


"""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 snake_case      (  _UpperCamelCase     ,				unittest.TestCase):
		__UpperCamelCase           =			ShapEPipeline
		__UpperCamelCase           =			['prompt']
		__UpperCamelCase           =			['prompt']
		__UpperCamelCase           =			[
		    'num_images_per_prompt',
		    'num_inference_steps',
		    'generator',
		    'latents',
		    'guidance_scale',
		    'frame_size',
		    'output_type',
		    'return_dict',
		]
		__UpperCamelCase           =			False







		@property
		def  a_ (					self      :       int		)       ->						int:





						'''simple docstring'''







						return 32







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





						'''simple docstring'''







						return 32







		@property
		def  a_ (					self      :       Dict		)       ->						Union[str, Any]:





						'''simple docstring'''







						return self.time_input_dim * 4







		@property
		def  a_ (					self      :       Union[str, Any]		)       ->						List[str]:





						'''simple docstring'''







						return 8







		@property
		def  a_ (					self      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







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







		@property
		def  a_ (					self      :       Optional[Any]		)       ->						Dict:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			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=10_00    ,   )
						return CLIPTextModelWithProjection(a__		)







		@property
		def  a_ (					self      :       Dict		)       ->						str:





						'''simple docstring'''







						torch.manual_seed(0		)

						_A					  =			{
						    "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					  =			PriorTransformer(**a__		)
						return model







		@property
		def  a_ (					self      :       Any		)       ->						Union[str, Any]:





						'''simple docstring'''







						torch.manual_seed(0		)

						_A					  =			{
						    "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					  =			ShapERenderer(**a__		)
						return model







		def  a_ (					self      :       Any		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			self.dummy_prior
						_A					  =			self.dummy_text_encoder
						_A					  =			self.dummy_tokenizer
						_A					  =			self.dummy_renderer

						_A					  =			HeunDiscreteScheduler(
						    beta_schedule="exp"    ,   num_train_timesteps=10_24    ,   prediction_type="sample"    ,   use_karras_sigmas=a__    ,   clip_sample=a__    ,   clip_sample_range=1.0    ,   )
						_A					  =			{
						    "prior": prior,
						    "text_encoder": text_encoder,
						    "tokenizer": tokenizer,
						    "renderer": renderer,
						    "scheduler": scheduler,
						}

						return components







		def  a_ (					self      :       List[str]    ,   a__      :       str    ,   a__      :       Tuple=0		)       ->						Union[str, Any]:





						'''simple docstring'''







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







		def  a_ (					self      :       List[Any]		)       ->						str:





						'''simple docstring'''







						_A					  =			"cpu"

						_A					  =			self.get_dummy_components()

						_A					  =			self.pipeline_class(**a__		)
						_A					  =			pipe.to(a__		)

						pipe.set_progress_bar_config(disable=a__		)

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

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

						_A					  =			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  a_ (					self      :       Union[str, Any]		)       ->						Any:





						'''simple docstring'''







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







		def  a_ (					self      :       str		)       ->						Dict:





						'''simple docstring'''







						_A					  =			torch_device == "cpu"
						_A					  =			True

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







		def  a_ (					self      :       Any		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			self.get_dummy_components()
						_A					  =			self.pipeline_class(**a__		)
						_A					  =			pipe.to(a__		)
						pipe.set_progress_bar_config(disable=a__		)

						_A					  =			1
						_A					  =			2

						_A					  =			self.get_dummy_inputs(a__		)

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

						_A					  =			pipe(**a__    ,   num_images_per_prompt=a__		)[0]

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





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







		def  a_ (					self      :       Dict		)       ->						str:





						'''simple docstring'''







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







		def  a_ (					self      :       Any		)       ->						int:





						'''simple docstring'''







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

						_A					  =			torch.Generator(device=a__		).manual_seed(0		)

						_A					  =			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__		)



"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def      a__ (				__lowercase   )	->	Optional[int]:
				_A					  =			[
				    "encoder.version",
				    "decoder.version",
				    "model.encoder.version",
				    "model.decoder.version",
				    "_float_tensor",
				    "decoder.output_projection.weight",
				]
				for k in ignore_keys:
								state_dict.pop(__lowercase     ,			__lowercase   )






def      a__ (				__lowercase   )	->	List[Any]:
				_A  ,		_A					  =			emb.weight.shape
				_A					  =			nn.Linear(__lowercase     ,			__lowercase     ,			bias=__lowercase   )
				_A					  =			emb.weight.data
				return lin_layer






def      a__ (				__lowercase     ,			__lowercase="facebook/mbart-large-en-ro"     ,			__lowercase=False     ,			__lowercase=False   )	->	List[str]:
				_A					  =			torch.load(__lowercase     ,			map_location="cpu"   )["model"]
				remove_ignore_keys_(__lowercase   )
				_A					  =			state_dict["encoder.embed_tokens.weight"].shape[0]

				_A					  =			MBartConfig.from_pretrained(__lowercase     ,			vocab_size=__lowercase   )
				if mbart_aa and finetuned:
								_A					  =			"relu"

				_A					  =			state_dict["decoder.embed_tokens.weight"]
				_A					  =			MBartForConditionalGeneration(__lowercase   )
				model.model.load_state_dict(__lowercase   )

				if finetuned:
								_A					  =			make_linear_from_emb(model.model.shared   )

				return model


if __name__ == "__main__":
		a_       							=    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "fairseq_path", type=str, help="bart.large, bart.large.cnn or a path to a model.pt on local filesystem."
		)
		parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument(
		    "--hf_config",
		    default="facebook/mbart-large-cc25",
		    type=str,
		    help="Which huggingface architecture to use: mbart-large",
		)
		parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
		parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
		a_       							=    parser.parse_args()
		a_       							=    convert_fairseq_mbart_checkpoint_from_disk(
		    args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
		)
		model.save_pretrained(args.pytorch_dump_folder_path)