




"""simple docstring"""


# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()






class  lowercase(					__a	,      __a	,      __a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = StableDiffusionControlNetImgaImgPipeline
 lowercase__			 = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
 lowercase__			 = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 lowercase__			 = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"control_image"}			)
 lowercase__			 = IMAGE_TO_IMAGE_IMAGE_PARAMS


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   torch.manual_seed(0		)
   _snake_case							:		str								=  UNetaDConditionModel(
       block_out_channels=(32, 64),				layers_per_block=2,				sample_size=32,				in_channels=4,				out_channels=4,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),				cross_attention_dim=32,				)
   torch.manual_seed(0		)
   _snake_case							:		Union[str, Any]								=  ControlNetModel(
       block_out_channels=(32, 64),				layers_per_block=2,				in_channels=4,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),				cross_attention_dim=32,				conditioning_embedding_out_channels=(16, 32),				)
   torch.manual_seed(0		)
   _snake_case							:		Dict								=  DDIMScheduler(
       beta_start=0.00_085,				beta_end=0.012,				beta_schedule="""scaled_linear""",				clip_sample=a_,				set_alpha_to_one=a_,				)
   torch.manual_seed(0		)
   _snake_case							:		Optional[int]								=  AutoencoderKL(
       block_out_channels=[32, 64],				in_channels=3,				out_channels=3,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],				latent_channels=4,				)
   torch.manual_seed(0		)
   _snake_case							:		Union[str, Any]								=  CLIPTextConfig(
       bos_token_id=0,				eos_token_id=2,				hidden_size=32,				intermediate_size=37,				layer_norm_eps=1E-05,				num_attention_heads=4,				num_hidden_layers=5,				pad_token_id=1,				vocab_size=1_000,				)
   _snake_case							:		Tuple								=  CLIPTextModel(a_		)
   _snake_case							:		Any								=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

   _snake_case							:		Any								=  {
       """unet""": unet,
       """controlnet""": controlnet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }
   return components


 def 			UpperCamelCase_       (    self:		str,				a_:		Any,				a_:		Union[str, Any]=0		):




   '''simple docstring'''



   if str(a_		).startswith("""mps"""		):
     _snake_case							:		Union[str, Any]								=  torch.manual_seed(a_		)
   else:
     _snake_case							:		Any								=  torch.Generator(device=a_		).manual_seed(a_		)

   _snake_case							:		Union[str, Any]								=  2
   _snake_case							:		Any								=  randn_tensor(
       (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),				generator=a_,				device=torch.device(a_		),				)
   _snake_case							:		str								=  floats_tensor(control_image.shape,				rng=random.Random(a_		)		).to(a_		)
   _snake_case							:		List[str]								=  image.cpu().permute(0,				2,				3,				1		)[0]
   _snake_case							:		int								=  Image.fromarray(np.uinta(a_		)		).convert("""RGB"""		).resize((64, 64)		)
   _snake_case							:		str								=  {
       """prompt""": """A painting of a squirrel eating a burger""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
       """image""": image,
       """control_image""": control_image,
   }

   return inputs


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)


 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available(),				reason="""XFormers attention is only available with CUDA and `xformers` installed""",				)
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)


 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   self._test_inference_batch_single_identical(expected_max_diff=2E-3		)







class  lowercase(					__a	,      __a	,      unittest.TestCase			):







 '''simple docstring'''





 lowercase__			 = StableDiffusionControlNetImgaImgPipeline
 lowercase__			 = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
 lowercase__			 = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
 lowercase__			 = frozenset([]			)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   torch.manual_seed(0		)
   _snake_case							:		str								=  UNetaDConditionModel(
       block_out_channels=(32, 64),				layers_per_block=2,				sample_size=32,				in_channels=4,				out_channels=4,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),				up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D"""),				cross_attention_dim=32,				)
   torch.manual_seed(0		)

   def init_weights(a_:		Optional[Any]		):
     if isinstance(a_,				torch.nn.Convad		):
       torch.nn.init.normal(m.weight		)
       m.bias.data.fill_(1.0		)

   _snake_case							:		Optional[Any]								=  ControlNetModel(
       block_out_channels=(32, 64),				layers_per_block=2,				in_channels=4,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),				cross_attention_dim=32,				conditioning_embedding_out_channels=(16, 32),				)
   controlneta.controlnet_down_blocks.apply(a_		)

   torch.manual_seed(0		)
   _snake_case							:		Tuple								=  ControlNetModel(
       block_out_channels=(32, 64),				layers_per_block=2,				in_channels=4,				down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D"""),				cross_attention_dim=32,				conditioning_embedding_out_channels=(16, 32),				)
   controlneta.controlnet_down_blocks.apply(a_		)

   torch.manual_seed(0		)
   _snake_case							:		Dict								=  DDIMScheduler(
       beta_start=0.00_085,				beta_end=0.012,				beta_schedule="""scaled_linear""",				clip_sample=a_,				set_alpha_to_one=a_,				)
   torch.manual_seed(0		)
   _snake_case							:		Optional[Any]								=  AutoencoderKL(
       block_out_channels=[32, 64],				in_channels=3,				out_channels=3,				down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],				up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],				latent_channels=4,				)
   torch.manual_seed(0		)
   _snake_case							:		Optional[Any]								=  CLIPTextConfig(
       bos_token_id=0,				eos_token_id=2,				hidden_size=32,				intermediate_size=37,				layer_norm_eps=1E-05,				num_attention_heads=4,				num_hidden_layers=5,				pad_token_id=1,				vocab_size=1_000,				)
   _snake_case							:		Union[str, Any]								=  CLIPTextModel(a_		)
   _snake_case							:		int								=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""		)

   _snake_case							:		Dict								=  MultiControlNetModel([controlneta, controlneta]		)

   _snake_case							:		str								=  {
       """unet""": unet,
       """controlnet""": controlnet,
       """scheduler""": scheduler,
       """vae""": vae,
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """safety_checker""": None,
       """feature_extractor""": None,
   }
   return components


 def 			UpperCamelCase_       (    self:		Union[str, Any],				a_:		Union[str, Any],				a_:		Tuple=0		):




   '''simple docstring'''



   if str(a_		).startswith("""mps"""		):
     _snake_case							:		Any								=  torch.manual_seed(a_		)
   else:
     _snake_case							:		List[Any]								=  torch.Generator(device=a_		).manual_seed(a_		)

   _snake_case							:		Union[str, Any]								=  2

   _snake_case							:		List[str]								=  [
       randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),				generator=a_,				device=torch.device(a_		),				),
       randn_tensor(
           (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor),				generator=a_,				device=torch.device(a_		),				),
   ]

   _snake_case							:		List[str]								=  floats_tensor(control_image[0].shape,				rng=random.Random(a_		)		).to(a_		)
   _snake_case							:		int								=  image.cpu().permute(0,				2,				3,				1		)[0]
   _snake_case							:		int								=  Image.fromarray(np.uinta(a_		)		).convert("""RGB"""		).resize((64, 64)		)
   _snake_case							:		Tuple								=  {
       """prompt""": """A painting of a squirrel eating a burger""",
       """generator""": generator,
       """num_inference_steps""": 2,
       """guidance_scale""": 6.0,
       """output_type""": """numpy""",
       """image""": image,
       """control_image""": control_image,
   }

   return inputs


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Any								=  self.get_dummy_components()
   _snake_case							:		Union[str, Any]								=  self.pipeline_class(**a_		)
   pipe.to(a_		)

   _snake_case							:		int								=  10.0
   _snake_case							:		List[str]								=  4

   _snake_case							:		Optional[Any]								=  self.get_dummy_inputs(a_		)
   _snake_case							:		str								=  steps
   _snake_case							:		Union[str, Any]								=  scale
   _snake_case							:		List[Any]								=  pipe(**a_		)[0]

   _snake_case							:		Tuple								=  self.get_dummy_inputs(a_		)
   _snake_case							:		Tuple								=  steps
   _snake_case							:		Tuple								=  scale
   _snake_case							:		str								=  pipe(**a_,				control_guidance_start=0.1,				control_guidance_end=0.2		)[0]

   _snake_case							:		int								=  self.get_dummy_inputs(a_		)
   _snake_case							:		Any								=  steps
   _snake_case							:		Tuple								=  scale
   _snake_case							:		str								=  pipe(**a_,				control_guidance_start=[0.1, 0.3],				control_guidance_end=[0.2, 0.7]		)[0]

   _snake_case							:		Any								=  self.get_dummy_inputs(a_		)
   _snake_case							:		Union[str, Any]								=  steps
   _snake_case							:		Dict								=  scale
   _snake_case							:		Tuple								=  pipe(**a_,				control_guidance_start=0.4,				control_guidance_end=[0.5, 0.8]		)[0]

   # make sure that all outputs are different
   assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
   assert np.sum(np.abs(output_a - output_a		)		) > 1E-3
   assert np.sum(np.abs(output_a - output_a		)		) > 1E-3


 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3		)


 @unittest.skipIf(
     torch_device != """cuda""" or not is_xformers_available(),				reason="""XFormers attention is only available with CUDA and `xformers` installed""",				)
 def 			UpperCamelCase_       (    self:		Tuple		):




   '''simple docstring'''



   self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3		)


 def 			UpperCamelCase_       (    self:		Optional[int]		):




   '''simple docstring'''



   self._test_inference_batch_single_identical(expected_max_diff=2E-3		)


 def 			UpperCamelCase_       (    self:		List[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[Any]								=  self.get_dummy_components()
   _snake_case							:		List[Any]								=  self.pipeline_class(**a_		)
   pipe.to(a_		)
   pipe.set_progress_bar_config(disable=a_		)
   with tempfile.TemporaryDirectory() as tmpdir:
     try:
       # save_pretrained is not implemented for Multi-ControlNet
       pipe.save_pretrained(a_		)
     except NotImplementedError:
       pass







@slow
@require_torch_gpu
class  lowercase(					unittest.TestCase			):







 '''simple docstring'''





 def 			UpperCamelCase_       (    self:		Union[str, Any]		):




   '''simple docstring'''



   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()


 def 			UpperCamelCase_       (    self:		Optional[Any]		):




   '''simple docstring'''



   _snake_case							:		Optional[int]								=  ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""		)

   _snake_case							:		List[str]								=  StableDiffusionControlNetImgaImgPipeline.from_pretrained(
       """runwayml/stable-diffusion-v1-5""",				safety_checker=a_,				controlnet=a_		)
   pipe.enable_model_cpu_offload()
   pipe.set_progress_bar_config(disable=a_		)

   _snake_case							:		List[str]								=  torch.Generator(device="""cpu"""		).manual_seed(0		)
   _snake_case							:		Dict								=  """evil space-punk bird"""
   _snake_case							:		Any								=  load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""		).resize((512, 512)		)
   _snake_case							:		Optional[int]								=  load_image(
       """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""		).resize((512, 512)		)

   _snake_case							:		List[str]								=  pipe(
       a_,				a_,				control_image=a_,				generator=a_,				output_type="""np""",				num_inference_steps=50,				strength=0.6,				)

   _snake_case							:		Dict								=  output.images[0]

   assert image.shape == (512, 512, 3)

   _snake_case							:		Optional[int]								=  load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"""		)

   assert np.abs(expected_image - image		).max() < 9E-2








"""simple docstring"""


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


A_      =    logging.get_logger(__name__)


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Dict		,       snake_case__							: Union[str, Any]		,       snake_case__							: int=False   ):
  """simple docstring"""

  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:
    _snake_case							:		Dict								=  os.path.abspath(snake_case__   )
    logger.info(F"Loading PyTorch weights from {pt_path}"   )

    _snake_case							:		Tuple								=  torch.load(snake_case__		,       map_location="""cpu"""   )
    logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()   ):,} parameters."   )

    _snake_case							:		int								=  convert_pytorch_state_dict_to_flax(snake_case__		,       snake_case__   )
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    _snake_case							:		Dict								=  convert_pytorch_sharded_state_dict_to_flax(snake_case__		,       snake_case__   )
  return flax_state_dict


def  UpperCAmelCase__	(snake_case__							: Tuple[str]		,       snake_case__							: np.ndarray		,       snake_case__							: Dict[str, jnp.ndarray]		,       snake_case__							: str		,       ):
  """simple docstring"""

  def is_key_or_prefix_key_in_dict(snake_case__							: Tuple[str]   ) -> bool:
    return len(set(snake_case__   ) & {key, (model_prefix,) + key}   ) > 0

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

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

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

  # embedding
  _snake_case							:		Any								=  pt_tuple_key[:-1] + ("""embedding""",)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case__   ):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  _snake_case							:		Optional[int]								=  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(snake_case__   ):
    _snake_case							:		Dict								=  pt_tensor.transpose(2		,       3		,       1		,       0   )
    return renamed_pt_tuple_key, pt_tensor

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

  # old PyTorch layer norm weight
  _snake_case							:		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
  _snake_case							:		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
  _snake_case							:		Optional[Any]								=  None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[-2] + """_g"""
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    _snake_case							:		Dict								=  pt_tuple_key[-2] + """_v"""
  if name is not None:
    _snake_case							:		Union[str, Any]								=  pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor


def  UpperCAmelCase__	(snake_case__							: Union[str, Any]		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  _snake_case							:		Tuple								=  {k: v.numpy() for k, v in pt_state_dict.items()}

  _snake_case							:		int								=  flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    _snake_case							:		Dict								=  flax_model.params["""params"""]
  else:
    _snake_case							:		List[Any]								=  flax_model.params
  _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    _snake_case							:		Union[str, Any]								=  flatten_dict(flax_model.params["""batch_stats"""]   )
    random_flax_state_dict.update(snake_case__   )

  _snake_case							:		Tuple								=  {}

  _snake_case							:		Dict								=  (model_prefix not in flax_model_params) and (
      model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
  )
  _snake_case							:		Optional[int]								=  (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():
    _snake_case							:		int								=  tuple(pt_key.split("""."""   )   )

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

    # Correctly rename weight parameters
    _snake_case		, _snake_case							:		int								=  rename_key_and_reshape_tensor(
        snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )

    # add model prefix if necessary
    _snake_case							:		Dict								=  (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		int								=  (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]:
        _snake_case							:		Union[str, Any]								=  jnp.asarray(snake_case__   )
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(snake_case__		,       snake_case__   )
        continue

      # also add unexpected weight so that warning is thrown
      _snake_case							:		List[Any]								=  jnp.asarray(snake_case__   )

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

  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: Optional[int]		,       snake_case__							: Dict   ):
  """simple docstring"""

  import torch

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

    _snake_case							:		List[str]								=  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:
      _snake_case							:		str								=  flax_model.params["""params"""]

      _snake_case							:		List[Any]								=  flatten_dict(snake_case__   )
      random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]   )   )
    else:
      _snake_case							:		List[Any]								=  flax_model.params
      _snake_case							:		Tuple								=  flatten_dict(snake_case__   )

    _snake_case							:		Tuple								=  (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("""."""   )[0] for k in pt_state_dict.keys()}
    )
    _snake_case							:		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():
      _snake_case							:		List[str]								=  tuple(pt_key.split("""."""   )   )

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

      # Correctly rename weight parameters
      _snake_case		, _snake_case							:		Optional[Any]								=  rename_key_and_reshape_tensor(
          snake_case__		,       snake_case__		,       snake_case__		,       snake_case__   )
      # add model prefix if necessary
      _snake_case							:		List[str]								=  (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        _snake_case							:		Any								=  (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]:
          _snake_case							:		Optional[int]								=  jnp.asarray(snake_case__   )
          continue
        if "var" in flax_key[-1]:
          _snake_case							:		Any								=  jnp.asarray(snake_case__   )
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(snake_case__		,       snake_case__   )
          continue

        # also add unexpected weight so that warning is thrown
        _snake_case							:		List[str]								=  jnp.asarray(snake_case__   )

      else:
        # also add unexpected weight so that warning is thrown
        _snake_case							:		Optional[Any]								=  jnp.asarray(snake_case__   )
  return unflatten_dict(snake_case__   )


def  UpperCAmelCase__	(snake_case__							: List[str]		,       snake_case__							: Optional[Any]   ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  os.path.abspath(snake_case__   )
  logger.info(F"Loading Flax weights from {flax_checkpoint_path}"   )

  # import correct flax class
  _snake_case							:		Union[str, Any]								=  getattr(snake_case__		,       """Flax""" + model.__class__.__name__   )

  # load flax weight dict
  with open(snake_case__		,       """rb"""   ) as state_f:
    try:
      _snake_case							:		Dict								=  from_bytes(snake_case__		,       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(snake_case__		,       snake_case__   )






def  UpperCAmelCase__	(snake_case__							: Tuple		,       snake_case__							: Optional[int]   ):
  """simple docstring"""

  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
  _snake_case							:		Optional[int]								=  flatten_dict(jax.tree_util.tree_map(lambda snake_case__   : x.dtype == jnp.bfloataa		,       snake_case__   )   ).values()
  if any(snake_case__   ):
    # 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."""   )
    _snake_case							:		Optional[int]								=  jax.tree_util.tree_map(
        lambda snake_case__   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params		,       snake_case__   )

  _snake_case							:		Dict								=  flatten_dict(snake_case__   )
  _snake_case							:		Optional[Any]								=  pt_model.state_dict()

  _snake_case							:		Union[str, Any]								=  (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()}
  )
  _snake_case							:		Optional[int]								=  (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
  _snake_case							:		str								=  []
  _snake_case							:		Tuple								=  set(pt_model_dict.keys()   )

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    _snake_case							:		Tuple								=  flax_key_tuple[0] == pt_model.base_model_prefix
    _snake_case							:		Optional[Any]								=  """.""".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:
      _snake_case							:		List[str]								=  flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      _snake_case							:		Union[str, Any]								=  (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(snake_case__   ) not in pt_model_dict:
      # conv layer
      _snake_case							:		Union[str, Any]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Optional[int]								=  jnp.transpose(snake_case__		,       (3, 2, 0, 1)   )
    elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__   ) not in pt_model_dict:
      # linear layer
      _snake_case							:		Optional[int]								=  flax_key_tuple[:-1] + ("""weight""",)
      _snake_case							:		Union[str, Any]								=  flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      _snake_case							:		int								=  flax_key_tuple[:-1] + ("""weight""",)

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

    if "batch_stats" in flax_state:
      _snake_case							:		int								=  """.""".join(flax_key_tuple[1:]   )  # Remove the params/batch_stats header
    else:
      _snake_case							:		int								=  """.""".join(snake_case__   )

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

    if flax_key in special_pt_names:
      _snake_case							:		Union[str, 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
        _snake_case							:		List[str]								=  np.asarray(snake_case__   ) if not isinstance(snake_case__		,       np.ndarray   ) else flax_tensor
        _snake_case							:		List[Any]								=  torch.from_numpy(snake_case__   )
        # remove from missing keys
        missing_keys.remove(snake_case__   )
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(snake_case__   )

  pt_model.load_state_dict(snake_case__   )

  # re-transform missing_keys to list
  _snake_case							:		List[str]								=  list(snake_case__   )

  if len(snake_case__   ) > 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(snake_case__   ) > 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


