



"""simple docstring"""




import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
    import os

    # The slow tests are often failing with OOM error on GPU
    # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
    # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
    __UpperCAmelCase	       =	'''platform'''

    import jax
    import jax.numpy as jnp

    from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
        FlaxBlenderbotSmallForConditionalGeneration,
        FlaxBlenderbotSmallModel,
        shift_tokens_right,
    )
def 		UpperCamelCase     (       snake_case__	:   Optional[Any]	,					snake_case__	:   List[str]	,					snake_case__	:   Optional[int]=None	,					snake_case__	:   Dict=None	,					snake_case__	:   str=None	,					snake_case__	:   Union[str, Any]=None	,					snake_case__	:   Dict=None	,					snake_case__	:   Dict=None	,					)     ->					List[Any]:





   if attention_mask is None:
      UpperCamelCase   :  str						     =     np.where(input_ids != config.pad_token_id	,					1	,					0     )
   if decoder_attention_mask is None:
      UpperCamelCase   :  List[str]						     =     np.where(decoder_input_ids != config.pad_token_id	,					1	,					0     )
   if head_mask is None:
      UpperCamelCase   :  str						     =     np.ones((config.encoder_layers, config.encoder_attention_heads)     )
   if decoder_head_mask is None:
      UpperCamelCase   :  Dict						     =     np.ones((config.decoder_layers, config.decoder_attention_heads)     )
   if cross_attn_head_mask is None:
      UpperCamelCase   :  Optional[Any]						     =     np.ones((config.decoder_layers, config.decoder_attention_heads)     )
   return {
       "input_ids": input_ids,
       "decoder_input_ids": decoder_input_ids,
       "attention_mask": attention_mask,
       "decoder_attention_mask": attention_mask,
   }



class     lowerCAmelCase_		:
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=13,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=False,      SCREAMING_SNAKE_CASE_=99,      SCREAMING_SNAKE_CASE_=16,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_=4,      SCREAMING_SNAKE_CASE_="gelu",      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=0.1,      SCREAMING_SNAKE_CASE_=32,      SCREAMING_SNAKE_CASE_=2,      SCREAMING_SNAKE_CASE_=1,      SCREAMING_SNAKE_CASE_=0,      SCREAMING_SNAKE_CASE_=0.02,      )					->		List[str]:
          UpperCamelCase   :  Union[str, Any]						     =     parent
          UpperCamelCase   :  List[str]						     =     batch_size
          UpperCamelCase   :  List[Any]						     =     seq_length
          UpperCamelCase   :  List[str]						     =     is_training
          UpperCamelCase   :  int						     =     use_labels
          UpperCamelCase   :  Optional[Any]						     =     vocab_size
          UpperCamelCase   :  Optional[Any]						     =     hidden_size
          UpperCamelCase   :  Union[str, Any]						     =     num_hidden_layers
          UpperCamelCase   :  List[str]						     =     num_attention_heads
          UpperCamelCase   :  Any						     =     intermediate_size
          UpperCamelCase   :  Tuple						     =     hidden_act
          UpperCamelCase   :  int						     =     hidden_dropout_prob
          UpperCamelCase   :  Dict						     =     attention_probs_dropout_prob
          UpperCamelCase   :  List[str]						     =     max_position_embeddings
          UpperCamelCase   :  Tuple						     =     eos_token_id
          UpperCamelCase   :  Dict						     =     pad_token_id
          UpperCamelCase   :  Any						     =     bos_token_id
          UpperCamelCase   :  Optional[int]						     =     initializer_range
       def  snake_case_	(      self	)					->		List[str]:
          UpperCamelCase   :  Tuple						     =     np.clip(ids_tensor([self.batch_size, self.seq_length - 1],      self.vocab_size	),      3,      self.vocab_size	)
          UpperCamelCase   :  Dict						     =     np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1),      dtype=np.intaa	)),      -1	)

          UpperCamelCase   :  Tuple						     =     shift_tokens_right(_UpperCAmelCase,      1,      2	)

          UpperCamelCase   :  Optional[Any]						     =     BlenderbotSmallConfig(
              vocab_size=self.vocab_size,      d_model=self.hidden_size,      encoder_layers=self.num_hidden_layers,      decoder_layers=self.num_hidden_layers,      encoder_attention_heads=self.num_attention_heads,      decoder_attention_heads=self.num_attention_heads,      encoder_ffn_dim=self.intermediate_size,      decoder_ffn_dim=self.intermediate_size,      dropout=self.hidden_dropout_prob,      attention_dropout=self.attention_probs_dropout_prob,      max_position_embeddings=self.max_position_embeddings,      eos_token_id=self.eos_token_id,      bos_token_id=self.bos_token_id,      pad_token_id=self.pad_token_id,      initializer_range=self.initializer_range,      use_cache=_UpperCAmelCase,      )
          UpperCamelCase   :  Tuple						     =     prepare_blenderbot_inputs_dict(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase	)
          return config, inputs_dict
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase     ,					UpperCamelCase   :  Any						     =     self.prepare_config_and_inputs()
          return config, inputs_dict
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Any:
          UpperCamelCase   :  Union[str, Any]						     =     20
          UpperCamelCase   :  List[Any]						     =     model_class_name(_UpperCAmelCase	)

          UpperCamelCase   :  int						     =     model.encode(inputs_dict['input_ids']	)

          UpperCamelCase     ,					UpperCamelCase   :  Optional[int]						     =     (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCamelCase   :  int						     =     model.init_cache(decoder_input_ids.shape[0],      _UpperCAmelCase,      _UpperCAmelCase	)
          UpperCamelCase   :  int						     =     jnp.ones((decoder_input_ids.shape[0], max_decoder_length),      dtype='i4'	)

          UpperCamelCase   :  int						     =     jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :],      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),      )
          UpperCamelCase   :  List[Any]						     =     model.decode(
              decoder_input_ids[:, :-1],      _UpperCAmelCase,      decoder_attention_mask=_UpperCAmelCase,      past_key_values=_UpperCAmelCase,      decoder_position_ids=_UpperCAmelCase,      )

          UpperCamelCase   :  Any						     =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],      dtype='i4'	)
          UpperCamelCase   :  Optional[int]						     =     model.decode(
              decoder_input_ids[:, -1:],      _UpperCAmelCase,      decoder_attention_mask=_UpperCAmelCase,      past_key_values=outputs_cache.past_key_values,      decoder_position_ids=_UpperCAmelCase,      )

          UpperCamelCase   :  Dict						     =     model.decode(_UpperCAmelCase,      _UpperCAmelCase	)

          UpperCamelCase   :  Dict						     =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
          self.parent.assertTrue(diff < 1e-3,      msg=F"""Max diff is {diff}"""	)






       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		int:
          UpperCamelCase   :  str						     =     20
          UpperCamelCase   :  Optional[int]						     =     model_class_name(_UpperCAmelCase	)

          UpperCamelCase   :  Tuple						     =     model.encode(inputs_dict['input_ids']	)

          UpperCamelCase     ,					UpperCamelCase   :  str						     =     (
              inputs_dict['decoder_input_ids'],
              inputs_dict['decoder_attention_mask'],
          )

          UpperCamelCase   :  Optional[int]						     =     jnp.concatenate(
              [
                  decoder_attention_mask,
                  jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])	),
              ],      axis=-1,      )

          UpperCamelCase   :  str						     =     model.init_cache(decoder_input_ids.shape[0],      _UpperCAmelCase,      _UpperCAmelCase	)
          UpperCamelCase   :  str						     =     jnp.broadcast_to(
              jnp.arange(decoder_input_ids.shape[-1] - 1	)[None, :],      (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1),      )

          UpperCamelCase   :  Tuple						     =     model.decode(
              decoder_input_ids[:, :-1],      _UpperCAmelCase,      decoder_attention_mask=_UpperCAmelCase,      past_key_values=_UpperCAmelCase,      decoder_position_ids=_UpperCAmelCase,      )
          UpperCamelCase   :  Optional[Any]						     =     jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]],      dtype='i4'	)
          UpperCamelCase   :  Dict						     =     model.decode(
              decoder_input_ids[:, -1:],      _UpperCAmelCase,      past_key_values=outputs_cache.past_key_values,      decoder_attention_mask=_UpperCAmelCase,      decoder_position_ids=_UpperCAmelCase,      )

          UpperCamelCase   :  Optional[int]						     =     model.decode(_UpperCAmelCase,      _UpperCAmelCase,      decoder_attention_mask=_UpperCAmelCase	)

          UpperCamelCase   :  List[Any]						     =     np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])	)	)
          self.parent.assertTrue(diff < 1e-3,      msg=F"""Max diff is {diff}"""	)






@require_flax
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Any       					=  99
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Dict						     =     np.array(
              [
                  [71, 82, 18, 33, 46, 91, 2],
                  [68, 34, 26, 58, 30, 82, 2],
                  [5, 97, 17, 39, 94, 40, 2],
                  [76, 83, 94, 25, 70, 78, 2],
                  [87, 59, 41, 35, 48, 66, 2],
                  [55, 13, 16, 58, 5, 2, 1],  # note padding
                  [64, 27, 31, 51, 12, 75, 2],
                  [52, 64, 86, 17, 83, 39, 2],
                  [48, 61, 9, 24, 71, 82, 2],
                  [26, 1, 60, 48, 22, 13, 2],
                  [21, 5, 62, 28, 14, 76, 2],
                  [45, 98, 37, 86, 59, 48, 2],
                  [70, 70, 50, 9, 28, 0, 2],
              ],      dtype=np.intaa,      )

          UpperCamelCase   :  str						     =     input_ids.shape[0]
          UpperCamelCase   :  Dict						     =     BlenderbotSmallConfig(
              vocab_size=self.vocab_size,      d_model=24,      encoder_layers=2,      decoder_layers=2,      encoder_attention_heads=2,      decoder_attention_heads=2,      encoder_ffn_dim=32,      decoder_ffn_dim=32,      max_position_embeddings=48,      eos_token_id=2,      pad_token_id=1,      bos_token_id=0,      )
          return config, input_ids, batch_size
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase     ,					UpperCamelCase     ,					UpperCamelCase   :  int						     =     self._get_config_and_data()
          UpperCamelCase   :  str						     =     FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase	)
          UpperCamelCase   :  Dict						     =     lm_model(input_ids=_UpperCAmelCase	)
          UpperCamelCase   :  str						     =     (batch_size, input_ids.shape[1], config.vocab_size)
          self.assertEqual(outputs['logits'].shape,      _UpperCAmelCase	)
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  int						     =     BlenderbotSmallConfig(
              vocab_size=self.vocab_size,      d_model=14,      encoder_layers=2,      decoder_layers=2,      encoder_attention_heads=2,      decoder_attention_heads=2,      encoder_ffn_dim=8,      decoder_ffn_dim=8,      max_position_embeddings=48,      )
          UpperCamelCase   :  Optional[int]						     =     FlaxBlenderbotSmallForConditionalGeneration(_UpperCAmelCase	)
          UpperCamelCase   :  Union[str, Any]						     =     np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]],      dtype=np.intaa	)
          UpperCamelCase   :  Tuple						     =     np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]],      dtype=np.intaa	)
          UpperCamelCase   :  List[Any]						     =     lm_model(input_ids=_UpperCAmelCase,      decoder_input_ids=_UpperCAmelCase	)
          UpperCamelCase   :  Tuple						     =     (*summary.shape, config.vocab_size)
          self.assertEqual(outputs['logits'].shape,      _UpperCAmelCase	)






       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]],      dtype=np.intaa	)
          UpperCamelCase   :  int						     =     shift_tokens_right(_UpperCAmelCase,      1,      2	)
          UpperCamelCase   :  Union[str, Any]						     =     np.equal(_UpperCAmelCase,      1	).astype(np.floataa	).sum()
          UpperCamelCase   :  Optional[Any]						     =     np.equal(_UpperCAmelCase,      1	).astype(np.floataa	).sum()
          self.assertEqual(shifted.shape,      input_ids.shape	)
          self.assertEqual(_UpperCAmelCase,      n_pad_before - 1	)
          self.assertTrue(np.equal(shifted[:, 0],      2	).all()	)






@require_flax
class     lowerCAmelCase_		(  lowerCamelCase_	,   unittest.TestCase	,   lowerCamelCase_							):
       UpperCAmelCase__    : Tuple       					=  True
       UpperCAmelCase__    : Optional[int]       					=  (
           (
               FlaxBlenderbotSmallModel,
               FlaxBlenderbotSmallForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCAmelCase__    : Optional[Any]       					=  (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase   :  Union[str, Any]						     =     FlaxBlenderbotSmallModelTester(self	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase     ,					UpperCamelCase   :  Tuple						     =     self.model_tester.prepare_config_and_inputs()
          for model_class in self.all_model_classes:
             self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase,      _UpperCAmelCase,      _UpperCAmelCase	)
       def  snake_case_	(      self	)					->		Dict:
          UpperCamelCase     ,					UpperCamelCase   :  List[Any]						     =     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__	):
                UpperCamelCase   :  Tuple						     =     self._prepare_for_class(_UpperCAmelCase,      _UpperCAmelCase	)
                UpperCamelCase   :  Union[str, Any]						     =     model_class(_UpperCAmelCase	)

                @jax.jit
                def encode_jitted(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=None,      **SCREAMING_SNAKE_CASE_	):
                   return model.encode(input_ids=_UpperCAmelCase,      attention_mask=_UpperCAmelCase	)

                with self.subTest('JIT Enabled'	):
                   UpperCamelCase   :  str						     =     encode_jitted(**_UpperCAmelCase	).to_tuple()

                with self.subTest('JIT Disabled'	):
                   with jax.disable_jit():
                      UpperCamelCase   :  Any						     =     encode_jitted(**_UpperCAmelCase	).to_tuple()

                self.assertEqual(len(_UpperCAmelCase	),      len(_UpperCAmelCase	)	)
                for jitted_output, output in zip(_UpperCAmelCase,      _UpperCAmelCase	):
                   self.assertEqual(jitted_output.shape,      output.shape	)
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase     ,					UpperCamelCase   :  str						     =     self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
             with self.subTest(model_class.__name__	):
                UpperCamelCase   :  Optional[Any]						     =     model_class(_UpperCAmelCase	)
                UpperCamelCase   :  str						     =     model.encode(inputs_dict['input_ids'],      inputs_dict['attention_mask']	)

                UpperCamelCase   :  int						     =     {
                    'decoder_input_ids': inputs_dict['decoder_input_ids'],
                    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
                    'encoder_outputs': encoder_outputs,
                }

                @jax.jit
                def decode_jitted(SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	):
                   return model.decode(
                       decoder_input_ids=_UpperCAmelCase,      decoder_attention_mask=_UpperCAmelCase,      encoder_outputs=_UpperCAmelCase,      )

                with self.subTest('JIT Enabled'	):
                   UpperCamelCase   :  Union[str, Any]						     =     decode_jitted(**_UpperCAmelCase	).to_tuple()

                with self.subTest('JIT Disabled'	):
                   with jax.disable_jit():
                      UpperCamelCase   :  Dict						     =     decode_jitted(**_UpperCAmelCase	).to_tuple()

                self.assertEqual(len(_UpperCAmelCase	),      len(_UpperCAmelCase	)	)
                for jitted_output, output in zip(_UpperCAmelCase,      _UpperCAmelCase	):
                   self.assertEqual(jitted_output.shape,      output.shape	)






       @slow
       def  snake_case_	(      self	)					->		List[str]:






          for model_class_name in self.all_model_classes:
             UpperCamelCase   :  str						     =     model_class_name.from_pretrained('facebook/blenderbot_small-90M'	)
             # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
             UpperCamelCase   :  str						     =     np.ones((1, 1)	) * model.config.eos_token_id
             UpperCamelCase   :  Any						     =     model(_UpperCAmelCase	)
             self.assertIsNotNone(_UpperCAmelCase	)








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_	)



