
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      (       __lowerCamelCase ,		unittest.TestCase					):
	UpperCamelCase_  :					Optional[Any]    		=	ShapEPipeline
	UpperCamelCase_  :					List[str]    		=	['prompt']
	UpperCamelCase_  :					Any    		=	['prompt']
	UpperCamelCase_  :					Optional[Any]    		=	[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	UpperCamelCase_  :					Dict    		=	False







	@property
	def  snake_case__     (     self      :Optional[int]			)		->      Tuple:







				"""simple docstring"""






				return 3_2







	@property
	def  snake_case__     (     self      :List[str]			)		->      List[str]:







				"""simple docstring"""






				return 3_2







	@property
	def  snake_case__     (     self      :Any			)		->      Optional[int]:







				"""simple docstring"""






				return self.time_input_dim * 4







	@property
	def  snake_case__     (     self      :Tuple			)		->      Tuple:







				"""simple docstring"""






				return 8







	@property
	def  snake_case__     (     self      :Optional[Any]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)
				return tokenizer







	@property
	def  snake_case__     (     self      :Dict			)		->      Tuple:







				"""simple docstring"""






				torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE     						= 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(lowercase			)







	@property
	def  snake_case__     (     self      :Tuple			)		->      Tuple:







				"""simple docstring"""






				torch.manual_seed(0			)

				SCREAMING_SNAKE_CASE     						= {
				    '''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,
				}

				SCREAMING_SNAKE_CASE     						= PriorTransformer(**lowercase			)
				return model







	@property
	def  snake_case__     (     self      :Tuple			)		->      Optional[Any]:







				"""simple docstring"""






				torch.manual_seed(0			)

				SCREAMING_SNAKE_CASE     						= {
				    '''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,
				    ),
				}
				SCREAMING_SNAKE_CASE     						= ShapERenderer(**lowercase			)
				return model







	def  snake_case__     (     self      :List[str]			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.dummy_prior
				SCREAMING_SNAKE_CASE     						= self.dummy_text_encoder
				SCREAMING_SNAKE_CASE     						= self.dummy_tokenizer
				SCREAMING_SNAKE_CASE     						= self.dummy_renderer

				SCREAMING_SNAKE_CASE     						= HeunDiscreteScheduler(
				    beta_schedule='''exp'''   ,     num_train_timesteps=1_0_2_4   ,     prediction_type='''sample'''   ,     use_karras_sigmas=lowercase   ,     clip_sample=lowercase   ,     clip_sample_range=1.0   ,     )
				SCREAMING_SNAKE_CASE     						= {
				    '''prior''': prior,
				    '''text_encoder''': text_encoder,
				    '''tokenizer''': tokenizer,
				    '''renderer''': renderer,
				    '''scheduler''': scheduler,
				}

				return components







	def  snake_case__     (     self      :Tuple   ,     lowercase      :List[str]   ,     lowercase      :Optional[Any]=0			)		->      str:







				"""simple docstring"""






				if str(lowercase			).startswith('''mps'''			):
							SCREAMING_SNAKE_CASE     						= torch.manual_seed(lowercase			)
				else:
							SCREAMING_SNAKE_CASE     						= torch.Generator(device=lowercase			).manual_seed(lowercase			)
				SCREAMING_SNAKE_CASE     						= {
				    '''prompt''': '''horse''',
				    '''generator''': generator,
				    '''num_inference_steps''': 1,
				    '''frame_size''': 3_2,
				    '''output_type''': '''np''',
				}
				return inputs







	def  snake_case__     (     self      :Optional[int]			)		->      Union[str, Any]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= '''cpu'''

				SCREAMING_SNAKE_CASE     						= self.get_dummy_components()

				SCREAMING_SNAKE_CASE     						= self.pipeline_class(**lowercase			)
				SCREAMING_SNAKE_CASE     						= pipe.to(lowercase			)

				pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= pipe(**self.get_dummy_inputs(lowercase			)			)
				SCREAMING_SNAKE_CASE     						= output.images[0]
				SCREAMING_SNAKE_CASE     						= image[0, -3:, -3:, -1]

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

				SCREAMING_SNAKE_CASE     						= 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      :Union[str, Any]			)		->      Dict:







				"""simple docstring"""






				self._test_inference_batch_consistent(batch_sizes=[1, 2]			)







	def  snake_case__     (     self      :List[str]			)		->      Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= torch_device == '''cpu'''
				SCREAMING_SNAKE_CASE     						= True

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







	def  snake_case__     (     self      :int			)		->      Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= self.get_dummy_components()
				SCREAMING_SNAKE_CASE     						= self.pipeline_class(**lowercase			)
				SCREAMING_SNAKE_CASE     						= pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= 1
				SCREAMING_SNAKE_CASE     						= 2

				SCREAMING_SNAKE_CASE     						= self.get_dummy_inputs(lowercase			)

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

				SCREAMING_SNAKE_CASE     						= pipe(**lowercase   ,     num_images_per_prompt=lowercase			)[0]

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





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







	def  snake_case__     (     self      :Union[str, Any]			)		->      List[str]:







				"""simple docstring"""






				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()







	def  snake_case__     (     self      :int			)		->      List[str]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE     						= load_numpy(
				    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
				    '''/shap_e/test_shap_e_np_out.npy'''			)
				SCREAMING_SNAKE_CASE     						= ShapEPipeline.from_pretrained('''openai/shap-e'''			)
				SCREAMING_SNAKE_CASE     						= pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				SCREAMING_SNAKE_CASE     						= torch.Generator(device=lowercase			).manual_seed(0			)

				SCREAMING_SNAKE_CASE     						= pipe(
				    '''a shark'''   ,     generator=lowercase   ,     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(lowercase   ,     lowercase			)

from typing import TYPE_CHECKING

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


__lowerCAmelCase					   =       {
    'configuration_deberta': ['DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DebertaConfig', 'DebertaOnnxConfig'],
    'tokenization_deberta': ['DebertaTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       ['DebertaTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'DebertaForMaskedLM',
							    'DebertaForQuestionAnswering',
							    'DebertaForSequenceClassification',
							    'DebertaForTokenClassification',
							    'DebertaModel',
							    'DebertaPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase					   =       [
							    'TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'TFDebertaForMaskedLM',
							    'TFDebertaForQuestionAnswering',
							    'TFDebertaForSequenceClassification',
							    'TFDebertaForTokenClassification',
							    'TFDebertaModel',
							    'TFDebertaPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
							from .tokenization_deberta import DebertaTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_deberta_fast import DebertaTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_deberta import (
														    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    DebertaForMaskedLM,
														    DebertaForQuestionAnswering,
														    DebertaForSequenceClassification,
														    DebertaForTokenClassification,
														    DebertaModel,
														    DebertaPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_deberta import (
														    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFDebertaForMaskedLM,
														    TFDebertaForQuestionAnswering,
														    TFDebertaForSequenceClassification,
														    TFDebertaForTokenClassification,
														    TFDebertaModel,
														    TFDebertaPreTrainedModel,
														)


else:
							import sys

							__lowerCAmelCase					   =       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)