





import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__lowercase  :   Dict						      =logging.getLogger(__name__)

@dataclass
class      A       :
      _snake_case							     =field(
          metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}						)
      _snake_case							     =field(
          default=__lowercase		,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}						)
      _snake_case							     =field(
          default=__lowercase		,      metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''}						)
      _snake_case							     =field(
          default=__lowercase		,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''}		,      )
      _snake_case							     =field(default=__lowercase		,      metadata={'''help''': '''Whether tp freeze the encoder.'''}						)
      _snake_case							     =field(default=__lowercase		,      metadata={'''help''': '''Whether  to freeze the embeddings.'''}						)

@dataclass
class      A       :
      _snake_case							     =field(
          metadata={'''help''': '''The input data dir. Should contain the .tsv files (or other data files) for the task.'''}						)
      _snake_case							     =field(
          default='''summarization'''		,      metadata={'''help''': '''Task name, summarization (or summarization_{dataset} for pegasus) or translation'''}		,      )
      _snake_case							     =field(
          default=1024		,      metadata={
              '''help''': (
                  '''The maximum total input sequence length after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          }		,      )
      _snake_case							     =field(
          default=128		,      metadata={
              '''help''': (
                  '''The maximum total sequence length for target text after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          }		,      )
      _snake_case							     =field(
          default=142		,      metadata={
              '''help''': (
                  '''The maximum total sequence length for validation target text after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded. '''
                  '''This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '''
                  '''during ``evaluate`` and ``predict``.'''
              )
          }		,      )
      _snake_case							     =field(
          default=142		,      metadata={
              '''help''': (
                  '''The maximum total sequence length for test target text after tokenization. Sequences longer '''
                  '''than this will be truncated, sequences shorter will be padded.'''
              )
          }		,      )
      _snake_case							     =field(default=-1		,      metadata={'''help''': '''# training examples. -1 means use all.'''}						)
      _snake_case							     =field(default=-1		,      metadata={'''help''': '''# validation examples. -1 means use all.'''}						)
      _snake_case							     =field(default=-1		,      metadata={'''help''': '''# test examples. -1 means use all.'''}						)
      _snake_case							     =field(default=__lowercase		,      metadata={'''help''': '''Source language id for translation.'''}						)
      _snake_case							     =field(default=__lowercase		,      metadata={'''help''': '''Target language id for translation.'''}						)
      _snake_case							     =field(default=__lowercase		,      metadata={'''help''': '''# num_beams to use for evaluation.'''}						)
      _snake_case							     =field(
          default=__lowercase		,      metadata={'''help''': '''If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'''}		,      )




def     a__ (					lowercase__  ,    lowercase__  ,    lowercase__  ):






   '''simple docstring'''






   logger.info(F'***** {split} metrics *****'  )
   for key in sorted(metrics.keys()  ):
      logger.info(F'  {key} = {metrics[key]}'  )
   save_json(lowercase__  ,    os.path.join(lowercase__  ,    F'{split}_results.json'  )  )




def     a__ (					):






   '''simple docstring'''






   UpperCAmelCase_											=HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)  )

   if len(sys.argv  ) == 2 and sys.argv[1].endswith(".json"  ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
   else:
      UpperCAmelCase_						,       UpperCAmelCase_						,       UpperCAmelCase_											=parser.parse_args_into_dataclasses()

   check_output_dir(lowercase__  )

   # Setup logging
   logging.basicConfig(
       format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"  ,    datefmt="%m/%d/%Y %H:%M:%S"  ,    level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,    )
   logger.warning(
       "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"  ,    training_args.local_rank  ,    training_args.device  ,    training_args.n_gpu  ,    bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED  )  ,    training_args.fpaa  ,    )
   transformers.utils.logging.enable_default_handler()
   transformers.utils.logging.enable_explicit_format()
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank  ):
      transformers.utils.logging.set_verbosity_info()
   logger.info("Training/evaluation parameters %s"  ,    lowercase__  )

   # Set seed
   set_seed(training_args.seed  )

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.

   UpperCAmelCase_											=AutoConfig.from_pretrained(
       model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,    cache_dir=model_args.cache_dir  ,    )

   UpperCAmelCase_											=("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
   for p in extra_model_params:
      if getattr(lowercase__  ,    lowercase__  ,    lowercase__  ):
         assert hasattr(lowercase__  ,    lowercase__  ), F'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
         setattr(lowercase__  ,    lowercase__  ,    getattr(lowercase__  ,    lowercase__  )  )

   UpperCAmelCase_											=AutoTokenizer.from_pretrained(
       model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,    cache_dir=model_args.cache_dir  ,    )
   UpperCAmelCase_											=AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path  ,    from_tf=".ckpt" in model_args.model_name_or_path  ,    config=lowercase__  ,    cache_dir=model_args.cache_dir  ,    )

   # use task specific params
   use_task_specific_params(lowercase__  ,    data_args.task  )

   # set num_beams for evaluation
   if data_args.eval_beams is None:
      UpperCAmelCase_											=model.config.num_beams

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(lowercase__  ,    (MBartTokenizer, MBartTokenizerFast)  ):
      assert (
          data_args.tgt_lang is not None and data_args.src_lang is not None
      ), "mBart requires --tgt_lang and --src_lang"
      if isinstance(lowercase__  ,    lowercase__  ):
         UpperCAmelCase_											=tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         UpperCAmelCase_											=tokenizer.convert_tokens_to_ids(data_args.tgt_lang  )

   if model_args.freeze_embeds:
      freeze_embeds(lowercase__  )
   if model_args.freeze_encoder:
      freeze_params(model.get_encoder()  )
      assert_all_frozen(model.get_encoder()  )

   UpperCAmelCase_											=SeqaSeqDataset

   # Get datasets
   UpperCAmelCase_											=(
       dataset_class(
           lowercase__  ,    type_path="train"  ,    data_dir=data_args.data_dir  ,    n_obs=data_args.n_train  ,    max_target_length=data_args.max_target_length  ,    max_source_length=data_args.max_source_length  ,    prefix=model.config.prefix or ""  ,    )
       if training_args.do_train
       else None
   )
   UpperCAmelCase_											=(
       dataset_class(
           lowercase__  ,    type_path="val"  ,    data_dir=data_args.data_dir  ,    n_obs=data_args.n_val  ,    max_target_length=data_args.val_max_target_length  ,    max_source_length=data_args.max_source_length  ,    prefix=model.config.prefix or ""  ,    )
       if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
       else None
   )
   UpperCAmelCase_											=(
       dataset_class(
           lowercase__  ,    type_path="test"  ,    data_dir=data_args.data_dir  ,    n_obs=data_args.n_test  ,    max_target_length=data_args.test_max_target_length  ,    max_source_length=data_args.max_source_length  ,    prefix=model.config.prefix or ""  ,    )
       if training_args.do_predict
       else None
   )

   # Initialize our Trainer
   UpperCAmelCase_											=(
       build_compute_metrics_fn(data_args.task  ,    lowercase__  ) if training_args.predict_with_generate else None
   )
   UpperCAmelCase_											=SeqaSeqTrainer(
       model=lowercase__  ,    args=lowercase__  ,    data_args=lowercase__  ,    train_dataset=lowercase__  ,    eval_dataset=lowercase__  ,    data_collator=SeqaSeqDataCollator(
           lowercase__  ,    lowercase__  ,    model.config.decoder_start_token_id  ,    training_args.tpu_num_cores  )  ,    compute_metrics=lowercase__  ,    tokenizer=lowercase__  ,    )

   UpperCAmelCase_											={}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"  )

      UpperCAmelCase_											=trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path  ) else None  )
      UpperCAmelCase_											=train_result.metrics
      UpperCAmelCase_											=data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"  ,    lowercase__  ,    training_args.output_dir  )
         all_metrics.update(lowercase__  )

         # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir  ,    "trainer_state.json"  )  )

         # For convenience, we also re-save the tokenizer to the same directory,
         # so that you can share your model easily on huggingface.co/models =)
         tokenizer.save_pretrained(training_args.output_dir  )

    # Evaluation
   if training_args.do_eval:
      logger.info("*** Evaluate ***"  )

      UpperCAmelCase_											=trainer.evaluate(metric_key_prefix="val"  )
      UpperCAmelCase_											=data_args.n_val
      UpperCAmelCase_											=round(metrics["val_loss"]  ,    4  )

      if trainer.is_world_process_zero():
         handle_metrics("val"  ,    lowercase__  ,    training_args.output_dir  )
         all_metrics.update(lowercase__  )

   if training_args.do_predict:
      logger.info("*** Predict ***"  )

      UpperCAmelCase_											=trainer.predict(test_dataset=lowercase__  ,    metric_key_prefix="test"  )
      UpperCAmelCase_											=test_output.metrics
      UpperCAmelCase_											=data_args.n_test

      if trainer.is_world_process_zero():
         UpperCAmelCase_											=round(metrics["test_loss"]  ,    4  )
         handle_metrics("test"  ,    lowercase__  ,    training_args.output_dir  )
         all_metrics.update(lowercase__  )

         if training_args.predict_with_generate:
            UpperCAmelCase_											=tokenizer.batch_decode(
                test_output.predictions  ,    skip_special_tokens=lowercase__  ,    clean_up_tokenization_spaces=lowercase__  )
            UpperCAmelCase_											=lmap(str.strip  ,    lowercase__  )
            write_txt_file(lowercase__  ,    os.path.join(training_args.output_dir  ,    "test_generations.txt"  )  )

   if trainer.is_world_process_zero():
      save_json(lowercase__  ,    os.path.join(training_args.output_dir  ,    "all_results.json"  )  )

   return all_metrics




def     a__ (					lowercase__  ):






   '''simple docstring'''






   main()


if __name__ == "__main__":
     main()











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      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						str       )						->    Union[str, Any]:
         '''simple docstring'''

         with tempfile.TemporaryDirectory() as tmpdirname:
            # pipeline has Flax weights
            UpperCAmelCase_											=FlaxDiffusionPipeline.from_pretrained(
                "hf-internal-testing/tiny-stable-diffusion-pipe"		,  safety_checker=_lowerCAmelCase		,  cache_dir=_lowerCAmelCase       )

            UpperCAmelCase_											=[t[-1] for t in os.walk(os.path.join(_lowerCAmelCase		,  os.listdir(_lowerCAmelCase       )[0]		,  "snapshots"       )       )]
            UpperCAmelCase_											=[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      A       (	unittest.TestCase						):



      def 	lowerCAmelCase__    (	self:						Union[str, Any]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "hf-internal-testing/tiny-stable-diffusion-pipe"		,  safety_checker=_lowerCAmelCase       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=4

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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() - 4.1_51_47_45       ) < 1e-3
            assert np.abs(np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 4_99_47.8_75       ) < 5e-1

         UpperCAmelCase_											=pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]       )       )       )
         assert len(_lowerCAmelCase       ) == num_samples



      def 	lowerCAmelCase__    (	self:						List[Any]       )						->    Optional[Any]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="flax"		,  safety_checker=_lowerCAmelCase       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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() - 0.05_65_24_01)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_38_38_08.2)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						int       )						->    Union[str, Any]:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  safety_checker=_lowerCAmelCase       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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() - 0.04_00_39_06)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_37_35_16.75)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						Optional[int]       )						->    Tuple:
         '''simple docstring'''

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa       )

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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() - 0.04_00_39_06)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_37_35_16.75)       ) < 5e-1



      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    str:
         '''simple docstring'''

         UpperCAmelCase_											=FlaxDDIMScheduler(
             beta_start=0.0_00_85		,  beta_end=0.0_12		,  beta_schedule="scaled_linear"		,  set_alpha_to_one=_lowerCAmelCase		,  steps_offset=1		,  )

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  scheduler=_lowerCAmelCase		,  safety_checker=_lowerCAmelCase		,  )
         UpperCAmelCase_											=scheduler.create_state()

         UpperCAmelCase_											=scheduler_state

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

         UpperCAmelCase_											=jax.random.PRNGKey(0       )
         UpperCAmelCase_											=50

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )

         # shard inputs and rng
         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=jax.random.split(_lowerCAmelCase		,  _lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )

         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).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_45_04_39_45)       ) < 1e-3
            assert np.abs((np.abs(_lowerCAmelCase		,  dtype=np.floataa       ).sum() - 2_34_76_93.5)       ) < 5e-1






      def 	lowerCAmelCase__    (	self:						Optional[Any]       )						->    int:
         '''simple docstring'''

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

         UpperCAmelCase_											=jax.device_count()
         UpperCAmelCase_											=num_samples * [prompt]
         UpperCAmelCase_											=jax.random.split(jax.random.PRNGKey(0       )		,  _lowerCAmelCase       )

         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  safety_checker=_lowerCAmelCase		,  )

         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images
         assert images.shape == (num_samples, 1, 512, 512, 3)
         UpperCAmelCase_											=images[2, 0, 256, 10:17, 1]

         # With memory efficient attention
         UpperCAmelCase_						,       UpperCAmelCase_											=FlaxStableDiffusionPipeline.from_pretrained(
             "CompVis/stable-diffusion-v1-4"		,  revision="bf16"		,  dtype=jnp.bfloataa		,  safety_checker=_lowerCAmelCase		,  use_memory_efficient_attention=_lowerCAmelCase		,  )

         UpperCAmelCase_											=replicate(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline.prepare_inputs(_lowerCAmelCase       )
         UpperCAmelCase_											=shard(_lowerCAmelCase       )
         UpperCAmelCase_											=pipeline(_lowerCAmelCase		,  _lowerCAmelCase		,  _lowerCAmelCase		,  jit=_lowerCAmelCase       ).images
         assert images_eff.shape == (num_samples, 1, 512, 512, 3)
         UpperCAmelCase_											=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




