max_length is not reached
          UpperCamelCase   :  str						     =     3
          UpperCamelCase   :  List[Any]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     logits_processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          self.assertFalse(jnp.isinf(SCREAMING_SNAKE_CASE_	).any()	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  int						     =     4
          UpperCamelCase   :  Tuple						     =     10
          UpperCamelCase   :  str						     =     15
          UpperCamelCase   :  List[str]						     =     2
          UpperCamelCase   :  Any						     =     1
          UpperCamelCase   :  List[str]						     =     15

          # dummy input_ids and scores
          UpperCamelCase   :  Dict						     =     ids_tensor((batch_size, sequence_length),      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     input_ids.copy()

          UpperCamelCase   :  Any						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     scores.copy()

          # instantiate all dist processors
          UpperCamelCase   :  List[str]						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  Optional[Any]						     =     FlaxTopKLogitsWarper(3	)
          UpperCamelCase   :  Optional[int]						     =     FlaxTopPLogitsWarper(0.8	)

          # instantiate all logits processors
          UpperCamelCase   :  Optional[Any]						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[Any]						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     10

          # no processor list
          UpperCamelCase   :  Any						     =     temp_dist_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Dict						     =     min_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     bos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     eos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # with processor list
          UpperCamelCase   :  List[str]						     =     FlaxLogitsProcessorList(
              [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]	)
          UpperCamelCase   :  Optional[Any]						     =     processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)

          # scores should be equal
          self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist(),      input_ids_comp.tolist()	)






       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Optional[Any]						     =     4
          UpperCamelCase   :  Tuple						     =     10
          UpperCamelCase   :  Union[str, Any]						     =     15
          UpperCamelCase   :  Union[str, Any]						     =     2
          UpperCamelCase   :  Optional[Any]						     =     1
          UpperCamelCase   :  int						     =     15

          # dummy input_ids and scores
          UpperCamelCase   :  Dict						     =     ids_tensor((batch_size, sequence_length),      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     input_ids.copy()

          UpperCamelCase   :  Optional[int]						     =     self._get_uniform_logits(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Union[str, Any]						     =     scores.copy()

          # instantiate all dist processors
          UpperCamelCase   :  Dict						     =     FlaxTemperatureLogitsWarper(temperature=0.5	)
          UpperCamelCase   :  Optional[Any]						     =     FlaxTopKLogitsWarper(3	)
          UpperCamelCase   :  Union[str, Any]						     =     FlaxTopPLogitsWarper(0.8	)

          # instantiate all logits processors
          UpperCamelCase   :  str						     =     FlaxMinLengthLogitsProcessor(min_length=10,      eos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     FlaxForcedBOSTokenLogitsProcessor(bos_token_id=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     FlaxForcedEOSTokenLogitsProcessor(max_length=SCREAMING_SNAKE_CASE_,      eos_token_id=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Any						     =     10

          # no processor list
          def run_no_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Optional[Any]						     =     temp_dist_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  str						     =     top_k_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  Any						     =     top_p_warp(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     min_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     bos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             UpperCamelCase   :  List[str]						     =     eos_dist_proc(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             return scores

          # with processor list
          def run_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
             UpperCamelCase   :  Tuple						     =     FlaxLogitsProcessorList(
                 [temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc]	)
             UpperCamelCase   :  Union[str, Any]						     =     processor(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      cur_len=SCREAMING_SNAKE_CASE_	)
             return scores

          UpperCamelCase   :  Dict						     =     jax.jit(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  List[str]						     =     jax.jit(SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  int						     =     jitted_run_no_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  int						     =     jitted_run_processor_list(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)

          # scores should be equal
          self.assertTrue(jnp.allclose(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      atol=1e-3	)	)

          # input_ids should never be changed
          self.assertListEqual(input_ids.tolist(),      input_ids_comp.tolist()	)








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     lowerCAmelCase_		(  a__	,   unittest.TestCase							):
       UpperCAmelCase__    : Union[str, Any]       					=  ShapEPipeline
       UpperCAmelCase__    : List[Any]       					=  ["prompt"]
       UpperCAmelCase__    : List[str]       					=  ["prompt"]
       UpperCAmelCase__    : int       					=  [
           "num_images_per_prompt",
           "num_inference_steps",
           "generator",
           "latents",
           "guidance_scale",
           "frame_size",
           "output_type",
           "return_dict",
       ]
       UpperCAmelCase__    : Union[str, Any]       					=  False
       @property
       def  snake_case_	(      self	)					->		Union[str, Any]:
          return 32
       @property
       def  snake_case_	(      self	)					->		List[str]:
          return 32
       @property
       def  snake_case_	(      self	)					->		int:
          return self.time_input_dim * 4
       @property
       def  snake_case_	(      self	)					->		Optional[int]:
          return 8
       @property
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Union[str, Any]						     =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)
          return tokenizer
       @property
       def  snake_case_	(      self	)					->		Optional[Any]:
          torch.manual_seed(0	)
          UpperCamelCase   :  Dict						     =     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=37,      layer_norm_eps=1e-05,      num_attention_heads=4,      num_hidden_layers=5,      pad_token_id=1,      vocab_size=1000,      )
          return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE_	)
       @property
       def  snake_case_	(      self	)					->		Dict:
          torch.manual_seed(0	)

          UpperCamelCase   :  int						     =     {
              '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',
              'encoder_hid_proj_type': None,
              'added_emb_type': None,
          }

          UpperCamelCase   :  Any						     =     PriorTransformer(**SCREAMING_SNAKE_CASE_	)
          return model
       @property
       def  snake_case_	(      self	)					->		Tuple:
          torch.manual_seed(0	)

          UpperCamelCase   :  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,
              ),
          }
          UpperCamelCase   :  Dict						     =     ShapERenderer(**SCREAMING_SNAKE_CASE_	)
          return model
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  List[Any]						     =     self.dummy_prior
          UpperCamelCase   :  int						     =     self.dummy_text_encoder
          UpperCamelCase   :  Dict						     =     self.dummy_tokenizer
          UpperCamelCase   :  List[str]						     =     self.dummy_renderer

          UpperCamelCase   :  str						     =     HeunDiscreteScheduler(
              beta_schedule='exp',      num_train_timesteps=1024,      prediction_type='sample',      use_karras_sigmas=SCREAMING_SNAKE_CASE_,      clip_sample=SCREAMING_SNAKE_CASE_,      clip_sample_range=1.0,      )
          UpperCamelCase   :  List[str]						     =     {
              'prior': prior,
              'text_encoder': text_encoder,
              'tokenizer': tokenizer,
              'renderer': renderer,
              'scheduler': scheduler,
          }

          return components
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=0	)					->		Any:
          if str(SCREAMING_SNAKE_CASE_	).startswith('mps'	):
             UpperCamelCase   :  Optional[int]						     =     torch.manual_seed(SCREAMING_SNAKE_CASE_	)
          else:
             UpperCamelCase   :  Any						     =     torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  str						     =     {
              'prompt': 'horse',
              'generator': generator,
              'num_inference_steps': 1,
              'frame_size': 32,
              'output_type': 'np',
          }
          return inputs
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  str						     =     'cpu'

          UpperCamelCase   :  Optional[int]						     =     self.get_dummy_components()

          UpperCamelCase   :  int						     =     self.pipeline_class(**SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[Any]						     =     pipe.to(SCREAMING_SNAKE_CASE_	)

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)	)
          UpperCamelCase   :  Optional[int]						     =     output.images[0]
          UpperCamelCase   :  Union[str, Any]						     =     image[0, -3:, -3:, -1]

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

          UpperCamelCase   :  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  snake_case_	(      self	)					->		int:
          # NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
          self._test_inference_batch_consistent(batch_sizes=[1, 2]	)
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  str						     =     torch_device == 'cpu'
          UpperCamelCase   :  Optional[int]						     =     True

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






       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  List[Any]						     =     self.get_dummy_components()
          UpperCamelCase   :  str						     =     self.pipeline_class(**SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Optional[int]						     =     pipe.to(SCREAMING_SNAKE_CASE_	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  Optional[Any]						     =     1
          UpperCamelCase   :  Union[str, Any]						     =     2

          UpperCamelCase   :  Union[str, Any]						     =     self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)

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

          UpperCamelCase   :  int						     =     pipe(**SCREAMING_SNAKE_CASE_,      num_images_per_prompt=SCREAMING_SNAKE_CASE_	)[0]

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






@slow
@require_torch_gpu
class     lowerCAmelCase_		(  unittest.TestCase							):
       def  snake_case_	(      self	)					->		str:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()






       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Optional[int]						     =     load_numpy(
              'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
              '/shap_e/test_shap_e_np_out.npy'	)
          UpperCamelCase   :  Optional[int]						     =     ShapEPipeline.from_pretrained('openai/shap-e'	)
          UpperCamelCase   :  Optional[int]						     =     pipe.to(SCREAMING_SNAKE_CASE_	)
          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  str						     =     torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(0	)

          UpperCamelCase   :  Dict						     =     pipe(
              'a shark',      generator=SCREAMING_SNAKE_CASE_,      guidance_scale=15.0,      num_inference_steps=64,      frame_size=64,      output_type='np',      ).images[0]

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

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)



