
import logging
import os
from typing import List, Tuple

import numpy as np
import psutil
import torch
import torch.distributed as dist

from transformers import RagRetriever


lowercase  :					int													=       logging.getLogger(__name__)




class  lowerCamelCase__							(				snake_case_):




	'''simple docstring'''




	def __init__(					self		:List[str]     ,   a		:int     ,   a		:List[str]     ,   a		:Dict     ,   a		:Union[str, Any]=None						)		-> str:
								super().__init__(
								    a     ,   question_encoder_tokenizer=a     ,   generator_tokenizer=a     ,   index=a     ,   init_retrieval=a     ,   )
								__UpperCamelCase	:   Optional[int]			   =					None







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:int						)		-> Optional[Any]:
								logger.info("initializing retrieval"						)

								# initializing a separate process group for retrieval as the default
								# nccl backend doesn't support gather/scatter operations while gloo
								# is too slow to replace nccl for the core gpu communication
								if dist.is_initialized():
															logger.info("dist initialized"						)
															# needs to be set manually
															__UpperCamelCase	:   Tuple			   =					self._infer_socket_ifname()
															# avoid clash with the NCCL port
															__UpperCamelCase	:   Optional[Any]			   =					str(distributed_port + 1						)
															__UpperCamelCase	:   List[Any]			   =					dist.new_group(ranks=a     ,   backend="gloo"						)

								# initialize retriever only on the main worker
								if not dist.is_initialized() or self._is_main():
															logger.info("dist not initialized / main"						)
															self.index.init_index()

								# all processes wait untill the retriever is initialized by the main process
								if dist.is_initialized():
															torch.distributed.barrier(group=self.process_group						)







	def    _lowerCamelCase				(					self		:Tuple						)		-> List[str]:
								return dist.get_rank(group=self.process_group						) == 0







	def    _lowerCamelCase				(					self		:Tuple     ,   a		:Optional[Any]     ,   a		:Dict     ,   a		:Optional[int]=torch.floataa						)		-> Optional[int]:
								__UpperCamelCase	:   Optional[int]			   =					torch.empty(a     ,   dtype=a						)
								dist.scatter(a     ,   src=0     ,   scatter_list=a     ,   group=self.process_group						)
								return target_tensor







	def    _lowerCamelCase				(					self		:int						)		-> List[Any]:
								__UpperCamelCase	:   List[Any]			   =					psutil.net_if_addrs()
								# a hacky way to deal with varying network interface names
								__UpperCamelCase	:   Optional[int]			   =					next((addr for addr in addrs if addr.startswith("e"						))     ,   a						)
								return ifname







	def    _lowerCamelCase				(					self		:List[str]     ,   a		:np.ndarray     ,   a		:int						)		-> Tuple[np.ndarray, List[dict]]:





								if not dist.is_initialized():
															__UpperCamelCase	:   Dict			   =					self._main_retrieve(a     ,   a						)
															return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(a						)

								# distributed training
								__UpperCamelCase	:   List[str]			   =					dist.get_world_size(group=self.process_group						)

								# gather logic
								__UpperCamelCase	:   Tuple			   =					None
								if self._is_main():
															__UpperCamelCase	:   Tuple			   =					[torch.empty(question_hidden_states.shape     ,   dtype=torch.floataa						) for _ in range(a						)]
								dist.gather(torch.tensor(a						)     ,   dst=0     ,   gather_list=a     ,   group=self.process_group						)

								# scatter logic
								__UpperCamelCase	:   List[Any]			   =					question_hidden_states.shape[0]
								__UpperCamelCase	:   List[Any]			   =					[]
								__UpperCamelCase	:   Optional[Any]			   =					[]
								if self._is_main():
															assert len(a						) == world_size
															__UpperCamelCase	:   Tuple			   =					self._main_retrieve(torch.cat(a						).numpy()     ,   a						)
															__UpperCamelCase	:   Tuple			   =					torch.tensor(a						), torch.tensor(a						)
															__UpperCamelCase	:   Optional[Any]			   =					self._chunk_tensor(a     ,   a						)
															__UpperCamelCase	:   Any			   =					self._chunk_tensor(a     ,   a						)
								__UpperCamelCase	:   Any			   =					self._scattered(a     ,   [n_queries, n_docs]     ,   target_type=torch.intaa						)
								__UpperCamelCase	:   str			   =					self._scattered(a     ,   [n_queries, n_docs, question_hidden_states.shape[1]]						)

								return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(a						)
import gc
import unittest

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


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





@slow
@require_flax
class 		_lowercase						(					unittest.TestCase       ):

 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						str:

  """simple docstring"""





  super().tearDown()
  gc.collect()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Union[str, Any]			)   ->						Tuple:

  """simple docstring"""





  UpperCamelCase_, UpperCamelCase_					:						str =					FlaxStableDiffusionPipeline.from_pretrained(
      'stabilityai/stable-diffusion-2'   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )

  UpperCamelCase_					:						str =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Any =					jax.device_count()
  UpperCamelCase_					:						List[str] =					num_samples * [prompt]
  UpperCamelCase_					:						List[Any] =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						Dict =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Dict =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						Tuple =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						Optional[Any] =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						int =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						Tuple =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						str =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						List[str] =					jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2


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

  """simple docstring"""





  UpperCamelCase_					:						Tuple =					'stabilityai/stable-diffusion-2'
  UpperCamelCase_, UpperCamelCase_					:						Tuple =					FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case   ,      subfolder='scheduler'			)
  UpperCamelCase_, UpperCamelCase_					:						Union[str, Any] =					FlaxStableDiffusionPipeline.from_pretrained(
      snake_case   ,      scheduler=snake_case   ,      revision='bf16'   ,      dtype=jnp.bfloataa   ,      )
  UpperCamelCase_					:						Optional[int] =					scheduler_params

  UpperCamelCase_					:						Optional[Any] =					'A painting of a squirrel eating a burger'
  UpperCamelCase_					:						Union[str, Any] =					jax.device_count()
  UpperCamelCase_					:						Union[str, Any] =					num_samples * [prompt]
  UpperCamelCase_					:						Tuple =					sd_pipe.prepare_inputs(snake_case			)

  UpperCamelCase_					:						List[Any] =					replicate(snake_case			)
  UpperCamelCase_					:						Optional[Any] =					shard(snake_case			)

  UpperCamelCase_					:						Tuple =					jax.random.PRNGKey(0			)
  UpperCamelCase_					:						str =					jax.random.split(snake_case   ,      jax.device_count()			)

  UpperCamelCase_					:						str =					sd_pipe(snake_case   ,      snake_case   ,      snake_case   ,      num_inference_steps=2_5   ,      jit=snake_case			)[0]
  assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

  UpperCamelCase_					:						str =					images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]			)
  UpperCamelCase_					:						int =					images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

  UpperCamelCase_					:						int =					jnp.asarray(jax.device_get(image_slice.flatten()			)			)
  UpperCamelCase_					:						Union[str, Any] =					jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]			)
  print(f"output_slice: {output_slice}"			)
  assert jnp.abs(output_slice - expected_slice			).max() < 1e-2



