






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     (  _lowerCAmelCase ,      unittest.TestCase ):
	__snake_case						:Optional[int] =							ShapEPipeline
	__snake_case						:Optional[int] =							['prompt']
	__snake_case						:str =							['prompt']
	__snake_case						:Tuple =							[
	    'num_images_per_prompt',
	    'num_inference_steps',
	    'generator',
	    'latents',
	    'guidance_scale',
	    'frame_size',
	    'output_type',
	    'return_dict',
	]
	__snake_case						:str =							False







	@property
	def 			_a (	self   :				Optional[int]      )		->			Optional[Any]:
		"""simple docstring"""







		return 32







	@property
	def 			_a (	self   :				str      )		->			str:
		"""simple docstring"""







		return 32







	@property
	def 			_a (	self   :				Optional[Any]      )		->			Dict:
		"""simple docstring"""







		return self.time_input_dim * 4







	@property
	def 			_a (	self   :				str      )		->			Union[str, Any]:
		"""simple docstring"""







		return 8







	@property
	def 			_a (	self   :				Union[str, Any]      )		->			Optional[int]:
		"""simple docstring"""







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







	@property
	def 			_a (	self   :				int      )		->			Optional[int]:
		"""simple docstring"""







		torch.manual_seed(0      )
		__lowercase   							=	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(_lowerCAmelCase      )







	@property
	def 			_a (	self   :				Dict      )		->			List[str]:
		"""simple docstring"""







		torch.manual_seed(0      )

		__lowercase   							=	{
		    """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,
		}

		__lowercase   							=	PriorTransformer(**_lowerCAmelCase      )
		return model







	@property
	def 			_a (	self   :				Any      )		->			int:
		"""simple docstring"""







		torch.manual_seed(0      )

		__lowercase   							=	{
		    """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,
		    ),
		}
		__lowercase   							=	ShapERenderer(**_lowerCAmelCase      )
		return model







	def 			_a (	self   :				List[Any]      )		->			str:
		"""simple docstring"""







		__lowercase   							=	self.dummy_prior
		__lowercase   							=	self.dummy_text_encoder
		__lowercase   							=	self.dummy_tokenizer
		__lowercase   							=	self.dummy_renderer

		__lowercase   							=	HeunDiscreteScheduler(
		    beta_schedule="""exp"""							,					num_train_timesteps=1024							,					prediction_type="""sample"""							,					use_karras_sigmas=_lowerCAmelCase							,					clip_sample=_lowerCAmelCase							,					clip_sample_range=1.0							,					)
		__lowercase   							=	{
		    """prior""": prior,
		    """text_encoder""": text_encoder,
		    """tokenizer""": tokenizer,
		    """renderer""": renderer,
		    """scheduler""": scheduler,
		}

		return components







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Union[str, Any]=0      )		->			Dict:
		"""simple docstring"""







		if str(_lowerCAmelCase      ).startswith("""mps"""      ):
			__lowercase   							=	torch.manual_seed(_lowerCAmelCase      )
		else:
			__lowercase   							=	torch.Generator(device=_lowerCAmelCase      ).manual_seed(_lowerCAmelCase      )
		__lowercase   							=	{
		    """prompt""": """horse""",
		    """generator""": generator,
		    """num_inference_steps""": 1,
		    """frame_size""": 32,
		    """output_type""": """np""",
		}
		return inputs







	def 			_a (	self   :				str      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	"""cpu"""

		__lowercase   							=	self.get_dummy_components()

		__lowercase   							=	self.pipeline_class(**_lowerCAmelCase      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )

		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

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

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

		__lowercase   							=	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 			_a (	self   :				Union[str, Any]      )		->			int:
		"""simple docstring"""







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







	def 			_a (	self   :				Dict      )		->			int:
		"""simple docstring"""







		__lowercase   							=	torch_device == """cpu"""
		__lowercase   							=	True

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







	def 			_a (	self   :				Any      )		->			str:
		"""simple docstring"""







		__lowercase   							=	self.get_dummy_components()
		__lowercase   							=	self.pipeline_class(**_lowerCAmelCase      )
		__lowercase   							=	pipe.to(_lowerCAmelCase      )
		pipe.set_progress_bar_config(disable=_lowerCAmelCase      )

		__lowercase   							=	1
		__lowercase   							=	2

		__lowercase   							=	self.get_dummy_inputs(_lowerCAmelCase      )

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

		__lowercase   							=	pipe(**_lowerCAmelCase							,					num_images_per_prompt=_lowerCAmelCase      )[0]

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


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







	def 			_a (	self   :				Dict      )		->			List[str]:
		"""simple docstring"""







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







	def 			_a (	self   :				Any      )		->			Optional[Any]:
		"""simple docstring"""







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

		__lowercase   							=	torch.Generator(device=_lowerCAmelCase      ).manual_seed(0      )

		__lowercase   							=	pipe(
		    """a shark"""							,					generator=_lowerCAmelCase							,					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(_lowerCAmelCase							,					_lowerCAmelCase      )





import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


UpperCAmelCase   :    Optional[Any]     =		logging.getLogger(__name__)







@dataclass
class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Optional[float]			       =		field(
		    default=0.0			,							metadata={"help": "The label smoothing epsilon to apply (if not zero)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "Whether to SortishSamler or not."}  )
		UpperCamelCase     :					bool			       =		field(
		    default=a_			,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}  )
		UpperCamelCase     :					bool			       =		field(default=a_			,							metadata={"help": "whether to use adafactor"}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Encoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Decoder layer dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(default=a_			,							metadata={"help": "Dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[float]			       =		field(
		    default=a_			,							metadata={"help": "Attention dropout probability. Goes into model.config."}  )
		UpperCamelCase     :					Optional[str]			       =		field(
		    default="linear"			,							metadata={"help": f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()  )}'''}			,							)

