



"""simple docstring"""





import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__lowerCAmelCase       :					Tuple      					=					logging.get_logger(__name__)
def 	__lowerCAmelCase						(		__UpperCamelCase      :			int=None					, __UpperCamelCase      :			Dict=None    ):


						'''simple docstring'''






						return field(default_factory=lambda: default					, metadata=__UpperCamelCase    )






@dataclass
class    _lowerCAmelCase    :





		"""simple docstring"""






		_lowerCamelCase			  =       list_field(
		    default=[]  ,				metadata={
		        '''help''': (
		            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
		            ''' of all available models'''
		        )
		    }  ,				)

		_lowerCamelCase			  =       list_field(
		    default=[8]  ,				metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}		)

		_lowerCamelCase			  =       list_field(
		    default=[8, 32, 128, 512]  ,				metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}  ,				)

		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}		)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Use FP16 to accelerate inference.'''}		)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Benchmark training of model'''}		)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Verbose memory tracing'''}		)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={
		        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
		    }  ,				)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Trace memory line by line'''}		)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Save result to a CSV file'''}		)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Save all print statements in a log file'''}		)
		_lowerCamelCase			  =       field(default=SCREAMING_SNAKE_CASE__  ,				metadata={'''help''': '''Whether to print environment information'''}		)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={
		        '''help''': (
		            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
		            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
		            ''' for debugging / testing and on TPU.'''
		        )
		    }  ,				)
		_lowerCamelCase			  =       field(
		    default=f'inference_time_{round(time()		)}.csv'  ,				metadata={'''help''': '''CSV filename used if saving time results to csv.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=f'inference_memory_{round(time()		)}.csv'  ,				metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=f'train_time_{round(time()		)}.csv'  ,				metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=f'train_memory_{round(time()		)}.csv'  ,				metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=f'env_info_{round(time()		)}.csv'  ,				metadata={'''help''': '''CSV filename used if saving environment information.'''}  ,				)
		_lowerCamelCase			  =       field(
		    default=f'log_{round(time()		)}.csv'  ,				metadata={'''help''': '''Log filename used if print statements are saved in log.'''}  ,				)
		_lowerCamelCase			  =       field(default=3  ,				metadata={'''help''': '''Times an experiment will be run.'''}		)
		_lowerCamelCase			  =       field(
		    default=SCREAMING_SNAKE_CASE__  ,				metadata={
		        '''help''': (
		            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
		            ''' model weights.'''
		        )
		    }  ,				)





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								warnings.warn(
								    f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
								    """ are deprecated in general and it is advised to use external Benchmarking libraries """
								    """ to benchmark Transformer models."""							,    _lowercase							,    )





		def    UpperCAmelCase__							(					self   ) ->						Tuple:







								'''simple docstring'''

								return json.dumps(dataclasses.asdict(self   )							,    indent=2   )





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







								'''simple docstring'''

								if len(self.models   ) <= 0:
														raise ValueError(
														    """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
														    """ bert-base-cased` or `args.models = ['bert-base-cased']."""   )
								return self.models





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







								'''simple docstring'''

								if not self.multi_process:
														return False
								elif self.is_tpu:
														logger.info("""Multiprocessing is currently not possible on TPU."""   )
														return False
								else:
														return True






"""simple docstring"""





import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase       :					Optional[Any]      					=					False



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

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





		@property
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return 1_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								return 1_2





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







								'''simple docstring'''

								return 3_2





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







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : List[Any]						=			VQModel(
								    block_out_channels=[3_2, 6_4]							,    in_channels=3							,    out_channels=3							,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,    latent_channels=3							,    num_vq_embeddings=self.num_embed							,    vq_embed_dim=3							,    )
								return model





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







								'''simple docstring'''

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





		@property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : Dict						=			CLIPTextConfig(
								    bos_token_id=0							,    eos_token_id=2							,    hidden_size=self.text_embedder_hidden_size							,    intermediate_size=3_7							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1_0_0_0							,    )
								return CLIPTextModel(_lowercase   )





		@property
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								torch.manual_seed(0   )

								snake_case_   : Union[str, Any]						=			1_2
								snake_case_   : Tuple						=			1_2

								snake_case_   : Tuple						=			{
								    """attention_bias""": True,
								    """cross_attention_dim""": 3_2,
								    """attention_head_dim""": height * width,
								    """num_attention_heads""": 1,
								    """num_vector_embeds""": self.num_embed,
								    """num_embeds_ada_norm""": self.num_embeds_ada_norm,
								    """norm_num_groups""": 3_2,
								    """sample_size""": width,
								    """activation_fn""": """geglu-approximate""",
								}

								snake_case_   : Optional[Any]						=			TransformeraDModel(**_lowercase   )
								return model





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : str						=			"""cpu"""

								snake_case_   : List[str]						=			self.dummy_vqvae
								snake_case_   : Any						=			self.dummy_text_encoder
								snake_case_   : Tuple						=			self.dummy_tokenizer
								snake_case_   : int						=			self.dummy_transformer
								snake_case_   : int						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : Dict						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase   )

								snake_case_   : Optional[Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : int						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : List[Any]						=			"""teddy bear playing in the pool"""

								snake_case_   : Dict						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : List[Any]						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Optional[int]						=			output.images

								snake_case_   : List[Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Dict						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

								snake_case_   : List[Any]						=			image[0, -3:, -3:, -1]
								snake_case_   : Any						=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : Dict						=			np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]   )

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





		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : int						=			"""cpu"""

								snake_case_   : List[Any]						=			self.dummy_vqvae
								snake_case_   : Optional[int]						=			self.dummy_text_encoder
								snake_case_   : List[Any]						=			self.dummy_tokenizer
								snake_case_   : Union[str, Any]						=			self.dummy_transformer
								snake_case_   : str						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(
								    learnable=_lowercase							,    hidden_size=self.text_embedder_hidden_size							,    length=tokenizer.model_max_length   )

								snake_case_   : Union[str, Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : Any						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : Tuple						=			"""teddy bear playing in the pool"""

								snake_case_   : str						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Tuple						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Dict						=			output.images

								snake_case_   : Union[str, Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Any						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

								snake_case_   : Optional[Any]						=			image[0, -3:, -3:, -1]
								snake_case_   : int						=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : int						=			np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2.0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2




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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

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





		def    UpperCAmelCase__							(					self   ) ->						List[str]:







								'''simple docstring'''

								snake_case_   : List[Any]						=			load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""   )

								snake_case_   : str						=			VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""   )
								snake_case_   : Optional[Any]						=			pipeline.to(_lowercase   )
								pipeline.set_progress_bar_config(disable=_lowercase   )

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								snake_case_   : Any						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Optional[int]						=			pipeline(
								    """teddy bear playing in the pool"""							,    num_images_per_prompt=1							,    generator=_lowercase							,    output_type="""np"""							,    )

								snake_case_   : Union[str, Any]						=			output.images[0]

								assert image.shape == (2_5_6, 2_5_6, 3)
								assert np.abs(expected_image - image   ).max() < 2.0

