


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      :str			    =       {
    """/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     (						_lowercase :     int  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      list(s_dict.keys()  )
     for key in keys:
          __UpperCAmelCase     :			int  							=      R'''.*/layers_(\d+)'''
          __UpperCAmelCase     :			Dict  							=      key
          if re.match(_lowercase      , _lowercase  ):
               __UpperCAmelCase     :			List[Any]  							=      re.sub(R'''layers_(\d+)'''      , R'''block/\1/layer'''      , _lowercase  )

          __UpperCAmelCase     :			int  							=      R'''(encoder|decoder)\/'''

          if re.match(_lowercase      , _lowercase  ):
               __UpperCAmelCase     :			str  							=      re.match(_lowercase      , _lowercase  ).groups()
               if groups[0] == "encoder":
                    __UpperCAmelCase     :			Union[str, Any]  							=      re.sub(R'''/mlp/'''      , R'''/1/mlp/'''      , _lowercase  )
                    __UpperCAmelCase     :			List[Any]  							=      re.sub(R'''/pre_mlp_layer_norm/'''      , R'''/1/layer_norm/'''      , _lowercase  )

               elif groups[0] == "decoder":
                    __UpperCAmelCase     :			Tuple  							=      re.sub(R'''/mlp/'''      , R'''/2/mlp/'''      , _lowercase  )
                    __UpperCAmelCase     :			str  							=      re.sub(R'''/pre_mlp_layer_norm/'''      , R'''/2/layer_norm/'''      , _lowercase  )

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

          print(F'{key} -> {new_key}'  )
          __UpperCAmelCase     :			Optional[int]  							=      s_dict.pop(_lowercase  )

     if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
          __UpperCAmelCase     :			List[str]  							=      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:
          __UpperCAmelCase     :			Dict  							=      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:
               __UpperCAmelCase     :			Optional[Any]  							=      s_dict[key].shape[0]
               __UpperCAmelCase     :			List[Any]  							=      s_dict[key]
               for idx in range(_lowercase  ):
                    __UpperCAmelCase     :			str  							=      expert_weihts[idx]
                    print(F'{key} -> {key.replace("expert/"      , "nested fstring"  )}'  )

               s_dict.pop(_lowercase  )

     return s_dict


__UpperCAmelCase      :Optional[Any]			    =       {
    """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     (						_lowercase :     Optional[int]      , _lowercase :     List[Any]  ):





     '''simple docstring'''






     import regex as re

     with open(_lowercase      , '''r'''  ) as f:
          __UpperCAmelCase     :			List[str]  							=      f.read()

     __UpperCAmelCase     :			Union[str, Any]  							=      re.findall(R'''(.*) = ([0-9.]*)'''      , _lowercase  )
     __UpperCAmelCase     :			Union[str, Any]  							=      {}
     for param, value in regex_match:
          if param in GIN_TO_CONFIG_MAPPING and value != "":
               __UpperCAmelCase     :			Optional[int]  							=      float(_lowercase  ) if '''.''' in value else int(_lowercase  )

     __UpperCAmelCase     :			str  							=      re.findall(R'''(.*activations) = \(\'(.*)\',\)'''      , _lowercase  )[0]
     __UpperCAmelCase     :			List[str]  							=      str(activation[1]  )

     __UpperCAmelCase     :			Dict  							=      num_experts
     __UpperCAmelCase     :			Union[str, Any]  							=      SwitchTransformersConfig(**_lowercase  )
     return config




def      _a     (						_lowercase :     Tuple      , _lowercase :     List[str]      , _lowercase :     Any=None      , _lowercase :     List[str]="./"      , _lowercase :     str=8  ):





     '''simple docstring'''






     print(F'Loading flax weights from : {flax_checkpoint_path}'  )
     __UpperCAmelCase     :			Tuple  							=      checkpoints.load_tax_checkpoint(_lowercase  )

     if gin_file is not None:
          __UpperCAmelCase     :			str  							=      convert_gin_to_config(_lowercase      , _lowercase  )
     else:
          __UpperCAmelCase     :			Any  							=      SwitchTransformersConfig.from_pretrained(_lowercase  )

     __UpperCAmelCase     :			Optional[int]  							=      SwitchTransformersForConditionalGeneration(_lowercase  )

     __UpperCAmelCase     :			Optional[int]  							=      flax_params['''target''']
     __UpperCAmelCase     :			str  							=      flatten_dict(_lowercase      , sep='''/'''  )
     __UpperCAmelCase     :			Optional[int]  							=      rename_keys(_lowercase  )
     __UpperCAmelCase     :			Union[str, Any]  							=      unflatten_dict(_lowercase      , sep='''/'''  )

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

     print(F'Save PyTorch model to {pytorch_dump_path}'  )
     pt_model.save_pretrained(_lowercase  )


if __name__ == "__main__":
      __UpperCAmelCase      :Union[str, Any]			    =       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      :str			    =       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,
      )



'''simple docstring'''



from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch



@dataclass
class      a    :


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			torch.Tensor  # [batch_size x 3]
     SCREAMING_SNAKE_CASE	:			int
     SCREAMING_SNAKE_CASE	:			int
     SCREAMING_SNAKE_CASE	:			float
     SCREAMING_SNAKE_CASE	:			float
     SCREAMING_SNAKE_CASE	:			Tuple[int]

     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
          assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
          assert len(self.x.shape      ) == len(self.y.shape      ) == len(self.z.shape      ) == len(self.origin.shape      ) == 2

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          return torch.from_numpy(np.array([self.width, self.height]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							Optional[Any]:
          return torch.from_numpy(np.array([self.x_fov, self.y_fov]							,			dtype=np.floataa      )      )

     def 			lowerCamelCase__			(    self					:		Any      )						->							torch.Tensor:
          __UpperCAmelCase     :			Dict  							=      torch.arange(self.height * self.width      )
          __UpperCAmelCase     :			Dict  							=      torch.stack(
              [
                  pixel_indices % self.width,
                  torch.div(snake_case							,			self.width							,			rounding_mode='''trunc'''      ),
              ]							,			axis=1							,			)
          return coords

     @property
     def 			lowerCamelCase__			(    self					:		Any      )						->							int:
          __UpperCAmelCase							,       *__UpperCAmelCase     :			str  							=      self.shape
          __UpperCAmelCase     :			Dict  							=      int(np.prod(snake_case      )      )

          __UpperCAmelCase     :			Tuple  							=      self.get_image_coords()
          __UpperCAmelCase     :			List[Any]  							=      torch.broadcast_to(coords.unsqueeze(0      )							,			[batch_size * inner_batch_size, *coords.shape]      )
          __UpperCAmelCase     :			Any  							=      self.get_camera_rays(snake_case      )

          __UpperCAmelCase     :			List[str]  							=      rays.view(snake_case							,			inner_batch_size * self.height * self.width							,			2							,			3      )

          return rays

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		torch.Tensor      )						->							torch.Tensor:
          __UpperCAmelCase							,       *__UpperCAmelCase							,       __UpperCAmelCase     :			List[str]  							=      coords.shape
          assert n_coords == 2
          assert batch_size == self.origin.shape[0]

          __UpperCAmelCase     :			List[str]  							=      coords.view(snake_case							,			-1							,			2      )

          __UpperCAmelCase     :			Optional[Any]  							=      self.resolution()
          __UpperCAmelCase     :			Tuple  							=      self.fov()

          __UpperCAmelCase     :			Optional[int]  							=      (flat.float() / (res - 1)) * 2 - 1
          __UpperCAmelCase     :			Union[str, Any]  							=      fracs * torch.tan(fov / 2      )

          __UpperCAmelCase     :			str  							=      fracs.view(snake_case							,			-1							,			2      )
          __UpperCAmelCase     :			Any  							=      (
              self.z.view(snake_case							,			1							,			3      )
              + self.x.view(snake_case							,			1							,			3      ) * fracs[:, :, :1]
              + self.y.view(snake_case							,			1							,			3      ) * fracs[:, :, 1:]
          )
          __UpperCAmelCase     :			Union[str, Any]  							=      directions / directions.norm(dim=-1							,			keepdim=snake_case      )
          __UpperCAmelCase     :			Union[str, Any]  							=      torch.stack(
              [
                  torch.broadcast_to(self.origin.view(snake_case							,			1							,			3      )							,			[batch_size, directions.shape[1], 3]      ),
                  directions,
              ]							,			dim=2							,			)
          return rays.view(snake_case							,			*snake_case							,			2							,			3      )






     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int							,			snake_case					:		int      )						->							"DifferentiableProjectiveCamera":
          assert width * self.height == height * self.width, "The aspect ratio should not change."
          return DifferentiableProjectiveCamera(
              origin=self.origin							,			x=self.x							,			y=self.y							,			z=self.z							,			width=snake_case							,			height=snake_case							,			x_fov=self.x_fov							,			y_fov=self.y_fov							,			)




def      _a     (						_lowercase :     int  ):





     '''simple docstring'''






     __UpperCAmelCase     :			str  							=      []
     __UpperCAmelCase     :			Optional[int]  							=      []
     __UpperCAmelCase     :			Union[str, Any]  							=      []
     __UpperCAmelCase     :			List[Any]  							=      []
     for theta in np.linspace(0      , 2 * np.pi      , num=20  ):
          __UpperCAmelCase     :			Dict  							=      np.array([np.sin(_lowercase  ), np.cos(_lowercase  ), -0.5]  )
          z /= np.sqrt(np.sum(z**2  )  )
          __UpperCAmelCase     :			Any  							=      -z * 4
          __UpperCAmelCase     :			Dict  							=      np.array([np.cos(_lowercase  ), -np.sin(_lowercase  ), 0.0]  )
          __UpperCAmelCase     :			List[str]  							=      np.cross(_lowercase      , _lowercase  )
          origins.append(_lowercase  )
          xs.append(_lowercase  )
          ys.append(_lowercase  )
          zs.append(_lowercase  )
     return DifferentiableProjectiveCamera(
         origin=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , x=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , y=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , z=torch.from_numpy(np.stack(_lowercase      , axis=0  )  ).float()      , width=_lowercase      , height=_lowercase      , x_fov=0.7      , y_fov=0.7      , shape=(1, len(_lowercase  ))      , )