





import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


SCREAMING_SNAKE_CASE__							:      int         =    logging.get_logger(__name__)






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase=False ):
   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
          " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
          " instructions." )
      raise

   if not is_sharded:
      a__						:    List[Any]   		=	os.path.abspath(lowerCamelCase )
      logger.info(F"""Loading PyTorch weights from {pt_path}""" )

      a__						:    Optional[int]   		=	torch.load(lowerCamelCase				,			map_location="cpu" )
      logger.info(F"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )

      a__						:    int   		=	convert_pytorch_state_dict_to_flax(lowerCamelCase				,			lowerCamelCase )
   else:
      # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
      a__						:    Optional[Any]   		=	convert_pytorch_sharded_state_dict_to_flax(lowerCamelCase				,			lowerCamelCase )
   return flax_state_dict






def    _A	(   lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			):

   def is_key_or_prefix_key_in_dict(lowerCamelCase ) -> bool:
      return len(set(lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0

   # layer norm
   a__						:    List[Any]   		=	pt_tuple_key[:-1] + ("scale",)
   if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(lowerCamelCase ):
      return renamed_pt_tuple_key, pt_tensor

   # batch norm layer mean
   a__						:    Optional[int]   		=	pt_tuple_key[:-1] + ("mean",)
   if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
      return renamed_pt_tuple_key, pt_tensor

   # batch norm layer var
   a__						:    Tuple   		=	pt_tuple_key[:-1] + ("var",)
   if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   a__						:    Union[str, Any]   		=	pt_tuple_key[:-1] + ("embedding",)
   if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(lowerCamelCase ):
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   a__						:    Union[str, Any]   		=	pt_tuple_key[:-1] + ("kernel",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
      a__						:    Dict   		=	pt_tensor.transpose(2				,			3				,			1				,			0 )
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   a__						:    List[str]   		=	pt_tuple_key[:-1] + ("kernel",)
   if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(lowerCamelCase ):
      a__						:    Any   		=	pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   a__						:    List[Any]   		=	pt_tuple_key[:-1] + ("weight",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   a__						:    Tuple   		=	pt_tuple_key[:-1] + ("bias",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
   a__						:    Dict   		=	None
   if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
      a__						:    Any   		=	pt_tuple_key[-2] + "_g"
   elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
      a__						:    Tuple   		=	pt_tuple_key[-2] + "_v"
   if name is not None:
      a__						:    str   		=	pt_tuple_key[:-3] + (name,)
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   # convert pytorch tensor to numpy
   a__						:    Tuple   		=	{k: v.numpy() for k, v in pt_state_dict.items()}

   a__						:    List[Any]   		=	flax_model.base_model_prefix

   # use params dict if the model contains batch norm layers
   if "params" in flax_model.params:
      a__						:    Any   		=	flax_model.params["params"]
   else:
      a__						:    Union[str, Any]   		=	flax_model.params
   a__						:    Tuple   		=	flatten_dict(lowerCamelCase )

   # add batch_stats keys,values to dict
   if "batch_stats" in flax_model.params:
      a__						:    List[str]   		=	flatten_dict(flax_model.params["batch_stats"] )
      random_flax_state_dict.update(lowerCamelCase )

   a__						:    Union[str, Any]   		=	{}

   a__						:    Any   		=	(model_prefix not in flax_model_params) and (
       model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
   )
   a__						:    Optional[Any]   		=	(model_prefix in flax_model_params) and (
       model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
   )

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      a__						:    Union[str, Any]   		=	tuple(pt_key.split("." ) )

      # remove base model prefix if necessary
      a__						:    int   		=	pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
         a__						:    List[str]   		=	pt_tuple_key[1:]

      # Correctly rename weight parameters
      a__		,  a__						:    str   		=	rename_key_and_reshape_tensor(
          lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )

      # add model prefix if necessary
      a__						:    Optional[int]   		=	(model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
         a__						:    Tuple   		=	(model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )

        # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
         if "mean" in flax_key[-1] or "var" in flax_key[-1]:
            a__						:    List[str]   		=	jnp.asarray(lowerCamelCase )
            continue
         # remove num_batches_tracked key
         if "num_batches_tracked" in flax_key[-1]:
            flax_state_dict.pop(lowerCamelCase				,			lowerCamelCase )
            continue

         # also add unexpected weight so that warning is thrown
         a__						:    int   		=	jnp.asarray(lowerCamelCase )

      else:
         # also add unexpected weight so that warning is thrown
         a__						:    Optional[Any]   		=	jnp.asarray(lowerCamelCase )

   return unflatten_dict(lowerCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   import torch

   # Load the index
   a__						:    Union[str, Any]   		=	{}
   for shard_file in shard_filenames:
      # load using msgpack utils
      a__						:    Dict   		=	torch.load(lowerCamelCase )
      a__						:    List[Any]   		=	{k: v.numpy() for k, v in pt_state_dict.items()}

      a__						:    Union[str, Any]   		=	flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
      if "batch_stats" in flax_model.params:
         a__						:    str   		=	flax_model.params["params"]

         a__						:    List[Any]   		=	flatten_dict(lowerCamelCase )
         random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"] ) )
      else:
         a__						:    List[Any]   		=	flax_model.params
         a__						:    Optional[int]   		=	flatten_dict(lowerCamelCase )

      a__						:    str   		=	(model_prefix not in flax_model_params) and (
          model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
      )
      a__						:    Union[str, Any]   		=	(model_prefix in flax_model_params) and (
          model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
      )
      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
         a__						:    Optional[Any]   		=	tuple(pt_key.split("." ) )

         # remove base model prefix if necessary
         a__						:    Union[str, Any]   		=	pt_tuple_key[0] == model_prefix
         if load_model_with_head_into_base_model and has_base_model_prefix:
            a__						:    Union[str, Any]   		=	pt_tuple_key[1:]

         # Correctly rename weight parameters
         a__		,  a__						:    int   		=	rename_key_and_reshape_tensor(
             lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase				,			lowerCamelCase )
         # add model prefix if necessary
         a__						:    Union[str, Any]   		=	(model_prefix,) + flax_key in random_flax_state_dict
         if load_base_model_into_model_with_head and require_base_model_prefix:
            a__						:    Dict   		=	(model_prefix,) + flax_key

         if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
               raise ValueError(
                   F"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
                   F"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )

            # add batch stats if the model contains batchnorm layers
         if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1]:
               a__						:    str   		=	jnp.asarray(lowerCamelCase )
               continue
            if "var" in flax_key[-1]:
               a__						:    List[Any]   		=	jnp.asarray(lowerCamelCase )
               continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
               flax_state_dict.pop(lowerCamelCase				,			lowerCamelCase )
               continue

            # also add unexpected weight so that warning is thrown
            a__						:    Dict   		=	jnp.asarray(lowerCamelCase )

         else:
            # also add unexpected weight so that warning is thrown
            a__						:    Union[str, Any]   		=	jnp.asarray(lowerCamelCase )
   return unflatten_dict(lowerCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase ):
   a__						:    Any   		=	os.path.abspath(lowerCamelCase )
   logger.info(F"""Loading Flax weights from {flax_checkpoint_path}""" )

   # import correct flax class
   a__						:    int   		=	getattr(lowerCamelCase				,			"Flax" + model.__class__.__name__ )

   # load flax weight dict
   with open(lowerCamelCase				,			"rb" ) as state_f:
      try:
         a__						:    Any   		=	from_bytes(lowerCamelCase				,			state_f.read() )
      except UnpicklingError:
         raise EnvironmentError(F"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """ )

   return load_flax_weights_in_pytorch_model(lowerCamelCase				,			lowerCamelCase )






def    _A	(   lowerCamelCase				,			lowerCamelCase ):

   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
          " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
          " instructions." )
      raise

   # check if we have bf16 weights
   a__						:    int   		=	flatten_dict(jax.tree_util.tree_map(lambda lowerCamelCase : x.dtype == jnp.bfloataa				,			lowerCamelCase ) ).values()
   if any(lowerCamelCase ):
      # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
      # and bf16 is not fully supported in PT yet.
      logger.warning(
          "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
          "before loading those in PyTorch model." )
      a__						:    Optional[Any]   		=	jax.tree_util.tree_map(
          lambda lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params				,			lowerCamelCase )

   a__						:    List[str]   		=	flatten_dict(lowerCamelCase )
   a__						:    Union[str, Any]   		=	pt_model.state_dict()

   a__						:    str   		=	(pt_model.base_model_prefix in flax_state) and (
       pt_model.base_model_prefix not in {k.split("." )[0] for k in pt_model_dict.keys()}
   )
   a__						:    Union[str, Any]   		=	(pt_model.base_model_prefix not in flax_state) and (
       pt_model.base_model_prefix in {k.split("." )[0] for k in pt_model_dict.keys()}
   )

   # keep track of unexpected & missing keys
   a__						:    Tuple   		=	[]
   a__						:    List[Any]   		=	set(pt_model_dict.keys() )

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      a__						:    List[str]   		=	flax_key_tuple[0] == pt_model.base_model_prefix
      a__						:    int   		=	".".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict

      # adapt flax_key to prepare for loading from/to base model only
      if load_model_with_head_into_base_model and has_base_model_prefix:
         a__						:    Union[str, Any]   		=	flax_key_tuple[1:]
      elif load_base_model_into_model_with_head and require_base_model_prefix:
         a__						:    Optional[int]   		=	(pt_model.base_model_prefix,) + flax_key_tuple

      # rename flax weights to PyTorch format
      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(lowerCamelCase ) not in pt_model_dict:
         # conv layer
         a__						:    Optional[int]   		=	flax_key_tuple[:-1] + ("weight",)
         a__						:    Optional[int]   		=	jnp.transpose(lowerCamelCase				,			(3, 2, 0, 1) )
      elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCamelCase ) not in pt_model_dict:
         # linear layer
         a__						:    Any   		=	flax_key_tuple[:-1] + ("weight",)
         a__						:    str   		=	flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
         a__						:    Any   		=	flax_key_tuple[:-1] + ("weight",)

      # adding batch stats from flax batch norm to pt
      elif "mean" in flax_key_tuple[-1]:
         a__						:    str   		=	flax_key_tuple[:-1] + ("running_mean",)
      elif "var" in flax_key_tuple[-1]:
         a__						:    int   		=	flax_key_tuple[:-1] + ("running_var",)

      if "batch_stats" in flax_state:
         a__						:    List[Any]   		=	".".join(flax_key_tuple[1:] )  # Remove the params/batch_stats header
      else:
         a__						:    List[Any]   		=	".".join(lowerCamelCase )

      # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
      a__						:    int   		=	{}
      # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
      for key in pt_model_dict:
         a__						:    Optional[Any]   		=	key.split("." )
         a__						:    Any   		=	None
         if key_components[-3::2] == ["parametrizations", "original0"]:
            a__						:    Tuple   		=	key_components[-2] + "_g"
         elif key_components[-3::2] == ["parametrizations", "original1"]:
            a__						:    Dict   		=	key_components[-2] + "_v"
         if name is not None:
            a__						:    Any   		=	key_components[:-3] + [name]
            a__						:    Union[str, Any]   		=	".".join(lowerCamelCase )
            a__						:    int   		=	key

      if flax_key in special_pt_names:
         a__						:    List[Any]   		=	special_pt_names[flax_key]

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
                F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
         else:
            # add weight to pytorch dict
            a__						:    Dict   		=	np.asarray(lowerCamelCase ) if not isinstance(lowerCamelCase				,			np.ndarray ) else flax_tensor
            a__						:    Optional[int]   		=	torch.from_numpy(lowerCamelCase )
            # remove from missing keys
            missing_keys.remove(lowerCamelCase )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(lowerCamelCase )

   pt_model.load_state_dict(lowerCamelCase )

   # re-transform missing_keys to list
   a__						:    Tuple   		=	list(lowerCamelCase )

   if len(lowerCamelCase ) > 0:
      logger.warning(
          "Some weights of the Flax model were not used when initializing the PyTorch model"
          F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
          F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
          " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
          F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
          " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
          " FlaxBertForSequenceClassification model)." )
   else:
      logger.warning(F"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n""" )
   if len(lowerCamelCase ) > 0:
      logger.warning(
          F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
          F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
          " use it for predictions and inference." )
   else:
      logger.warning(
          F"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
          "If your task is similar to the task the model of the checkpoint was trained on, "
          F"""you can already use {pt_model.__class__.__name__} for predictions without further training.""" )

   return pt_model








from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import (
    BaseOutput,
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_onnx_available,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)







@dataclass
class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Union[List[PIL.Image.Image], np.ndarray]
   _UpperCamelCase			:    Optional[List[bool]]


try:
  if not (is_transformers_available() and is_torch_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
  from .pipeline_cycle_diffusion import CycleDiffusionPipeline
  from .pipeline_stable_diffusion import StableDiffusionPipeline
  from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
  from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
  from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
  from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
  from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
  from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
  from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
  from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
  from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
  from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
  from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
  from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
  from .pipeline_stable_unclip import StableUnCLIPPipeline
  from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
  from .safety_checker import StableDiffusionSafetyChecker
  from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer

try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
  from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      StableDiffusionDepthaImgPipeline,
      StableDiffusionDiffEditPipeline,
      StableDiffusionPixaPixZeroPipeline,
  )
else:
  from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
  from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
  from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline


try:
  if not (
      is_torch_available()
      and is_transformers_available()
      and is_k_diffusion_available()
      and is_k_diffusion_version(""">=""", """0.0.12""")
  ):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
  from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline

try:
  if not (is_transformers_available() and is_onnx_available()):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_onnx_objects import *  # noqa F403
else:
  from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
  from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
  from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
  from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
  from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline

if is_transformers_available() and is_flax_available():
  import flax







  @flax.struct.dataclass
  class 	__lowerCAmelCase      (       _UpperCamelCase	):
     _UpperCamelCase			:    np.ndarray
     _UpperCamelCase			:    List[bool]







  from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
  from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
  from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
  from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
  from .safety_checker_flax import FlaxStableDiffusionSafetyChecker

