

'''simple docstring'''

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class   UpperCamelCase__    (   UpperCAmelCase_   ):


		"""simple docstring"""

		def __init__( self    ,			snake_case    ,			snake_case    ,			snake_case=1_0_2_4    ,			snake_case=1_0_2_4    ,			snake_case=3.6			):


							'''simple docstring'''

							UpperCAmelCase		:							List[Any]   					=   tokenizer
							UpperCAmelCase		:							str   					=   tokenizer.bos_token_id
							UpperCAmelCase		:							Optional[Any]   					=   dataset
							UpperCAmelCase		:							Tuple   					=   seq_length
							UpperCAmelCase		:							str   					=   seq_length * chars_per_token * num_of_sequences







		def __iter__( self			):


							'''simple docstring'''

							UpperCAmelCase		:							Dict   					=   iter(self.dataset			)
							UpperCAmelCase		:							Union[str, Any]   					=   True
							while more_examples:
												UpperCAmelCase		:							Optional[int]   					=   [], 0
												while True:
																	if buffer_len >= self.input_characters:
																						break
																	try:
																						buffer.append(next(lowerCAmelCase__			)["content"]			)
																						buffer_len += len(buffer[-1]			)
																	except StopIteration:
																						UpperCAmelCase		:							str   					=   False
																						break
												UpperCAmelCase		:							str   					=   tokenizer(lowerCAmelCase__    ,			truncation=lowerCAmelCase__			)["input_ids"]
												UpperCAmelCase		:							Optional[Any]   					=   []
												for tokenized_input in tokenized_inputs:
																	all_token_ids.extend(tokenized_input + [self.concat_token_id]			)
												for i in range(0    ,			len(lowerCAmelCase__			)    ,			self.seq_length			):
																	UpperCAmelCase		:							Tuple   					=   all_token_ids[i : i + self.seq_length]
																	if len(lowerCAmelCase__			) == self.seq_length:
																						yield torch.tensor(lowerCAmelCase__			)





def       lowercase					(     __magic_name__							):



					'''simple docstring'''







					UpperCAmelCase		:							Any   					=   {"streaming": True}
					UpperCAmelCase		:							Any   					=   load_dataset(args.dataset_name ,    split="train" ,    **_UpperCAmelCase							)
					UpperCAmelCase		:							int   					=   ConstantLengthDataset(_UpperCAmelCase ,    _UpperCAmelCase ,    seq_length=args.seq_length							)
					UpperCAmelCase		:							Tuple   					=   DataLoader(_UpperCAmelCase ,    batch_size=args.batch_size							)
					return eval_dataloader





def       lowercase					(     __magic_name__							):



					'''simple docstring'''







					model.eval()
					UpperCAmelCase		:							Optional[Any]   					=   []
					for step, batch in enumerate(_UpperCAmelCase							):
										with torch.no_grad():
															UpperCAmelCase		:							Optional[int]   					=   model(_UpperCAmelCase ,    labels=_UpperCAmelCase							)
										UpperCAmelCase		:							int   					=   outputs.loss.repeat(args.batch_size							)
										losses.append(accelerator.gather(_UpperCAmelCase							)							)

										if args.max_eval_steps > 0 and step >= args.max_eval_steps:
															break
					UpperCAmelCase		:							Optional[int]   					=   torch.mean(torch.cat(_UpperCAmelCase							)							)
					try:
										UpperCAmelCase		:							Dict   					=   torch.exp(_UpperCAmelCase							)
					except OverflowError:
										UpperCAmelCase		:							Any   					=   float("inf"							)
					return loss.item(), perplexity.item()


# Setup Accelerator
a   :			Optional[Any]         = Accelerator()

# Parse configuration
a   :			List[str]         = HfArgumentParser(EvaluationArguments)
a   :			List[str]         = parser.parse_args()
set_seed(args.seed)

# Logging
a   :			List[str]         = logging.getLogger(__name__)
logging.basicConfig(
    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)

# Load model and tokenizer
a   :			Optional[Any]         = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a   :			Any         = AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a   :			Optional[Any]         = create_dataloader(args)

# Prepare everything with our `accelerator`.
a   :			List[str]         = accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
a   :			List[str]         = evaluate(args)




logger.info(F'loss/eval: {eval_loss}, perplexity: {perplexity}')



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   __lowercase				( UpperCAmelCase_		,       unittest.TestCase	):




			"""simple docstring"""







			_UpperCAmelCase   :							List[str]										=     ShapEPipeline
			_UpperCAmelCase   :							Tuple										=     ['''prompt''']
			_UpperCAmelCase   :							Dict										=     ['''prompt''']
			_UpperCAmelCase   :							Any										=     [
			    '''num_images_per_prompt''',
			    '''num_inference_steps''',
			    '''generator''',
			    '''latents''',
			    '''guidance_scale''',
			    '''frame_size''',
			    '''output_type''',
			    '''return_dict''',
			]
			_UpperCAmelCase   :							Optional[int]										=     False



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



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



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									return self.time_input_dim * 4



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]):
									return 8



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: str  		=       CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
									return tokenizer



			@property
			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]):
									torch.manual_seed(0)

									SCREAMING_SNAKE_CASE_: Tuple  		=       {
									    "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,
									}

									SCREAMING_SNAKE_CASE_: Any  		=       PriorTransformer(**lowerCAmelCase__)
									return model



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

									SCREAMING_SNAKE_CASE_: Union[str, 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,
									    ),
									}
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       ShapERenderer(**lowerCAmelCase__)
									return model



			def        _SCREAMING_SNAKE_CASE			( self  :     List[str]):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.dummy_prior
									SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.dummy_text_encoder
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       self.dummy_tokenizer
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.dummy_renderer

									SCREAMING_SNAKE_CASE_: Any  		=       HeunDiscreteScheduler(
									    beta_schedule="exp"   ,						num_train_timesteps=1024   ,						prediction_type="sample"   ,						use_karras_sigmas=lowerCAmelCase__   ,						clip_sample=lowerCAmelCase__   ,						clip_sample_range=1.0   ,						)
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       {
									    "prior": prior,
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "renderer": renderer,
									    "scheduler": scheduler,
									}

									return components



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



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       "cpu"

									SCREAMING_SNAKE_CASE_: Tuple  		=       self.get_dummy_components()

									SCREAMING_SNAKE_CASE_: Dict  		=       self.pipeline_class(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Any  		=       pipe.to(lowerCAmelCase__)

									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

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

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

									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       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        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]):
									# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
									self._test_inference_batch_consistent(batch_sizes=[1, 2])



			def        _SCREAMING_SNAKE_CASE			( self  :     Any):
									SCREAMING_SNAKE_CASE_: Dict  		=       torch_device == "cpu"
									SCREAMING_SNAKE_CASE_: List[Any]  		=       True

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




			def        _SCREAMING_SNAKE_CASE			( self  :     int):
									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_dummy_components()
									SCREAMING_SNAKE_CASE_: str  		=       self.pipeline_class(**lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: Tuple  		=       pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: List[Any]  		=       1
									SCREAMING_SNAKE_CASE_: Any  		=       2

									SCREAMING_SNAKE_CASE_: Dict  		=       self.get_dummy_inputs(lowerCAmelCase__)

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

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

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







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




			"""simple docstring"""







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




			def        _SCREAMING_SNAKE_CASE			( self  :     str):
									SCREAMING_SNAKE_CASE_: List[str]  		=       load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/shap_e/test_shap_e_np_out.npy")
									SCREAMING_SNAKE_CASE_: List[str]  		=       ShapEPipeline.from_pretrained("openai/shap-e")
									SCREAMING_SNAKE_CASE_: Optional[int]  		=       pipe.to(lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_: int  		=       torch.Generator(device=lowerCAmelCase__).manual_seed(0)

									SCREAMING_SNAKE_CASE_: int  		=       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__)






