
import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, 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 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       str	     =				ShapEImgaImgPipeline
		__lowercase       :       List[Any]	     =				["image"]
		__lowercase       :       Optional[Any]	     =				["image"]
		__lowercase       :       Tuple	     =				[
		    "num_images_per_prompt",
		    "num_inference_steps",
		    "generator",
		    "latents",
		    "guidance_scale",
		    "frame_size",
		    "output_type",
		    "return_dict",
		]
		__lowercase       :       str	     =				False



		@property
		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase ( self       )  ->					Any:
				"""simple docstring"""
				return 32



		@property
		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				return self.time_input_dim * 4



		@property
		def __UpperCamelCase ( self       )  ->					List[Any]:
				"""simple docstring"""
				return 8



		@property
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				torch.manual_seed(0       )
				UpperCamelCase		=     CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size				,      image_size=64				,      projection_dim=self.text_embedder_hidden_size				,      intermediate_size=37				,      num_attention_heads=4				,      num_channels=3				,      num_hidden_layers=5				,      patch_size=1				,      )

				UpperCamelCase		=     CLIPVisionModel(A_       )
				return model



		@property
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				UpperCamelCase		=     CLIPImageProcessor(
				    crop_size=224				,      do_center_crop=A_				,      do_normalize=A_				,      do_resize=A_				,      image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073]				,      image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711]				,      resample=3				,      size=224				,      )

				return image_processor



		@property
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				torch.manual_seed(0       )

				UpperCamelCase		=     {
				    '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',
				    'embedding_proj_norm_type': 'layer',
				    'encoder_hid_proj_type': None,
				    'added_emb_type': None,
				}

				UpperCamelCase		=     PriorTransformer(**A_       )
				return model



		@property
		def __UpperCamelCase ( self       )  ->					Union[str, Any]:
				"""simple docstring"""
				torch.manual_seed(0       )

				UpperCamelCase		=     {
				    '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		=     ShapERenderer(**A_       )
				return model



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     self.dummy_prior
				UpperCamelCase		=     self.dummy_image_encoder
				UpperCamelCase		=     self.dummy_image_processor
				UpperCamelCase		=     self.dummy_renderer

				UpperCamelCase		=     HeunDiscreteScheduler(
				    beta_schedule='exp'				,      num_train_timesteps=1_024				,      prediction_type='sample'				,      use_karras_sigmas=A_				,      clip_sample=A_				,      clip_sample_range=1.0				,      )
				UpperCamelCase		=     {
				    'prior': prior,
				    'image_encoder': image_encoder,
				    'image_processor': image_processor,
				    'renderer': renderer,
				    'scheduler': scheduler,
				}

				return components



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     floats_tensor((1, 3, 64, 64)				,      rng=random.Random(A_       )       ).to(A_       )

				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )
				UpperCamelCase		=     {
				    'image': input_image,
				    'generator': generator,
				    'num_inference_steps': 1,
				    'frame_size': 32,
				    'output_type': 'np',
				}
				return inputs



		def __UpperCamelCase ( self       )  ->					int:
				"""simple docstring"""
				UpperCamelCase		=     'cpu'

				UpperCamelCase		=     self.get_dummy_components()

				UpperCamelCase		=     self.pipeline_class(**A_       )
				UpperCamelCase		=     pipe.to(A_       )

				pipe.set_progress_bar_config(disable=A_       )

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

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

				UpperCamelCase		=     np.array(
				    [
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				        0.0003_9216,
				    ]       )

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



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
				self._test_inference_batch_consistent(batch_sizes=[1, 2]       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				UpperCamelCase		=     torch_device == 'cpu'
				UpperCamelCase		=     True
				self._test_inference_batch_single_identical(
				    batch_size=2				,      test_max_difference=A_				,      relax_max_difference=A_				,      )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     self.get_dummy_components()
				UpperCamelCase		=     self.pipeline_class(**A_       )
				UpperCamelCase		=     pipe.to(A_       )
				pipe.set_progress_bar_config(disable=A_       )

				UpperCamelCase		=     1
				UpperCamelCase		=     2

				UpperCamelCase		=     self.get_dummy_inputs(A_       )

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

				UpperCamelCase		=     pipe(**A_				,      num_images_per_prompt=A_       )[0]

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






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



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				# clean up the VRAM after each test
				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				UpperCamelCase		=     load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/shap_e/corgi.png'       )
				UpperCamelCase		=     load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/shap_e/test_shap_e_img2img_out.npy'       )
				UpperCamelCase		=     ShapEImgaImgPipeline.from_pretrained('openai/shap-e-img2img'       )
				UpperCamelCase		=     pipe.to(A_       )
				pipe.set_progress_bar_config(disable=A_       )

				UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(0       )

				UpperCamelCase		=     pipe(
				    A_				,      generator=A_				,      guidance_scale=3.0				,      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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCAmelCase					: Union[str, Any]      =       logging.get_logger(__name__)

_UpperCAmelCase					: List[str]      =       {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}


class 				lowercase					(		_SCREAMING_SNAKE_CASE   ):
		__lowercase       :       List[str]	     =				"gpt_neox"



		def __init__( self				,      A_=50_432				,      A_=6_144				,      A_=44				,      A_=64				,      A_=24_576				,      A_="gelu"				,      A_=0.25				,      A_=10_000				,      A_=0.0				,      A_=0.0				,      A_=0.1				,      A_=2_048				,      A_=0.02				,      A_=1e-5				,      A_=True				,      A_=0				,      A_=2				,      A_=False				,      A_=True				,      A_=None				,      **A_				,      )  ->					Tuple:
				"""simple docstring"""
				super().__init__(bos_token_id=A_				,      eos_token_id=A_				,      **A_       )
				UpperCamelCase		=     vocab_size
				UpperCamelCase		=     max_position_embeddings
				UpperCamelCase		=     hidden_size
				UpperCamelCase		=     num_hidden_layers
				UpperCamelCase		=     num_attention_heads
				UpperCamelCase		=     intermediate_size
				UpperCamelCase		=     hidden_act
				UpperCamelCase		=     rotary_pct
				UpperCamelCase		=     rotary_emb_base
				UpperCamelCase		=     attention_dropout
				UpperCamelCase		=     hidden_dropout
				UpperCamelCase		=     classifier_dropout
				UpperCamelCase		=     initializer_range
				UpperCamelCase		=     layer_norm_eps
				UpperCamelCase		=     use_cache
				UpperCamelCase		=     tie_word_embeddings
				UpperCamelCase		=     use_parallel_residual
				UpperCamelCase		=     rope_scaling
				self._rope_scaling_validation()

				if self.hidden_size % self.num_attention_heads != 0:
						raise ValueError(
						    'The hidden size is not divisble by the number of attention heads! Make sure to update them!'       )



		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				if self.rope_scaling is None:
						return

				if not isinstance(self.rope_scaling				,      A_       ) or len(self.rope_scaling       ) != 2:
						raise ValueError(
						    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
						    F'''got {self.rope_scaling}'''       )
				UpperCamelCase		=     self.rope_scaling.get('type'				,      A_       )
				UpperCamelCase		=     self.rope_scaling.get('factor'				,      A_       )
				if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
						raise ValueError(
						    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''       )
				if rope_scaling_factor is None or not isinstance(A_				,      A_       ) or rope_scaling_factor <= 1.0:
						raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''       )
