


"""simple docstring"""


import argparse
import json
import os
import pickle
import shutil

import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset

from transformers import (
    BertConfig,
    BertForMaskedLM,
    BertTokenizer,
    DistilBertConfig,
    DistilBertForMaskedLM,
    DistilBertTokenizer,
    GPTaConfig,
    GPTaLMHeadModel,
    GPTaTokenizer,
    RobertaConfig,
    RobertaForMaskedLM,
    RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed


__lowerCamelCase      :Optional[int]	    =		{
    'distilbert': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    'roberta': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    'bert': (BertConfig, BertForMaskedLM, BertTokenizer),
    'gpt2': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}




def   snake_case		(     UpperCamelCase__       :						Union[str, Any]    )   ->      Tuple:
      assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
      assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
      if args.mlm:
            assert os.path.isfile(args.token_counts    )
            assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
      else:
            assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])

      assert args.teacher_type == args.student_type or (
          args.student_type == "distilbert" and args.teacher_type == "bert"
      )
      assert os.path.isfile(args.student_config    )
      if args.student_pretrained_weights is not None:
            assert os.path.isfile(args.student_pretrained_weights    )

      if args.freeze_token_type_embds:
            assert args.student_type in ["roberta"]

      assert args.alpha_ce >= 0.0
      assert args.alpha_mlm >= 0.0
      assert args.alpha_clm >= 0.0
      assert args.alpha_mse >= 0.0
      assert args.alpha_cos >= 0.0
      assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0




def   snake_case		(     UpperCamelCase__       :						Optional[Any]			,				UpperCamelCase__       :						str    )   ->      List[str]:
      if args.student_type == "roberta":
            lowerCamelCase					:					List[Any]        =	False
      elif args.student_type == "gpt2":
            lowerCamelCase					:					Optional[int]        =	False




def   snake_case		(     UpperCamelCase__       :						List[Any]			,				UpperCamelCase__       :						str    )   ->      Optional[int]:
      if args.student_type == "roberta":
            lowerCamelCase					:					str        =	False




def   snake_case		(     )   ->      Optional[Any]:
      lowerCamelCase					:					Optional[Any]        =	argparse.ArgumentParser(description="""Training"""    )
      parser.add_argument("""--force"""			,				action="""store_true"""			,				help="""Overwrite dump_path if it already exists."""    )

      parser.add_argument(
          """--dump_path"""			,				type=UpperCamelCase__			,				required=UpperCamelCase__			,				help="""The output directory (log, checkpoints, parameters, etc.)"""    )
      parser.add_argument(
          """--data_file"""			,				type=UpperCamelCase__			,				required=UpperCamelCase__			,				help="""The binarized file (tokenized + tokens_to_ids) and grouped by sequence."""			,				)

      parser.add_argument(
          """--student_type"""			,				type=UpperCamelCase__			,				choices=["""distilbert""", """roberta""", """gpt2"""]			,				required=UpperCamelCase__			,				help="""The student type (DistilBERT, RoBERTa)."""			,				)
      parser.add_argument("""--student_config"""			,				type=UpperCamelCase__			,				required=UpperCamelCase__			,				help="""Path to the student configuration."""    )
      parser.add_argument(
          """--student_pretrained_weights"""			,				default=UpperCamelCase__			,				type=UpperCamelCase__			,				help="""Load student initialization checkpoint."""    )

      parser.add_argument(
          """--teacher_type"""			,				choices=["""bert""", """roberta""", """gpt2"""]			,				required=UpperCamelCase__			,				help="""Teacher type (BERT, RoBERTa)."""    )
      parser.add_argument("""--teacher_name"""			,				type=UpperCamelCase__			,				required=UpperCamelCase__			,				help="""The teacher model."""    )

      parser.add_argument("""--temperature"""			,				default=2.0			,				type=UpperCamelCase__			,				help="""Temperature for the softmax temperature."""    )
      parser.add_argument(
          """--alpha_ce"""			,				default=0.5			,				type=UpperCamelCase__			,				help="""Linear weight for the distillation loss. Must be >=0."""    )
      parser.add_argument(
          """--alpha_mlm"""			,				default=0.0			,				type=UpperCamelCase__			,				help="""Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag."""			,				)
      parser.add_argument("""--alpha_clm"""			,				default=0.5			,				type=UpperCamelCase__			,				help="""Linear weight for the CLM loss. Must be >=0."""    )
      parser.add_argument("""--alpha_mse"""			,				default=0.0			,				type=UpperCamelCase__			,				help="""Linear weight of the MSE loss. Must be >=0."""    )
      parser.add_argument(
          """--alpha_cos"""			,				default=0.0			,				type=UpperCamelCase__			,				help="""Linear weight of the cosine embedding loss. Must be >=0."""    )

      parser.add_argument(
          """--mlm"""			,				action="""store_true"""			,				help="""The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM."""    )
      parser.add_argument(
          """--mlm_mask_prop"""			,				default=0.1_5			,				type=UpperCamelCase__			,				help="""Proportion of tokens for which we need to make a prediction."""			,				)
      parser.add_argument("""--word_mask"""			,				default=0.8			,				type=UpperCamelCase__			,				help="""Proportion of tokens to mask out."""    )
      parser.add_argument("""--word_keep"""			,				default=0.1			,				type=UpperCamelCase__			,				help="""Proportion of tokens to keep."""    )
      parser.add_argument("""--word_rand"""			,				default=0.1			,				type=UpperCamelCase__			,				help="""Proportion of tokens to randomly replace."""    )
      parser.add_argument(
          """--mlm_smoothing"""			,				default=0.7			,				type=UpperCamelCase__			,				help="""Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)."""			,				)
      parser.add_argument("""--token_counts"""			,				type=UpperCamelCase__			,				help="""The token counts in the data_file for MLM."""    )

      parser.add_argument(
          """--restrict_ce_to_mask"""			,				action="""store_true"""			,				help="""If true, compute the distillation loss only the [MLM] prediction distribution."""			,				)
      parser.add_argument(
          """--freeze_pos_embs"""			,				action="""store_true"""			,				help="""Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only."""			,				)
      parser.add_argument(
          """--freeze_token_type_embds"""			,				action="""store_true"""			,				help="""Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only."""			,				)

      parser.add_argument("""--n_epoch"""			,				type=UpperCamelCase__			,				default=3			,				help="""Number of pass on the whole dataset."""    )
      parser.add_argument("""--batch_size"""			,				type=UpperCamelCase__			,				default=5			,				help="""Batch size (for each process)."""    )
      parser.add_argument(
          """--group_by_size"""			,				action="""store_false"""			,				help="""If true, group sequences that have similar length into the same batch. Default is true."""			,				)

      parser.add_argument(
          """--gradient_accumulation_steps"""			,				type=UpperCamelCase__			,				default=50			,				help="""Gradient accumulation for larger training batches."""			,				)
      parser.add_argument("""--warmup_prop"""			,				default=0.0_5			,				type=UpperCamelCase__			,				help="""Linear warmup proportion."""    )
      parser.add_argument("""--weight_decay"""			,				default=0.0			,				type=UpperCamelCase__			,				help="""Weight decay if we apply some."""    )
      parser.add_argument("""--learning_rate"""			,				default=5E-4			,				type=UpperCamelCase__			,				help="""The initial learning rate for Adam."""    )
      parser.add_argument("""--adam_epsilon"""			,				default=1E-6			,				type=UpperCamelCase__			,				help="""Epsilon for Adam optimizer."""    )
      parser.add_argument("""--max_grad_norm"""			,				default=5.0			,				type=UpperCamelCase__			,				help="""Max gradient norm."""    )
      parser.add_argument("""--initializer_range"""			,				default=0.0_2			,				type=UpperCamelCase__			,				help="""Random initialization range."""    )

      parser.add_argument(
          """--fp16"""			,				action="""store_true"""			,				help="""Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"""			,				)
      parser.add_argument(
          """--fp16_opt_level"""			,				type=UpperCamelCase__			,				default="""O1"""			,				help=(
              """For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."""
              """See details at https://nvidia.github.io/apex/amp.html"""
          )			,				)
      parser.add_argument("""--n_gpu"""			,				type=UpperCamelCase__			,				default=1			,				help="""Number of GPUs in the node."""    )
      parser.add_argument("""--local_rank"""			,				type=UpperCamelCase__			,				default=-1			,				help="""Distributed training - Local rank"""    )
      parser.add_argument("""--seed"""			,				type=UpperCamelCase__			,				default=56			,				help="""Random seed"""    )

      parser.add_argument("""--log_interval"""			,				type=UpperCamelCase__			,				default=500			,				help="""Tensorboard logging interval."""    )
      parser.add_argument("""--checkpoint_interval"""			,				type=UpperCamelCase__			,				default=4000			,				help="""Checkpoint interval."""    )
      lowerCamelCase					:					Union[str, Any]        =	parser.parse_args()
      sanity_checks(UpperCamelCase__    )

      # ARGS #
      init_gpu_params(UpperCamelCase__    )
      set_seed(UpperCamelCase__    )
      if args.is_master:
            if os.path.exists(args.dump_path    ):
                  if not args.force:
                        raise ValueError(
                            F'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
                            """ itUse `--force` if you want to overwrite it"""    )
                  else:
                        shutil.rmtree(args.dump_path    )

            if not os.path.exists(args.dump_path    ):
                  os.makedirs(args.dump_path    )
            logger.info(F'Experiment will be dumped and logged in {args.dump_path}'    )

            # SAVE PARAMS #
            logger.info(F'Param: {args}'    )
            with open(os.path.join(args.dump_path			,				"""parameters.json"""    )			,				"""w"""    ) as f:
                  json.dump(vars(UpperCamelCase__    )			,				UpperCamelCase__			,				indent=4    )
            git_log(args.dump_path    )

      lowerCamelCase					:					str        =	MODEL_CLASSES[args.student_type]
      lowerCamelCase					:					str        =	MODEL_CLASSES[args.teacher_type]

      # TOKENIZER #
      lowerCamelCase					:					Union[str, Any]        =	teacher_tokenizer_class.from_pretrained(args.teacher_name    )
      lowerCamelCase					:					List[Any]        =	{}
      for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
            lowerCamelCase					:					Union[str, Any]        =	tokenizer.all_special_tokens.index(UpperCamelCase__    )
            lowerCamelCase					:					int        =	tokenizer.all_special_ids[idx]
      logger.info(F'Special tokens {special_tok_ids}'    )
      lowerCamelCase					:					Any        =	special_tok_ids
      lowerCamelCase					:					int        =	tokenizer.max_model_input_sizes[args.teacher_name]

      # DATA LOADER #
      logger.info(F'Loading data from {args.data_file}'    )
      with open(args.data_file			,				"""rb"""    ) as fp:
            lowerCamelCase					:					Optional[int]        =	pickle.load(UpperCamelCase__    )

      if args.mlm:
            logger.info(F'Loading token counts from {args.token_counts} (already pre-computed)'    )
            with open(args.token_counts			,				"""rb"""    ) as fp:
                  lowerCamelCase					:					str        =	pickle.load(UpperCamelCase__    )

            lowerCamelCase					:					List[Any]        =	np.maximum(UpperCamelCase__			,				1    ) ** -args.mlm_smoothing
            for idx in special_tok_ids.values():
                  lowerCamelCase					:					Union[str, Any]        =	0.0  # do not predict special tokens
            lowerCamelCase					:					List[str]        =	torch.from_numpy(UpperCamelCase__    )
      else:
            lowerCamelCase					:					Union[str, Any]        =	None

      lowerCamelCase					:					int        =	LmSeqsDataset(params=UpperCamelCase__			,				data=UpperCamelCase__    )
      logger.info("""Data loader created."""    )

      # STUDENT #
      logger.info(F'Loading student config from {args.student_config}'    )
      lowerCamelCase					:					List[Any]        =	student_config_class.from_pretrained(args.student_config    )
      lowerCamelCase					:					Union[str, Any]        =	True

      if args.student_pretrained_weights is not None:
            logger.info(F'Loading pretrained weights from {args.student_pretrained_weights}'    )
            lowerCamelCase					:					str        =	student_model_class.from_pretrained(args.student_pretrained_weights			,				config=UpperCamelCase__    )
      else:
            lowerCamelCase					:					Optional[Any]        =	student_model_class(UpperCamelCase__    )

      if args.n_gpu > 0:
            student.to(F'cuda:{args.local_rank}'    )
      logger.info("""Student loaded."""    )

      # TEACHER #
      lowerCamelCase					:					str        =	teacher_model_class.from_pretrained(args.teacher_name			,				output_hidden_states=UpperCamelCase__    )
      if args.n_gpu > 0:
            teacher.to(F'cuda:{args.local_rank}'    )
      logger.info(F'Teacher loaded from {args.teacher_name}.'    )

      # FREEZING #
      if args.freeze_pos_embs:
            freeze_pos_embeddings(UpperCamelCase__			,				UpperCamelCase__    )
      if args.freeze_token_type_embds:
            freeze_token_type_embeddings(UpperCamelCase__			,				UpperCamelCase__    )

      # SANITY CHECKS #
      assert student.config.vocab_size == teacher.config.vocab_size
      assert student.config.hidden_size == teacher.config.hidden_size
      assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
      if args.mlm:
            assert token_probs.size(0    ) == stu_architecture_config.vocab_size

      # DISTILLER #
      torch.cuda.empty_cache()
      lowerCamelCase					:					Dict        =	Distiller(
          params=UpperCamelCase__			,				dataset=UpperCamelCase__			,				token_probs=UpperCamelCase__			,				student=UpperCamelCase__			,				teacher=UpperCamelCase__    )
      distiller.train()
      logger.info("""Let's go get some drinks."""    )


if __name__ == "__main__":
    main()









"""simple docstring"""


import gc
import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			A__	(						__lowercase ,     __lowercase ,     unittest.TestCase):


     """simple docstring"""
     snake_case__	:					str													=StableDiffusionXLImgaImgPipeline
     snake_case__	:					Any													=TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
     snake_case__	:					Optional[int]													=PipelineTesterMixin.required_optional_params - {'''latents'''}
     snake_case__	:					Dict													=TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     snake_case__	:					Tuple													=IMAGE_TO_IMAGE_IMAGE_PARAMS
     snake_case__	:					List[str]													=IMAGE_TO_IMAGE_IMAGE_PARAMS

     def 		a__						(	self:						List[str]	)->    int:
           torch.manual_seed(0	)
           lowerCamelCase					:					Any        =	UNetaDConditionModel(
               block_out_channels=(32, 64)     ,		layers_per_block=2     ,		sample_size=32     ,		in_channels=4     ,		out_channels=4     ,		down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")     ,		up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")     ,		attention_head_dim=(2, 4)     ,		use_linear_projection=__a     ,		addition_embed_type="""text_time"""     ,		addition_time_embed_dim=8     ,		transformer_layers_per_block=(1, 2)     ,		projection_class_embeddings_input_dim=80     ,		cross_attention_dim=64     ,		)
           lowerCamelCase					:					Any        =	EulerDiscreteScheduler(
               beta_start=0.0_00_85     ,		beta_end=0.0_12     ,		steps_offset=1     ,		beta_schedule="""scaled_linear"""     ,		timestep_spacing="""leading"""     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					Any        =	AutoencoderKL(
               block_out_channels=[32, 64]     ,		in_channels=3     ,		out_channels=3     ,		down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]     ,		up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]     ,		latent_channels=4     ,		sample_size=128     ,		)
           torch.manual_seed(0	)
           lowerCamelCase					:					Optional[Any]        =	CLIPTextConfig(
               bos_token_id=0     ,		eos_token_id=2     ,		hidden_size=32     ,		intermediate_size=37     ,		layer_norm_eps=1e-0_5     ,		num_attention_heads=4     ,		num_hidden_layers=5     ,		pad_token_id=1     ,		vocab_size=1_000     ,		hidden_act="""gelu"""     ,		projection_dim=32     ,		)
           lowerCamelCase					:					Dict        =	CLIPTextModel(__a	)
           lowerCamelCase					:					Union[str, Any]        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,		local_files_only=__a	)

           lowerCamelCase					:					Dict        =	CLIPTextModelWithProjection(__a	)
           lowerCamelCase					:					Optional[int]        =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     ,		local_files_only=__a	)

           lowerCamelCase					:					str        =	{
               """unet""": unet,
               """scheduler""": scheduler,
               """vae""": vae,
               """text_encoder""": text_encoder,
               """tokenizer""": tokenizer,
               """text_encoder_2""": text_encoder_a,
               """tokenizer_2""": tokenizer_a,
               # "safety_checker": None,
               # "feature_extractor": None,
           }
           return components

     def 		a__						(	self:						Any     ,		__a:						str     ,		__a:						Tuple=0	)->    Union[str, Any]:
           lowerCamelCase					:					List[Any]        =	floats_tensor((1, 3, 32, 32)     ,		rng=random.Random(__a	)	).to(__a	)
           lowerCamelCase					:					Any        =	image / 2 + 0.5
           if str(__a	).startswith("""mps"""	):
                 lowerCamelCase					:					Dict        =	torch.manual_seed(__a	)
           else:
                 lowerCamelCase					:					Tuple        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					Tuple        =	{
               """prompt""": """A painting of a squirrel eating a burger""",
               """image""": image,
               """generator""": generator,
               """num_inference_steps""": 2,
               """guidance_scale""": 5.0,
               """output_type""": """numpy""",
               """strength""": 0.75,
           }
           return inputs

     def 		a__						(	self:						Dict	)->    Optional[Any]:
           lowerCamelCase					:					Any        =	"""cpu"""  # ensure determinism for the device-dependent torch.Generator
           lowerCamelCase					:					Union[str, Any]        =	self.get_dummy_components()
           lowerCamelCase					:					Optional[int]        =	StableDiffusionXLImgaImgPipeline(**__a	)
           lowerCamelCase					:					int        =	sd_pipe.to(__a	)
           sd_pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Optional[Any]        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Optional[int]        =	sd_pipe(**__a	).images
           lowerCamelCase					:					Tuple        =	image[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)

           lowerCamelCase					:					Any        =	np.array([0.46_56, 0.48_40, 0.44_39, 0.66_98, 0.55_74, 0.45_24, 0.57_99, 0.59_43, 0.51_65]	)

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

     def 		a__						(	self:						Optional[int]	)->    Union[str, Any]:
           super().test_attention_slicing_forward_pass(expected_max_diff=3e-3	)

     def 		a__						(	self:						Optional[Any]	)->    str:
           super().test_inference_batch_single_identical(expected_max_diff=3e-3	)

     def 		a__						(	self:						List[str]	)->    Optional[Any]:
           pass







     def 		a__						(	self:						List[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Tuple        =	self.get_dummy_components()
           lowerCamelCase					:					Union[str, Any]        =	StableDiffusionXLImgaImgPipeline(**__a	)
           lowerCamelCase					:					str        =	sd_pipe.to(__a	)
           lowerCamelCase					:					Any        =	sd_pipe.to(__a	)
           sd_pipe.set_progress_bar_config(disable=__a	)

           # forward without prompt embeds
           lowerCamelCase					:					Dict        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					Any        =	3 * ["""this is a negative prompt"""]
           lowerCamelCase					:					Optional[int]        =	negative_prompt
           lowerCamelCase					:					Tuple        =	3 * [inputs["""prompt"""]]

           lowerCamelCase					:					List[Any]        =	sd_pipe(**__a	)
           lowerCamelCase					:					Optional[int]        =	output.images[0, -3:, -3:, -1]

           # forward with prompt embeds
           lowerCamelCase					:					Tuple        =	self.get_dummy_inputs(__a	)
           lowerCamelCase					:					List[Any]        =	3 * ["""this is a negative prompt"""]
           lowerCamelCase					:					Tuple        =	3 * [inputs.pop("""prompt"""	)]

           (
               (
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			(
               lowerCamelCase
           )     ,			
           )					:					Union[str, Any]        =	sd_pipe.encode_prompt(__a     ,		negative_prompt=__a	)

           lowerCamelCase					:					int        =	sd_pipe(
               **__a     ,		prompt_embeds=__a     ,		negative_prompt_embeds=__a     ,		pooled_prompt_embeds=__a     ,		negative_pooled_prompt_embeds=__a     ,		)
           lowerCamelCase					:					Union[str, Any]        =	output.images[0, -3:, -3:, -1]

           # make sure that it's equal
           assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()	).max() < 1e-4


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


     """simple docstring"""

     def 		a__						(	self:						Dict	)->    str:
           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()

     def 		a__						(	self:						Union[str, Any]     ,		__a:						Any     ,		__a:						Any="cpu"     ,		__a:						str=torch.floataa     ,		__a:						Any=0	)->    Optional[Any]:
           lowerCamelCase					:					Optional[Any]        =	torch.Generator(device=__a	).manual_seed(__a	)
           lowerCamelCase					:					List[Any]        =	np.random.RandomState(__a	).standard_normal((1, 4, 64, 64)	)
           lowerCamelCase					:					List[str]        =	torch.from_numpy(__a	).to(device=__a     ,		dtype=__a	)
           lowerCamelCase					:					int        =	{
               """prompt""": """a photograph of an astronaut riding a horse""",
               """latents""": latents,
               """generator""": generator,
               """num_inference_steps""": 3,
               """guidance_scale""": 7.5,
               """output_type""": """numpy""",
           }
           return inputs







     def 		a__						(	self:						Optional[int]	)->    List[str]:
           lowerCamelCase					:					Tuple        =	DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""	)
           pipe.to(__a	)
           pipe.set_progress_bar_config(disable=__a	)

           lowerCamelCase					:					Optional[int]        =	self.get_inputs(__a	)
           lowerCamelCase					:					Optional[Any]        =	pipe(**__a	).images
           lowerCamelCase					:					Dict        =	image[0, -3:, -3:, -1].flatten()

           assert image.shape == (1, 512, 512, 3)
           lowerCamelCase					:					List[str]        =	np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]	)
           assert np.abs(image_slice - expected_slice	).max() < 7e-3




