



"""simple docstring"""




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_  (    _UpperCamelCase	,      unittest.TestCase						):
      __SCREAMING_SNAKE_CASE						:       Tuple								=			ShapEPipeline
      __SCREAMING_SNAKE_CASE						:       Dict								=			['prompt']
      __SCREAMING_SNAKE_CASE						:       int								=			['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 						snake_case_ (					self					:     Tuple						):
           return 3_2



      @property
      def 						snake_case_ (					self					:     Any						):
           return 3_2



      @property
      def 						snake_case_ (					self					:     List[Any]						):
           return self.time_input_dim * 4



      @property
      def 						snake_case_ (					self					:     Optional[int]						):
           return 8



      @property
      def 						snake_case_ (					self					:     Dict						):
           _UpperCAmelCase      :     Any        =   CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"						)
           return tokenizer



      @property
      def 						snake_case_ (					self					:     Optional[Any]						):
           torch.manual_seed(0						)
           _UpperCAmelCase      :     Any        =   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(A						)



      @property
      def 						snake_case_ (					self					:     Any						):
           torch.manual_seed(0						)

           _UpperCAmelCase      :     int        =   {
               "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,
           }

           _UpperCAmelCase      :     Tuple        =   PriorTransformer(**A						)
           return model



      @property
      def 						snake_case_ (					self					:     Dict						):
           torch.manual_seed(0						)

           _UpperCAmelCase      :     str        =   {
               "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,
               ),
           }
           _UpperCAmelCase      :     Tuple        =   ShapERenderer(**A						)
           return model



      def 						snake_case_ (					self					:     Union[str, Any]						):
           _UpperCAmelCase      :     Union[str, Any]        =   self.dummy_prior
           _UpperCAmelCase      :     Dict        =   self.dummy_text_encoder
           _UpperCAmelCase      :     int        =   self.dummy_tokenizer
           _UpperCAmelCase      :     List[Any]        =   self.dummy_renderer

           _UpperCAmelCase      :     Any        =   HeunDiscreteScheduler(
               beta_schedule="exp"					,     num_train_timesteps=1_0_2_4					,     prediction_type="sample"					,     use_karras_sigmas=A					,     clip_sample=A					,     clip_sample_range=1.0					,     )
           _UpperCAmelCase      :     int        =   {
               "prior": prior,
               "text_encoder": text_encoder,
               "tokenizer": tokenizer,
               "renderer": renderer,
               "scheduler": scheduler,
           }

           return components



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     List[Any]					,     A					:     Dict=0						):
           if str(A						).startswith("mps"						):
                _UpperCAmelCase      :     Union[str, Any]        =   torch.manual_seed(A						)
           else:
                _UpperCAmelCase      :     str        =   torch.Generator(device=A						).manual_seed(A						)
           _UpperCAmelCase      :     Any        =   {
               "prompt": "horse",
               "generator": generator,
               "num_inference_steps": 1,
               "frame_size": 3_2,
               "output_type": "np",
           }
           return inputs



      def 						snake_case_ (					self					:     int						):
           _UpperCAmelCase      :     Any        =   "cpu"

           _UpperCAmelCase      :     Dict        =   self.get_dummy_components()

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

           pipe.set_progress_bar_config(disable=A						)

           _UpperCAmelCase      :     str        =   pipe(**self.get_dummy_inputs(A						)						)
           _UpperCAmelCase      :     Dict        =   output.images[0]
           _UpperCAmelCase      :     Any        =   image[0, -3:, -3:, -1]

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

           _UpperCAmelCase      :     Dict        =   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 						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					:     List[str]						):
           _UpperCAmelCase      :     Union[str, Any]        =   torch_device == "cpu"
           _UpperCAmelCase      :     List[str]        =   True

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



      def 						snake_case_ (					self					:     List[Any]						):
           _UpperCAmelCase      :     int        =   self.get_dummy_components()
           _UpperCAmelCase      :     List[Any]        =   self.pipeline_class(**A						)
           _UpperCAmelCase      :     int        =   pipe.to(A						)
           pipe.set_progress_bar_config(disable=A						)

           _UpperCAmelCase      :     Any        =   1
           _UpperCAmelCase      :     Union[str, Any]        =   2

           _UpperCAmelCase      :     Dict        =   self.get_dummy_inputs(A						)

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

           _UpperCAmelCase      :     Any        =   pipe(**A					,     num_images_per_prompt=A						)[0]

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







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



      def 						snake_case_ (					self					:     Any						):
           # clean up the VRAM after each test
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()



      def 						snake_case_ (					self					:     Optional[int]						):
           _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      :     int        =   ShapEPipeline.from_pretrained("openai/shap-e"						)
           _UpperCAmelCase      :     Optional[Any]        =   pipe.to(A						)
           pipe.set_progress_bar_config(disable=A						)

           _UpperCAmelCase      :     Optional[Any]        =   torch.Generator(device=A						).manual_seed(0						)

           _UpperCAmelCase      :     int        =   pipe(
               "a shark"					,     generator=A					,     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(A					,     A						)





"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCAmelCase    : Union[str, Any]							=							{
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							["RoFormerTokenizerFast"]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Any							=							[
     "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "RoFormerForCausalLM",
     "RoFormerForMaskedLM",
     "RoFormerForMultipleChoice",
     "RoFormerForQuestionAnswering",
     "RoFormerForSequenceClassification",
     "RoFormerForTokenClassification",
     "RoFormerLayer",
     "RoFormerModel",
     "RoFormerPreTrainedModel",
     "load_tf_weights_in_roformer",
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : Optional[int]							=							[
     "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFRoFormerForCausalLM",
     "TFRoFormerForMaskedLM",
     "TFRoFormerForMultipleChoice",
     "TFRoFormerForQuestionAnswering",
     "TFRoFormerForSequenceClassification",
     "TFRoFormerForTokenClassification",
     "TFRoFormerLayer",
     "TFRoFormerModel",
     "TFRoFormerPreTrainedModel",
 ]


try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase    : List[str]							=							[
     "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaxRoFormerForMaskedLM",
     "FlaxRoFormerForMultipleChoice",
     "FlaxRoFormerForQuestionAnswering",
     "FlaxRoFormerForSequenceClassification",
     "FlaxRoFormerForTokenClassification",
     "FlaxRoFormerModel",
     "FlaxRoFormerPreTrainedModel",
 ]


if TYPE_CHECKING:
 from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
 from .tokenization_roformer import RoFormerTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_roformer_fast import RoFormerTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_roformer import (
      ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      RoFormerForCausalLM,
      RoFormerForMaskedLM,
      RoFormerForMultipleChoice,
      RoFormerForQuestionAnswering,
      RoFormerForSequenceClassification,
      RoFormerForTokenClassification,
      RoFormerLayer,
      RoFormerModel,
      RoFormerPreTrainedModel,
      load_tf_weights_in_roformer,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_roformer import (
      TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRoFormerForCausalLM,
      TFRoFormerForMaskedLM,
      TFRoFormerForMultipleChoice,
      TFRoFormerForQuestionAnswering,
      TFRoFormerForSequenceClassification,
      TFRoFormerForTokenClassification,
      TFRoFormerLayer,
      TFRoFormerModel,
      TFRoFormerPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_roformer import (
      FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaxRoFormerForMaskedLM,
      FlaxRoFormerForMultipleChoice,
      FlaxRoFormerForQuestionAnswering,
      FlaxRoFormerForSequenceClassification,
      FlaxRoFormerForTokenClassification,
      FlaxRoFormerModel,
      FlaxRoFormerPreTrainedModel,
  )


else:
 import sys

 _lowerCAmelCase    : List[str]							=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
