



'''simple docstring'''






import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


UpperCamelCase__:   Union[str, Any]								=			logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()


def  snake_case_     (					_lowerCAmelCase  :						str  )      ->   Optional[int]:
							if "token" in model_name_or_path:
														return "rag_token"
							if "sequence" in model_name_or_path:
														return "rag_sequence"
							if "bart" in model_name_or_path:
														return "bart"
							return None


def  snake_case_     (					_lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						str  )      ->   Dict:
							return max(metric_fn(_lowerCAmelCase   ,      _lowerCAmelCase  ) for gt in ground_truths  )


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						List[str]   ,      _lowerCAmelCase  :						Optional[int]  )      ->   List[str]:
							UpperCAmelCase :       str 							=		[line.strip() for line in open(_lowerCAmelCase   ,      '''r'''  ).readlines()]
							UpperCAmelCase :       Optional[Any] 							=		[]

							if args.gold_data_mode == "qa":
														UpperCAmelCase :       Optional[int] 							=		pd.read_csv(_lowerCAmelCase   ,      sep='''\t'''   ,      header=_lowerCAmelCase  )
														for answer_list in data[1]:
																					UpperCAmelCase :       int 							=		ast.literal_eval(_lowerCAmelCase  )
																					answers.append(_lowerCAmelCase  )
							else:
														UpperCAmelCase :       int 							=		[line.strip() for line in open(_lowerCAmelCase   ,      '''r'''  ).readlines()]
														UpperCAmelCase :       Optional[Any] 							=		[[reference] for reference in references]

							UpperCAmelCase :       Optional[int] 							=		0
							for prediction, ground_truths in zip(_lowerCAmelCase   ,      _lowerCAmelCase  ):
														total += 1
														em += metric_max_over_ground_truths(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
														fa += metric_max_over_ground_truths(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )

							UpperCAmelCase :       Union[str, Any] 							=		1_0_0.0 * em / total
							UpperCAmelCase :       Any 							=		1_0_0.0 * fa / total

							logger.info(f"""F1: {fa:.2f}"""  )
							logger.info(f"""EM: {em:.2f}"""  )


def  snake_case_     (					_lowerCAmelCase  :						List[Any]   ,      _lowerCAmelCase  :						Dict   ,      _lowerCAmelCase  :						Optional[int]  )      ->   Optional[Any]:
							UpperCAmelCase :       Union[str, Any] 							=		args.k
							UpperCAmelCase :       Tuple 							=		[line.strip() for line in open(_lowerCAmelCase   ,      '''r'''  ).readlines()]
							UpperCAmelCase :       Tuple 							=		[line.strip() for line in open(_lowerCAmelCase   ,      '''r'''  ).readlines()]

							UpperCAmelCase :       Any 							=		0
							for hypo, reference in zip(_lowerCAmelCase   ,      _lowerCAmelCase  ):
														UpperCAmelCase :       Dict 							=		set(hypo.split('''\t'''  )[:k]  )
														UpperCAmelCase :       Optional[Any] 							=		set(reference.split('''\t'''  )  )
														total += 1
														em += len(hypo_provenance & ref_provenance  ) / k

							UpperCAmelCase :       str 							=		1_0_0.0 * em / total
							logger.info(f"""Precision@{k}: {em: .2f}"""  )


def  snake_case_     (					_lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						int   ,      _lowerCAmelCase  :						Dict  )      ->   Optional[int]:
							def strip_title(_lowerCAmelCase  :						Optional[int]  ):
														if title.startswith('''"'''  ):
																					UpperCAmelCase :       Tuple 							=		title[1:]
														if title.endswith('''"'''  ):
																					UpperCAmelCase :       Optional[Any] 							=		title[:-1]
														return title

							UpperCAmelCase :       Union[str, Any] 							=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
							    _lowerCAmelCase   ,      return_tensors='''pt'''   ,      padding=_lowerCAmelCase   ,      truncation=_lowerCAmelCase   ,      )['''input_ids'''].to(args.device  )

							UpperCAmelCase :       str 							=		rag_model.rag.question_encoder(_lowerCAmelCase  )
							UpperCAmelCase :       Union[str, Any] 							=		question_enc_outputs[0]

							UpperCAmelCase :       Any 							=		rag_model.retriever(
							    _lowerCAmelCase   ,      question_enc_pool_output.cpu().detach().to(torch.floataa  ).numpy()   ,      prefix=rag_model.rag.generator.config.prefix   ,      n_docs=rag_model.config.n_docs   ,      return_tensors='''pt'''   ,      )
							UpperCAmelCase :       int 							=		rag_model.retriever.index.get_doc_dicts(result.doc_ids  )
							UpperCAmelCase :       int 							=		[]
							for docs in all_docs:
														UpperCAmelCase :       Optional[int] 							=		[strip_title(_lowerCAmelCase  ) for title in docs['''title''']]
														provenance_strings.append('''\t'''.join(_lowerCAmelCase  )  )
							return provenance_strings


def  snake_case_     (					_lowerCAmelCase  :						Optional[int]   ,      _lowerCAmelCase  :						Any   ,      _lowerCAmelCase  :						Dict  )      ->   List[str]:
							with torch.no_grad():
														UpperCAmelCase :       Any 							=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
														    _lowerCAmelCase   ,      return_tensors='''pt'''   ,      padding=_lowerCAmelCase   ,      truncation=_lowerCAmelCase  )

														UpperCAmelCase :       Union[str, Any] 							=		inputs_dict.input_ids.to(args.device  )
														UpperCAmelCase :       Union[str, Any] 							=		inputs_dict.attention_mask.to(args.device  )
														UpperCAmelCase :       Optional[int] 							=		rag_model.generate(  # rag_model overwrites generate
														    _lowerCAmelCase   ,      attention_mask=_lowerCAmelCase   ,      num_beams=args.num_beams   ,      min_length=args.min_length   ,      max_length=args.max_length   ,      early_stopping=_lowerCAmelCase   ,      num_return_sequences=1   ,      bad_words_ids=[[0, 0]]   ,      )
														UpperCAmelCase :       str 							=		rag_model.retriever.generator_tokenizer.batch_decode(_lowerCAmelCase   ,      skip_special_tokens=_lowerCAmelCase  )

														if args.print_predictions:
																					for q, a in zip(_lowerCAmelCase   ,      _lowerCAmelCase  ):
																												logger.info('''Q: {} - A: {}'''.format(_lowerCAmelCase   ,      _lowerCAmelCase  )  )

														return answers


def  snake_case_     (					)      ->   List[Any]:
							UpperCAmelCase :       Any 							=		argparse.ArgumentParser()
							parser.add_argument(
							    '''--model_type'''   ,      choices=['''rag_sequence''', '''rag_token''', '''bart''']   ,      type=_lowerCAmelCase   ,      help=(
							        '''RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the'''
							        ''' model_name_or_path'''
							    )   ,      )
							parser.add_argument(
							    '''--index_name'''   ,      default=_lowerCAmelCase   ,      choices=['''exact''', '''compressed''', '''legacy''']   ,      type=_lowerCAmelCase   ,      help='''RAG model retriever type'''   ,      )
							parser.add_argument(
							    '''--index_path'''   ,      default=_lowerCAmelCase   ,      type=_lowerCAmelCase   ,      help='''Path to the retrieval index'''   ,      )
							parser.add_argument('''--n_docs'''   ,      default=5   ,      type=_lowerCAmelCase   ,      help='''Number of retrieved docs'''  )
							parser.add_argument(
							    '''--model_name_or_path'''   ,      default=_lowerCAmelCase   ,      type=_lowerCAmelCase   ,      required=_lowerCAmelCase   ,      help='''Path to pretrained checkpoints or model identifier from huggingface.co/models'''   ,      )
							parser.add_argument(
							    '''--eval_mode'''   ,      choices=['''e2e''', '''retrieval''']   ,      default='''e2e'''   ,      type=_lowerCAmelCase   ,      help=(
							        '''Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates'''
							        ''' precision@k.'''
							    )   ,      )
							parser.add_argument('''--k'''   ,      default=1   ,      type=_lowerCAmelCase   ,      help='''k for the precision@k calculation'''  )
							parser.add_argument(
							    '''--evaluation_set'''   ,      default=_lowerCAmelCase   ,      type=_lowerCAmelCase   ,      required=_lowerCAmelCase   ,      help='''Path to a file containing evaluation samples'''   ,      )
							parser.add_argument(
							    '''--gold_data_path'''   ,      default=_lowerCAmelCase   ,      type=_lowerCAmelCase   ,      required=_lowerCAmelCase   ,      help='''Path to a tab-separated file with gold samples'''   ,      )
							parser.add_argument(
							    '''--gold_data_mode'''   ,      default='''qa'''   ,      type=_lowerCAmelCase   ,      choices=['''qa''', '''ans''']   ,      help=(
							        '''Format of the gold data file'''
							        '''qa - a single line in the following format: question [tab] answer_list'''
							        '''ans - a single line of the gold file contains the expected answer string'''
							    )   ,      )
							parser.add_argument(
							    '''--predictions_path'''   ,      type=_lowerCAmelCase   ,      default='''predictions.txt'''   ,      help='''Name of the predictions file, to be stored in the checkpoints directory'''   ,      )
							parser.add_argument(
							    '''--eval_all_checkpoints'''   ,      action='''store_true'''   ,      help='''Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number'''   ,      )
							parser.add_argument(
							    '''--eval_batch_size'''   ,      default=8   ,      type=_lowerCAmelCase   ,      help='''Batch size per GPU/CPU for evaluation.'''   ,      )
							parser.add_argument(
							    '''--recalculate'''   ,      help='''Recalculate predictions even if the prediction file exists'''   ,      action='''store_true'''   ,      )
							parser.add_argument(
							    '''--num_beams'''   ,      default=4   ,      type=_lowerCAmelCase   ,      help='''Number of beams to be used when generating answers'''   ,      )
							parser.add_argument('''--min_length'''   ,      default=1   ,      type=_lowerCAmelCase   ,      help='''Min length of the generated answers'''  )
							parser.add_argument('''--max_length'''   ,      default=50   ,      type=_lowerCAmelCase   ,      help='''Max length of the generated answers'''  )

							parser.add_argument(
							    '''--print_predictions'''   ,      action='''store_true'''   ,      help='''If True, prints predictions while evaluating.'''   ,      )
							parser.add_argument(
							    '''--print_docs'''   ,      action='''store_true'''   ,      help='''If True, prints docs retried while generating.'''   ,      )
							UpperCAmelCase :       Any 							=		parser.parse_args()
							UpperCAmelCase :       str 							=		torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )
							return args


def  snake_case_     (					_lowerCAmelCase  :						Union[str, Any]  )      ->   int:
							UpperCAmelCase :       int 							=		{}
							if args.model_type is None:
														UpperCAmelCase :       Union[str, Any] 							=		infer_model_type(args.model_name_or_path  )
														assert args.model_type is not None
							if args.model_type.startswith('''rag'''  ):
														UpperCAmelCase :       Optional[Any] 							=		RagTokenForGeneration if args.model_type == '''rag_token''' else RagSequenceForGeneration
														UpperCAmelCase :       Dict 							=		args.n_docs
														if args.index_name is not None:
																					UpperCAmelCase :       Dict 							=		args.index_name
														if args.index_path is not None:
																					UpperCAmelCase :       str 							=		args.index_path
							else:
														UpperCAmelCase :       int 							=		BartForConditionalGeneration

							UpperCAmelCase :       Optional[Any] 							=		(
							    [f.path for f in os.scandir(args.model_name_or_path  ) if f.is_dir()]
							    if args.eval_all_checkpoints
							    else [args.model_name_or_path]
							)

							logger.info('''Evaluate the following checkpoints: %s'''   ,      _lowerCAmelCase  )

							UpperCAmelCase :       str 							=		get_scores if args.eval_mode == '''e2e''' else get_precision_at_k
							UpperCAmelCase :       Any 							=		evaluate_batch_eae if args.eval_mode == '''e2e''' else evaluate_batch_retrieval

							for checkpoint in checkpoints:
														if os.path.exists(args.predictions_path  ) and (not args.recalculate):
																					logger.info('''Calculating metrics based on an existing predictions file: {}'''.format(args.predictions_path  )  )
																					score_fn(_lowerCAmelCase   ,      args.predictions_path   ,      args.gold_data_path  )
																					continue

														logger.info('''***** Running evaluation for {} *****'''.format(_lowerCAmelCase  )  )
														logger.info('''  Batch size = %d'''   ,      args.eval_batch_size  )
														logger.info('''  Predictions will be stored under {}'''.format(args.predictions_path  )  )

														if args.model_type.startswith('''rag'''  ):
																					UpperCAmelCase :       int 							=		RagRetriever.from_pretrained(_lowerCAmelCase   ,      **_lowerCAmelCase  )
																					UpperCAmelCase :       Optional[Any] 							=		model_class.from_pretrained(_lowerCAmelCase   ,      retriever=_lowerCAmelCase   ,      **_lowerCAmelCase  )
																					model.retriever.init_retrieval()
														else:
																					UpperCAmelCase :       Union[str, Any] 							=		model_class.from_pretrained(_lowerCAmelCase   ,      **_lowerCAmelCase  )
														model.to(args.device  )

														with open(args.evaluation_set   ,      '''r'''  ) as eval_file, open(args.predictions_path   ,      '''w'''  ) as preds_file:
																					UpperCAmelCase :       List[str] 							=		[]
																					for line in tqdm(_lowerCAmelCase  ):
																												questions.append(line.strip()  )
																												if len(_lowerCAmelCase  ) == args.eval_batch_size:
																																			UpperCAmelCase :       Any 							=		evaluate_batch_fn(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																																			preds_file.write('''\n'''.join(_lowerCAmelCase  ) + '''\n'''  )
																																			preds_file.flush()
																																			UpperCAmelCase :       str 							=		[]
																					if len(_lowerCAmelCase  ) > 0:
																												UpperCAmelCase :       Optional[int] 							=		evaluate_batch_fn(_lowerCAmelCase   ,      _lowerCAmelCase   ,      _lowerCAmelCase  )
																												preds_file.write('''\n'''.join(_lowerCAmelCase  )  )
																												preds_file.flush()

																					score_fn(_lowerCAmelCase   ,      args.predictions_path   ,      args.gold_data_path  )


if __name__ == "__main__":
						UpperCamelCase__:   List[str]								=			get_args()
						main(args)










'''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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     str		)			->		int:
								with tempfile.TemporaryDirectory() as tmpdirname:
															# pipeline has Flax weights
															UpperCAmelCase :       Optional[Any] 							=		FlaxDiffusionPipeline.from_pretrained(
															    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		,			cache_dir=__snake_case		)

															UpperCAmelCase :       str 							=		[t[-1] for t in os.walk(os.path.join(__snake_case		,			os.listdir(__snake_case		)[0]		,			'''snapshots'''		)		)]
															UpperCAmelCase :       str 							=		[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 				SCREAMING_SNAKE_CASE(							unittest.TestCase			):







	"""simple docstring"""






	def      A	(						self			:     List[str]		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       str 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''hf-internal-testing/tiny-stable-diffusion-pipe'''		,			safety_checker=__snake_case		)

								UpperCAmelCase :       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'''
								)

								UpperCAmelCase :       List[str] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Optional[Any] 							=		4

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Tuple 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).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(__snake_case		,			dtype=np.floataa		).sum() - 4_99_47.8_75		) < 5E-1

								UpperCAmelCase :       Union[str, Any] 							=		pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]		)		)		)
								assert len(__snake_case		) == num_samples







	def      A	(						self			:     List[Any]		)			->		List[str]:
								UpperCAmelCase							,		UpperCAmelCase :       Union[str, Any] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''flax'''		,			safety_checker=__snake_case		)

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

								UpperCAmelCase :       Optional[Any] 							=		jax.random.PRNGKey(0		)
								UpperCAmelCase :       Any 							=		50

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       int 							=		num_samples * [prompt]
								UpperCAmelCase :       Union[str, Any] 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Dict 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Tuple 							=		shard(__snake_case		)

								UpperCAmelCase :       Tuple 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).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(__snake_case		,			dtype=np.floataa		).sum() - 2_38_38_08.2)		) < 5E-1







	def      A	(						self			:     int		)			->		Dict:
								UpperCAmelCase							,		UpperCAmelCase :       List[str] 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			safety_checker=__snake_case		)

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

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

								UpperCAmelCase :       Union[str, Any] 							=		jax.device_count()
								UpperCAmelCase :       List[Any] 							=		num_samples * [prompt]
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       Optional[int] 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).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(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     int		)			->		Any:
								UpperCAmelCase							,		UpperCAmelCase :       Dict 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		)

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

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

								UpperCAmelCase :       Optional[int] 							=		jax.device_count()
								UpperCAmelCase :       List[str] 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

								# shard inputs and rng
								UpperCAmelCase :       Tuple 							=		replicate(__snake_case		)
								UpperCAmelCase :       Any 							=		jax.random.split(__snake_case		,			__snake_case		)
								UpperCAmelCase :       str 							=		shard(__snake_case		)

								UpperCAmelCase :       Optional[int] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).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(__snake_case		,			dtype=np.floataa		).sum() - 2_37_35_16.75)		) < 5E-1







	def      A	(						self			:     Tuple		)			->		Optional[Any]:
								UpperCAmelCase :       int 							=		FlaxDDIMScheduler(
								    beta_start=0.0_00_85		,			beta_end=0.0_12		,			beta_schedule='''scaled_linear'''		,			set_alpha_to_one=__snake_case		,			steps_offset=1		,			)

								UpperCAmelCase							,		UpperCAmelCase :       Tuple 							=		FlaxStableDiffusionPipeline.from_pretrained(
								    '''CompVis/stable-diffusion-v1-4'''		,			revision='''bf16'''		,			dtype=jnp.bfloataa		,			scheduler=__snake_case		,			safety_checker=__snake_case		,			)
								UpperCAmelCase :       Tuple 							=		scheduler.create_state()

								UpperCAmelCase :       Dict 							=		scheduler_state

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

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

								UpperCAmelCase :       Optional[Any] 							=		jax.device_count()
								UpperCAmelCase :       Any 							=		num_samples * [prompt]
								UpperCAmelCase :       Dict 							=		pipeline.prepare_inputs(__snake_case		)

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

								UpperCAmelCase :       Dict 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).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(__snake_case		,			dtype=np.floataa		).sum() - 2_34_76_93.5)		) < 5E-1







	def      A	(						self			:     Any		)			->		Tuple:
								UpperCAmelCase :       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'''
								)

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

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

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

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

								UpperCAmelCase :       int 							=		replicate(__snake_case		)
								UpperCAmelCase :       int 							=		pipeline.prepare_inputs(__snake_case		)
								UpperCAmelCase :       List[Any] 							=		shard(__snake_case		)
								UpperCAmelCase :       Optional[Any] 							=		pipeline(__snake_case		,			__snake_case		,			__snake_case		,			jit=__snake_case		).images
								assert images_eff.shape == (num_samples, 1, 512, 512, 3)
								UpperCAmelCase :       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





