

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_      (	_lowercase						,      unittest.TestCase	):
	_lowercase		:   Any     					=				ShapEPipeline
	_lowercase		:   int     					=				['''prompt''']
	_lowercase		:   List[str]     					=				['''prompt''']
	_lowercase		:   Union[str, Any]     					=				[
	    '''num_images_per_prompt''',
	    '''num_inference_steps''',
	    '''generator''',
	    '''latents''',
	    '''guidance_scale''',
	    '''frame_size''',
	    '''output_type''',
	    '''return_dict''',
	]
	_lowercase		:   Optional[Any]     					=				False




	@property
	def        lowerCAmelCase_       (     self						:     List[str]							):
		return 32




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		return 32




	@property
	def        lowerCAmelCase_       (     self						:     Dict							):
		return self.time_input_dim * 4




	@property
	def        lowerCAmelCase_       (     self						:     str							):
		return 8




	@property
	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Tuple		    =			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""							)
		return tokenizer




	@property
	def        lowerCAmelCase_       (     self						:     str							):
		torch.manual_seed(0							)
		__A :			Tuple		    =			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-0_5					,      num_attention_heads=4					,      num_hidden_layers=5					,      pad_token_id=1					,      vocab_size=1000					,      )
		return CLIPTextModelWithProjection(__A							)




	@property
	def        lowerCAmelCase_       (     self						:     List[Any]							):
		torch.manual_seed(0							)

		__A :			Optional[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,
		}

		__A :			List[str]		    =			PriorTransformer(**__A							)
		return model




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

		__A :			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,
		    ),
		}
		__A :			Tuple		    =			ShapERenderer(**__A							)
		return model




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			Union[str, Any]		    =			self.dummy_prior
		__A :			List[Any]		    =			self.dummy_text_encoder
		__A :			Union[str, Any]		    =			self.dummy_tokenizer
		__A :			Tuple		    =			self.dummy_renderer

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

		return components




	def        lowerCAmelCase_       (     self						:     List[Any]					,      __A						:     str					,      __A						:     Any=0							):
		if str(__A							).startswith("""mps"""							):
			__A :			int		    =			torch.manual_seed(__A							)
		else:
			__A :			List[Any]		    =			torch.Generator(device=__A							).manual_seed(__A							)
		__A :			Optional[int]		    =			{
		    """prompt""": """horse""",
		    """generator""": generator,
		    """num_inference_steps""": 1,
		    """frame_size""": 32,
		    """output_type""": """np""",
		}
		return inputs




	def        lowerCAmelCase_       (     self						:     Optional[int]							):
		__A :			str		    =			"""cpu"""

		__A :			Tuple		    =			self.get_dummy_components()

		__A :			Optional[Any]		    =			self.pipeline_class(**__A							)
		__A :			List[str]		    =			pipe.to(__A							)

		pipe.set_progress_bar_config(disable=__A							)

		__A :			Tuple		    =			pipe(**self.get_dummy_inputs(__A							)							)
		__A :			Tuple		    =			output.images[0]
		__A :			Optional[Any]		    =			image[0, -3:, -3:, -1]

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

		__A :			List[Any]		    =			np.array(
		    [
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		        0.0_0_0_3_9_2_1_6,
		    ]							)

		assert np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2




	def        lowerCAmelCase_       (     self						:     Dict							):
		# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
		self._test_inference_batch_consistent(batch_sizes=[1, 2]							)




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			List[str]		    =			torch_device == """cpu"""
		__A :			str		    =			True

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




	def        lowerCAmelCase_       (     self						:     Dict							):
		__A :			List[Any]		    =			self.get_dummy_components()
		__A :			List[str]		    =			self.pipeline_class(**__A							)
		__A :			Any		    =			pipe.to(__A							)
		pipe.set_progress_bar_config(disable=__A							)

		__A :			List[Any]		    =			1
		__A :			int		    =			2

		__A :			int		    =			self.get_dummy_inputs(__A							)

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

		__A :			Tuple		    =			pipe(**__A					,      num_images_per_prompt=__A							)[0]

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






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




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




	def        lowerCAmelCase_       (     self						:     int							):
		__A :			Any		    =			load_numpy(
		    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
		    """/shap_e/test_shap_e_np_out.npy"""							)
		__A :			Union[str, Any]		    =			ShapEPipeline.from_pretrained("""openai/shap-e"""							)
		__A :			Any		    =			pipe.to(__A							)
		pipe.set_progress_bar_config(disable=__A							)

		__A :			int		    =			torch.Generator(device=__A							).manual_seed(0							)

		__A :			str		    =			pipe(
		    """a shark"""					,      generator=__A					,      guidance_scale=1_5.0					,      num_inference_steps=64					,      frame_size=64					,      output_type="""np"""					,      ).images[0]

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

		assert_mean_pixel_difference(__A					,      __A							)








from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   complex			,a__    :   str = "x"			,a__    :   float = 10**-10			,a__    :   int = 1			,)					->				complex:
	__A :			Tuple		    =			symbols(a__       )
	__A :			List[str]		    =			lambdify(a__			,a__       )
	__A :			Any		    =			lambdify(a__			,diff(a__			,a__       )       )

	__A :			Dict		    =			starting_point

	while True:
		if diff_function(a__       ) != 0:
			__A :			Optional[int]		    =			prev_guess - multiplicity * func(a__       ) / diff_function(
			    a__       )
		else:
			raise ZeroDivisionError("""Could not find root"""       ) from None

		# Precision is checked by comparing the difference of consecutive guesses
		if abs(next_guess - prev_guess       ) < precision:
			return next_guess

		__A :			List[Any]		    =			next_guess


# Let's Execute
if __name__ == "__main__":
			# Find root of trigonometric function
			# Find value of pi
			print(f"""The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}""")

			# Find root of polynomial
			# Find fourth Root of 5
			print(f"""The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5J)}""")

			# Find value of e
			print(
			    '''The root of log(y) - 1 = 0 is ''',
			    f"""{newton_raphson("log(y) - 1", 2, variable="y")}""",
			)

			# Exponential Roots
			print(
			    '''The root of exp(x) - 1 = 0 is''',
			    f"""{newton_raphson("exp(x) - 1", 10, precision=0.005)}""",
			)

			# Find root of cos(x)
			print(f"""The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}""")





