"""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


__SCREAMING_SNAKE_CASE								={
    "distilbert": (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
    "roberta": (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
    "bert": (BertConfig, BertForMaskedLM, BertTokenizer),
    "gpt2": (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}






def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     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     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]   ):
     if args.student_type == "roberta":
          lowercase_			:       Union[str, Any]									=			False
     elif args.student_type == "gpt2":
          lowercase_			:       Optional[int]									=			False






def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]   ):
     if args.student_type == "roberta":
          lowercase_			:       Union[str, Any]									=			False






def     lowercase__( ):
     lowercase_			:       List[str]									=			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=SCREAMING_SNAKE_CASE_  ,    required=SCREAMING_SNAKE_CASE_  ,    help='The output directory (log, checkpoints, parameters, etc.)'   )
     parser.add_argument(
         '--data_file'  ,    type=SCREAMING_SNAKE_CASE_  ,    required=SCREAMING_SNAKE_CASE_  ,    help='The binarized file (tokenized + tokens_to_ids) and grouped by sequence.'  ,    )

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

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

     parser.add_argument('--temperature'  ,    default=2.0  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Temperature for the softmax temperature.'   )
     parser.add_argument(
         '--alpha_ce'  ,    default=0.5  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Linear weight for the distillation loss. Must be >=0.'   )
     parser.add_argument(
         '--alpha_mlm'  ,    default=0.0  ,    type=SCREAMING_SNAKE_CASE_  ,    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=SCREAMING_SNAKE_CASE_  ,    help='Linear weight for the CLM loss. Must be >=0.'   )
     parser.add_argument('--alpha_mse'  ,    default=0.0  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Linear weight of the MSE loss. Must be >=0.'   )
     parser.add_argument(
         '--alpha_cos'  ,    default=0.0  ,    type=SCREAMING_SNAKE_CASE_  ,    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=0.15  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Proportion of tokens for which we need to make a prediction.'  ,    )
     parser.add_argument('--word_mask'  ,    default=0.8  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Proportion of tokens to mask out.'   )
     parser.add_argument('--word_keep'  ,    default=0.1  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Proportion of tokens to keep.'   )
     parser.add_argument('--word_rand'  ,    default=0.1  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Proportion of tokens to randomly replace.'   )
     parser.add_argument(
         '--mlm_smoothing'  ,    default=0.7  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec).'  ,    )
     parser.add_argument('--token_counts'  ,    type=SCREAMING_SNAKE_CASE_  ,    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=SCREAMING_SNAKE_CASE_  ,    default=3  ,    help='Number of pass on the whole dataset.'   )
     parser.add_argument('--batch_size'  ,    type=SCREAMING_SNAKE_CASE_  ,    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=SCREAMING_SNAKE_CASE_  ,    default=50  ,    help='Gradient accumulation for larger training batches.'  ,    )
     parser.add_argument('--warmup_prop'  ,    default=0.05  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Linear warmup proportion.'   )
     parser.add_argument('--weight_decay'  ,    default=0.0  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Weight decay if we apply some.'   )
     parser.add_argument('--learning_rate'  ,    default=5E-4  ,    type=SCREAMING_SNAKE_CASE_  ,    help='The initial learning rate for Adam.'   )
     parser.add_argument('--adam_epsilon'  ,    default=1E-6  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Epsilon for Adam optimizer.'   )
     parser.add_argument('--max_grad_norm'  ,    default=5.0  ,    type=SCREAMING_SNAKE_CASE_  ,    help='Max gradient norm.'   )
     parser.add_argument('--initializer_range'  ,    default=0.02  ,    type=SCREAMING_SNAKE_CASE_  ,    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=SCREAMING_SNAKE_CASE_  ,    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=SCREAMING_SNAKE_CASE_  ,    default=1  ,    help='Number of GPUs in the node.'   )
     parser.add_argument('--local_rank'  ,    type=SCREAMING_SNAKE_CASE_  ,    default=-1  ,    help='Distributed training - Local rank'   )
     parser.add_argument('--seed'  ,    type=SCREAMING_SNAKE_CASE_  ,    default=56  ,    help='Random seed'   )

     parser.add_argument('--log_interval'  ,    type=SCREAMING_SNAKE_CASE_  ,    default=5_00  ,    help='Tensorboard logging interval.'   )
     parser.add_argument('--checkpoint_interval'  ,    type=SCREAMING_SNAKE_CASE_  ,    default=40_00  ,    help='Checkpoint interval.'   )
     lowercase_			:       Tuple									=			parser.parse_args()
     sanity_checks(SCREAMING_SNAKE_CASE_   )

     # ARGS #
     init_gpu_params(SCREAMING_SNAKE_CASE_   )
     set_seed(SCREAMING_SNAKE_CASE_   )
     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(SCREAMING_SNAKE_CASE_   )  ,    SCREAMING_SNAKE_CASE_  ,    indent=4   )
          git_log(args.dump_path   )

     lowercase_							,						lowercase_							,						lowercase_			:       str									=			MODEL_CLASSES[args.student_type]
     lowercase_							,						lowercase_							,						lowercase_			:       Optional[Any]									=			MODEL_CLASSES[args.teacher_type]

     # TOKENIZER #
     lowercase_			:       List[Any]									=			teacher_tokenizer_class.from_pretrained(args.teacher_name   )
     lowercase_			:       Tuple									=			{}
     for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
          lowercase_			:       List[str]									=			tokenizer.all_special_tokens.index(SCREAMING_SNAKE_CASE_   )
          lowercase_			:       Dict									=			tokenizer.all_special_ids[idx]
     logger.info(F'''Special tokens {special_tok_ids}'''   )
     lowercase_			:       Union[str, Any]									=			special_tok_ids
     lowercase_			:       Any									=			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:
          lowercase_			:       Optional[Any]									=			pickle.load(SCREAMING_SNAKE_CASE_   )

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

          lowercase_			:       Dict									=			np.maximum(SCREAMING_SNAKE_CASE_  ,    1   ) ** -args.mlm_smoothing
          for idx in special_tok_ids.values():
               lowercase_			:       Dict									=			0.0  # do not predict special tokens
          lowercase_			:       Dict									=			torch.from_numpy(SCREAMING_SNAKE_CASE_   )
     else:
          lowercase_			:       Tuple									=			None

     lowercase_			:       Union[str, Any]									=			LmSeqsDataset(params=SCREAMING_SNAKE_CASE_  ,    data=SCREAMING_SNAKE_CASE_   )
     logger.info('Data loader created.'   )

     # STUDENT #
     logger.info(F'''Loading student config from {args.student_config}'''   )
     lowercase_			:       int									=			student_config_class.from_pretrained(args.student_config   )
     lowercase_			:       Dict									=			True

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

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

     # TEACHER #
     lowercase_			:       Union[str, Any]									=			teacher_model_class.from_pretrained(args.teacher_name  ,    output_hidden_states=SCREAMING_SNAKE_CASE_   )
     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(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   )
     if args.freeze_token_type_embds:
          freeze_token_type_embeddings(SCREAMING_SNAKE_CASE_  ,    SCREAMING_SNAKE_CASE_   )

     # 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()
     lowercase_			:       Any									=			Distiller(
         params=SCREAMING_SNAKE_CASE_  ,    dataset=SCREAMING_SNAKE_CASE_  ,    token_probs=SCREAMING_SNAKE_CASE_  ,    student=SCREAMING_SNAKE_CASE_  ,    teacher=SCREAMING_SNAKE_CASE_   )
     distiller.train()
     logger.info('Let\'s go get some drinks.'   )


if __name__ == "__main__":
       main()


"""simple docstring"""







import os
import tempfile
import unittest

import numpy as np

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard

       from diffusers import FlaxDDIMScheduler, FlaxDiffusionPipeline, FlaxStableDiffusionPipeline







@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        with tempfile.TemporaryDirectory() as tmpdirname:
             # pipeline has Flax weights
             lowercase_			:       Optional[int]									=			FlaxDiffusionPipeline.from_pretrained(
                 'hf-internal-testing/tiny-stable-diffusion-pipe'  ,safety_checker=__UpperCamelCase  ,cache_dir=__UpperCamelCase    )

             lowercase_			:       Dict									=			[t[-1] for t in os.walk(os.path.join(__UpperCamelCase  ,os.listdir(__UpperCamelCase    )[0]  ,'snapshots'    )    )]
             lowercase_			:       Tuple									=			[item for sublist in all_root_files for item in sublist]

             # None of the downloaded files should be a PyTorch file even if we have some here:
             # https://huggingface.co/hf-internal-testing/tiny-stable-diffusion-pipe/blob/main/unet/diffusion_pytorch_model.bin
             assert not any(f.endswith('.bin'    ) for f in files    )







@slow
@require_flax
class  UpperCamelCase	(    unittest.TestCase						):
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       List[Any]									=			FlaxStableDiffusionPipeline.from_pretrained(
            'hf-internal-testing/tiny-stable-diffusion-pipe'  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       Dict									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Optional[Any]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Optional[int]									=			4

        lowercase_			:       int									=			jax.device_count()
        lowercase_			:       Optional[int]									=			num_samples * [prompt]
        lowercase_			:       List[Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       Dict									=			replicate(__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Dict									=			shard(__UpperCamelCase    )

        lowercase_			:       List[Any]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 64, 64, 3)
        if jax.device_count() == 8:
             assert np.abs(np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 4.151_4745    ) < 1e-3
             assert np.abs(np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 4_9947.875    ) < 5e-1

        lowercase_			:       List[Any]									=			pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]    )    )    )
        assert len(__UpperCamelCase    ) == num_samples
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       Any									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='flax'  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       Any									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       List[str]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Tuple									=			50

        lowercase_			:       Optional[Any]									=			jax.device_count()
        lowercase_			:       List[Any]									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[str]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			shard(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0565_2401)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 238_3808.2)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       Tuple									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase    )

        lowercase_			:       List[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Dict									=			jax.random.PRNGKey(0    )
        lowercase_			:       Optional[int]									=			50

        lowercase_			:       Tuple									=			jax.device_count()
        lowercase_			:       Dict									=			num_samples * [prompt]
        lowercase_			:       Any									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       int									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			shard(__UpperCamelCase    )

        lowercase_			:       List[str]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0400_3906)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 237_3516.75)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       str									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa    )

        lowercase_			:       Optional[int]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Optional[Any]									=			jax.random.PRNGKey(0    )
        lowercase_			:       Union[str, Any]									=			50

        lowercase_			:       Optional[Any]									=			jax.device_count()
        lowercase_			:       Tuple									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			shard(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0400_3906)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 237_3516.75)    ) < 5e-1
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        lowercase_			:       Tuple									=			FlaxDDIMScheduler(
            beta_start=0.0_0085  ,beta_end=0.012  ,beta_schedule='scaled_linear'  ,set_alpha_to_one=__UpperCamelCase  ,steps_offset=1  ,)

        lowercase_							,						lowercase_			:       str									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,scheduler=__UpperCamelCase  ,safety_checker=__UpperCamelCase  ,)
        lowercase_			:       Optional[int]									=			scheduler.create_state()

        lowercase_			:       List[Any]									=			scheduler_state

        lowercase_			:       List[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       str									=			jax.random.PRNGKey(0    )
        lowercase_			:       str									=			50

        lowercase_			:       List[Any]									=			jax.device_count()
        lowercase_			:       List[str]									=			num_samples * [prompt]
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )

        # shard inputs and rng
        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			jax.random.split(__UpperCamelCase  ,__UpperCamelCase    )
        lowercase_			:       str									=			shard(__UpperCamelCase    )

        lowercase_			:       str									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images

        assert images.shape == (num_samples, 1, 512, 512, 3)
        if jax.device_count() == 8:
             assert np.abs((np.abs(images[0, 0, :2, :2, -2:]  ,dtype=np.floataa    ).sum() - 0.0_4504_3945)    ) < 1e-3
             assert np.abs((np.abs(__UpperCamelCase  ,dtype=np.floataa    ).sum() - 234_7693.5)    ) < 5e-1







   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Optional[Any]									=			(
            'A cinematic film still of Morgan Freeman starring as Jimi Hendrix, portrait, 40mm lens, shallow depth of'
            ' field, close up, split lighting, cinematic'
        )

        lowercase_			:       Union[str, Any]									=			jax.device_count()
        lowercase_			:       List[str]									=			num_samples * [prompt]
        lowercase_			:       int									=			jax.random.split(jax.random.PRNGKey(0    )  ,__UpperCamelCase    )

        lowercase_							,						lowercase_			:       Dict									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase  ,)

        lowercase_			:       str									=			replicate(__UpperCamelCase    )
        lowercase_			:       int									=			pipeline.prepare_inputs(__UpperCamelCase    )
        lowercase_			:       Optional[int]									=			shard(__UpperCamelCase    )
        lowercase_			:       Tuple									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images
        assert images.shape == (num_samples, 1, 512, 512, 3)
        lowercase_			:       Union[str, Any]									=			images[2, 0, 256, 10:17, 1]

        # With memory efficient attention
        lowercase_							,						lowercase_			:       List[Any]									=			FlaxStableDiffusionPipeline.from_pretrained(
            'CompVis/stable-diffusion-v1-4'  ,revision='bf16'  ,dtype=jnp.bfloataa  ,safety_checker=__UpperCamelCase  ,use_memory_efficient_attention=__UpperCamelCase  ,)

        lowercase_			:       List[Any]									=			replicate(__UpperCamelCase    )
        lowercase_			:       Union[str, Any]									=			pipeline.prepare_inputs(__UpperCamelCase    )
        lowercase_			:       List[Any]									=			shard(__UpperCamelCase    )
        lowercase_			:       Any									=			pipeline(__UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase  ,jit=__UpperCamelCase    ).images
        assert images_eff.shape == (num_samples, 1, 512, 512, 3)
        lowercase_			:       Optional[int]									=			images[2, 0, 256, 10:17, 1]

        # I checked the results visually and they are very similar. However, I saw that the max diff is `1` and the `sum`
        # over the 8 images is exactly `256`, which is very suspicious. Testing a random slice for now.
        assert abs(slice_eff - slice    ).max() < 1e-2

