

"""simple docstring"""


import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


UpperCAmelCase													=logging.get_logger(__name__)  # pylint: disable=invalid-name



def   _A					(	_a	:						Union[List, PIL.Image.Image, torch.Tensor]				):



      """simple docstring"""






      warnings.warn(
          """The preprocess method is deprecated and will be removed in a future version. Please"""
          """ use VaeImageProcessor.preprocess instead"""     ,   _a     ,   )
      if isinstance(_a     ,   torch.Tensor				):
            return image
      elif isinstance(_a     ,   PIL.Image.Image				):
            A				  =		[image]

      if isinstance(image[0]     ,   PIL.Image.Image				):
            A  ,			A				  =		image[0].size
            A  ,			A				  =		(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

            A				  =		[np.array(i.resize((w, h)     ,   resample=PIL_INTERPOLATION["""lanczos"""]				)				)[None, :] for i in image]
            A				  =		np.concatenate(_a     ,   axis=0				)
            A				  =		np.array(_a				).astype(np.floataa				) / 2_55.0
            A				  =		image.transpose(0     ,   3     ,   1     ,   2				)
            A				  =		2.0 * image - 1.0
            A				  =		torch.from_numpy(_a				)
      elif isinstance(image[0]     ,   torch.Tensor				):
            A				  =		torch.cat(_a     ,   dim=0				)
      return image



def   _A					(	_a	:						Union[List, PIL.Image.Image, torch.Tensor]				):



      """simple docstring"""






      if isinstance(_a     ,   torch.Tensor				):
            return mask
      elif isinstance(_a     ,   PIL.Image.Image				):
            A				  =		[mask]

      if isinstance(mask[0]     ,   PIL.Image.Image				):
            A  ,			A				  =		mask[0].size
            A  ,			A				  =		(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
            A				  =		[np.array(m.convert("""L"""				).resize((w, h)     ,   resample=PIL_INTERPOLATION["""nearest"""]				)				)[None, :] for m in mask]
            A				  =		np.concatenate(_a     ,   axis=0				)
            A				  =		mask.astype(np.floataa				) / 2_55.0
            A				  =		0
            A				  =		1
            A				  =		torch.from_numpy(_a				)
      elif isinstance(mask[0]     ,   torch.Tensor				):
            A				  =		torch.cat(_a     ,   dim=0				)
      return mask





class lowerCamelCase__   (				SCREAMING_SNAKE_CASE    ):



    '''simple docstring'''




    _lowerCamelCase					=   42
    _lowerCamelCase					=   42




    def __init__( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Dict:
          super().__init__()
          self.register_modules(unet=lowerCamelCase_ ,scheduler=lowerCamelCase_    )




    @torch.no_grad()
    def __call__( self ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ = 2_5_0 ,lowerCamelCase_ = 0.0 ,lowerCamelCase_ = 1_0 ,lowerCamelCase_ = 1_0 ,lowerCamelCase_ = None ,lowerCamelCase_ = "pil" ,lowerCamelCase_ = True ,)    ->      Union[ImagePipelineOutput, Tuple]:
          A				  =		image

          A				  =		_preprocess_image(lowerCamelCase_    )
          A				  =		original_image.to(device=self.device ,dtype=self.unet.dtype    )
          A				  =		_preprocess_mask(lowerCamelCase_    )
          A				  =		mask_image.to(device=self.device ,dtype=self.unet.dtype    )

          A				  =		original_image.shape[0]

          # sample gaussian noise to begin the loop
          if isinstance(lowerCamelCase_ ,lowerCamelCase_    ) and len(lowerCamelCase_    ) != batch_size:
                raise ValueError(
                    f'You have passed a list of generators of length {len(lowerCamelCase_    )}, but requested an effective batch'
                    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'    )

          A				  =		original_image.shape
          A				  =		randn_tensor(lowerCamelCase_ ,generator=lowerCamelCase_ ,device=self.device ,dtype=self.unet.dtype    )

          # set step values
          self.scheduler.set_timesteps(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,self.device    )
          A				  =		eta

          A				  =		self.scheduler.timesteps[0] + 1
          A				  =		generator[0] if isinstance(lowerCamelCase_ ,lowerCamelCase_    ) else generator
          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps    )    ):
                if t < t_last:
                      # predict the noise residual
                      A				  =		self.unet(lowerCamelCase_ ,lowerCamelCase_    ).sample
                      # compute previous image: x_t -> x_t-1
                      A				  =		self.scheduler.step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    ).prev_sample

                else:
                      # compute the reverse: x_t-1 -> x_t
                      A				  =		self.scheduler.undo_step(lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_    )
                A				  =		t

          A				  =		(image / 2 + 0.5).clamp(0 ,1    )
          A				  =		image.cpu().permute(0 ,2 ,3 ,1    ).numpy()
          if output_type == "pil":
                A				  =		self.numpy_to_pil(lowerCamelCase_    )

          if not return_dict:
                return (image,)

          return ImagePipelineOutput(images=lowerCamelCase_    )







"""simple docstring"""


import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
UpperCAmelCase													={
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}



def   _A					(	_a	:						str				):



      """simple docstring"""






      A				  =		list(s_dict.keys()				)
      for key in keys:
            A				  =		r""".*/layers_(\d+)"""
            A				  =		key
            if re.match(_a     ,   _a				):
                  A				  =		re.sub(r"""layers_(\d+)"""     ,   r"""block/\1/layer"""     ,   _a				)

            A				  =		r"""(encoder|decoder)\/"""

            if re.match(_a     ,   _a				):
                  A				  =		re.match(_a     ,   _a				).groups()
                  if groups[0] == "encoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/1/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/1/layer_norm/"""     ,   _a				)

                  elif groups[0] == "decoder":
                        A				  =		re.sub(r"""/mlp/"""     ,   r"""/2/mlp/"""     ,   _a				)
                        A				  =		re.sub(r"""/pre_mlp_layer_norm/"""     ,   r"""/2/layer_norm/"""     ,   _a				)

        # 2. Convert other classic mappings
            for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
                  if old_key in new_key:
                        A				  =		new_key.replace(_a     ,   _a				)

            print(f'{key} -> {new_key}'				)
            A				  =		s_dict.pop(_a				)

      if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T
      if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
            A				  =		s_dict[
                """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
            ].T

      # 3. Take extra care of the EXPERTS layer
      for key in list(s_dict.keys()				):
            if "expert" in key:
                  A				  =		s_dict[key].shape[0]
                  A				  =		s_dict[key]
                  for idx in range(_a				):
                        A				  =		expert_weihts[idx]
                        print(f'{key} -> {key.replace("expert/"     ,   "nested fstring"				)}'				)

                  s_dict.pop(_a				)

      return s_dict


UpperCAmelCase													={
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}



def   _A					(	_a	:						Dict     ,   _a	:						int				):



      """simple docstring"""






      import regex as re

      with open(_a     ,   """r"""				) as f:
            A				  =		f.read()

      A				  =		re.findall(r"""(.*) = ([0-9.]*)"""     ,   _a				)
      A				  =		{}
      for param, value in regex_match:
            if param in GIN_TO_CONFIG_MAPPING and value != "":
                  A				  =		float(_a				) if """.""" in value else int(_a				)

      A				  =		re.findall(r"""(.*activations) = \(\'(.*)\',\)"""     ,   _a				)[0]
      A				  =		str(activation[1]				)

      A				  =		num_experts
      A				  =		SwitchTransformersConfig(**_a				)
      return config



def   _A					(	_a	:						Union[str, Any]     ,   _a	:						Dict     ,   _a	:						Optional[Any]=None     ,   _a	:						Dict="./"     ,   _a	:						Dict=8				):



      """simple docstring"""






      print(f'Loading flax weights from : {flax_checkpoint_path}'				)
      A				  =		checkpoints.load_tax_checkpoint(_a				)

      if gin_file is not None:
            A				  =		convert_gin_to_config(_a     ,   _a				)
      else:
            A				  =		SwitchTransformersConfig.from_pretrained(_a				)

      A				  =		SwitchTransformersForConditionalGeneration(_a				)

      A				  =		flax_params["""target"""]
      A				  =		flatten_dict(_a     ,   sep="""/"""				)
      A				  =		rename_keys(_a				)
      A				  =		unflatten_dict(_a     ,   sep="""/"""				)

      # Load the flax params in the PT model
      load_flax_weights_in_pytorch_model(_a     ,   _a				)

      print(f'Save PyTorch model to {pytorch_dump_path}'				)
      pt_model.save_pretrained(_a				)


if __name__ == "__main__":
  UpperCAmelCase													=argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--switch_t5x_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help=(
          "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
          " model architecture. If not provided, a `gin_file` has to be provided."
      ),
  )
  parser.add_argument(
      "--gin_file",
      default=None,
      type=str,
      required=False,
      help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
  )
  parser.add_argument(
      "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
  )
  parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
  UpperCAmelCase													=parser.parse_args()
  convert_flax_checkpoint_to_pytorch(
      args.switch_tax_checkpoint_path,
      args.config_name,
      args.gin_file,
      args.pytorch_dump_folder_path,
      args.num_experts,
  )




